3403 results about "Imaging technology" patented technology

The invention provides a soybean planting fertilization management system, and relates to the technical field of management systems.Soil physicochemical properties, a meteorological microenvironment, a plant growth state and agricultural machinery operation data are collected in real time through a field sensor network and an unmanned aerial vehicle multispectral imaging technology, and a farmland dynamic portrait is constructed; the intelligent decision-making unit uses a deep learning model and a GLCM texture analysis technology to dynamically identify plant nutrient deficiency pathological characteristics, determines a nutrient deficiency type in combination with an element concentration threshold and triggers early warning; the scheme decision module dynamically optimizes the nitrogen-phosphorus-potassium ratio through a reinforcement learning algorithm, generates a field-level fertilization prescription map by using Kriging interpolation, recommends the optimal fertilization opportunity and dosage, and the precise execution module is linked with a variable fertilizer applicator to realize integrated precise application of water and fertilizer. The user interaction module supports remote monitoring and intervention of farmers through a GIS visual interface and a mobile terminal APP, can dynamically adapt to environmental changes and crop requirements, and significantly improves the nutrient utilization efficiency.

The invention relates to the technical field of image recognition, in particular to a license plate recognition system and method based on an image technology and a medium. The method comprises the following steps: acquiring area sensing data and a camera image set, and performing deformation effect compensation to obtain an environment compensation image set; performing image diffusion reverse enhancement on the environment compensation image set to obtain a license plate area enhanced image set; performing character region high-dimensional topological mapping based on the license plate region enhanced image set to obtain a character segmentation matrix; extracting character morphological characteristics according to the character segmentation matrix, and performing character recognition on the character morphological characteristics to obtain a character recognition result; and carrying out cross-character semantic compensation on the character recognition result to obtain a semantic compensation license plate character vector, and carrying out multi-target cross verification on the semantic compensation license plate character vector to obtain a license plate recognition result. According to the invention, the accuracy and robustness of license plate recognition can be improved.

The present disclosure provides methods and assay systems for use in spatially encoded biological assays, including assays to determine a spatial pattern of abundance, expression, and / or activity of one or more biological targets across multiple sites in a sample. In particular, the biological targets comprise proteins, and the methods and assay systems do not depend on imaging techniques for the spatial information of the targets. The present disclosure provides methods and assay systems capable of high levels of multiplexing where reagents are provided to a biological sample in order to address tag the sites to which reagents are delivered; instrumentation capable of controlled delivery of reagents; and a decoding scheme providing a readout that is digital in nature.

The invention discloses an intelligent rock weathering detection method based on a multispectral imaging technology, and particularly relates to the field of rock detection.The method comprises the steps that a multispectral imaging device is used for collecting a rock sample image, and then radiometric calibration, geometric correction and image enhancement processing are sequentially conducted on the image. The method comprises the following steps of: preprocessing an image, acquiring spectrum, texture and morphological characteristic parameters from the preprocessed image, collecting samples through a multi-source database, constructing a deep convolutional neural network model, inputting the acquired characteristic parameters into the model to obtain a rock weathering probability vector, judging the weathering degree according to a maximum probability criterion, and outputting a result to a user side. According to the method, multispectral imaging and deep learning technologies are fused, the defects of a traditional detection method are overcome, the rock weathering degree can be detected more accurately and efficiently, nondestructive detection and real-time monitoring are achieved, and the method has important application value in the fields of geological research, engineering construction and the like.

The invention relates to the technical field of three-dimensional imaging, and discloses a high-precision underwater structured light three-dimensional point cloud imaging method and system, and the method comprises the following steps: deploying a plurality of sensor nodes, monitoring the temperature, salinity and turbidity key parameters of a water body in real time, selecting proper light source wavelength and power according to the collected data, and carrying out the three-dimensional point cloud imaging of the underwater structured light. It is ensured that the optimal signal-to-noise ratio is obtained under the current condition, and meanwhile the exposure time and gain setting of the camera are initialized. According to the method, the optimal signal-to-noise ratio is obtained by monitoring water body conditions in real time and dynamically adjusting light source characteristics, the optimal structured light pattern is predicted by using a machine learning model, scattering noise is removed, advanced registration and fusion algorithms and an accurate phase unwrapping method are applied, and a high-quality comprehensive structured light pattern is generated. Point cloud gaps are filled through a geometric correction algorithm and an interpolation / filtering algorithm, so that a reconstruction model is closer to the surface of a real object.

The invention discloses an automatic identification method and system for a wafer internal defect image of a 3D stacked chip, and belongs to the technical field of semiconductor manufacturing and detection. According to the method, optical, X-ray and ultrasonic image data are synchronously acquired based on a multi-modal imaging technology, imaging parameters are dynamically adjusted to adapt to different wafer levels and material characteristics, multi-modal features are extracted in combination with layered filtering and denoising, multi-resolution registration and a self-supervised deep learning method, and micron-sized defects are positioned by using an attention mechanism. And further constructing a defect-process parameter correlation model through reinforcement learning, generating a closed-loop process optimization instruction, and transmitting the closed-loop process optimization instruction to an execution system. The system comprises a multi-modal imaging module, a noise suppression module, a deep learning analysis module and a process optimization module, and supports edge computing deployment. According to the invention, the internal defect detection efficiency and precision of the multi-layer stacked chip are significantly improved, real-time closed-loop control of detection-analysis is realized, the wafer manufacturing quality risk is reduced, and the method is suitable for intelligent defect detection of an advanced packaging production line.

The invention discloses an intelligent detection method for product defects on an automatic production line and a detection system based on machine vision, and relates to the technical field of industrial product quality detection. According to the method, a multi-waveband imaging technology is combined with temperature and chemical component information to generate a multi-dimensional feature data set, and an unsupervised learning algorithm is utilized to perform clustering analysis, so that the limitation of traditional single-waveband imaging is broken through, product features are comprehensively captured, the defect detection range and accuracy are remarkably improved, and a foundation is laid for subsequent analysis and classification; further utilizing an attention mechanism and a deep learning technology to accurately position and classify defects, and triggering deep scanning through a priority index to improve the detection precision and the system adaptability; and finally, combining with a random forest algorithm to analyze defect influence, and feeding back and optimizing production parameters through a dynamic adjustment mechanism, thereby realizing production optimization closed-loop management, and improving production efficiency and product quality detection.

The invention provides a preparation method of a transmission electron microscope ultrathin sample with a cross section of an electron beam sensitive material, which comprises the following steps: selecting a specific region as a to-be-extracted sample region for the electron beam sensitive material by adopting an electron beam imaging technology; depositing a protective layer on the surface of the to-be-extracted sample area by adopting an electron beam and / or an ion beam; bombarding a to-be-extracted sample area by utilizing a high-energy ion beam, extracting an electron beam sensitive material cross section sample from the to-be-extracted sample area, and fixing the extracted sample to a metal bracket; performing pre-thinning treatment on the extracted sample by using a focused ion beam to obtain a first thinned sample of which the thickness is reduced to be less than a first thickness; and transferring the first thinned sample into a micro-beam fixed-point ion thinning system, and carrying out multi-time stepped low-energy ion beam fine thinning treatment on the first thinned sample by using inert gas ion micro-beams to obtain a second thinned sample which is clean in surface and free of a surface ion damage layer and has the thickness reduced to be less than a second thickness, and taking the second thinned sample as a transmission electron microscope ultrathin sample.

The invention discloses an optimization method for spatial distribution of garden plants, which comprises the following steps: generating a high-precision 3D model of a garden terrain by adopting a plant spatial adaptive layout algorithm and utilizing a laser radar or unmanned aerial vehicle technology, generating a weight matrix according to soil types, illumination intensity and drainage characteristics, and applying a dynamic weight Voronoi algorithm in the terrain model to optimize the spatial distribution of the garden plants. The method comprises the following steps: generating a planting range of each plant, establishing a root distribution model by using an underground imaging technology, simulating distribution dynamic of moisture and nutrients among root systems in combination with underground hydrological data, collecting sensor data in real time by using a wireless network, designing a dynamic irrigation scheme, and optimizing the planting density and irrigation range of the plants. Data such as terrain and climate are integrated into a virtual garden, the virtual garden is ensured to be consistent with an actual state through interaction of a sensor and a digital twinborn model, plant growth, ecological benefit changes and disaster risks are predicted by using the digital model, and a user is allowed to modify and optimize the garden layout in real time through a visual interface.

The invention discloses a brain tumor multi-mode large model construction method, device and equipment and a storage medium, and is applied to the technical field of brain tumor imagines.The method comprises the steps that pixel-concept level alignment is conducted on a multi-mode MRI image and a pathological text; constructing a multi-modal feature fusion network for fusing image features and text features by adopting an attention mechanism of pathology perception and combining medical semantic information; training the multi-modal feature fusion network to generate an analysis report and a segmentation result; according to the technical scheme of multi-task cooperation, cross-modal pathological semantic accurate alignment, pathological knowledge graph injection and lightweight and continuous optimization parallelization, full-process coverage of brain tumor accurate segmentation, analysis report generation and prognosis prediction is achieved, the problems that a traditional model lacks pathological semantic support and is insufficient in clinical adaptability are solved, and the clinical adaptability of the traditional model is improved. And the deployment feasibility and the dynamic optimization capability are also considered.

The invention discloses a substation personnel safety operation early warning system and method fusing UWB and imaging technology, and the system comprises the steps: a plurality of cameras are distributed in all regions in a substation, collect the image information in the substation in real time, and transmit the image data to a data fusion and analysis module; the UWB tag is configured to a person entering the transformer substation and periodically transmits UWB signals; the UWB base station receives the UWB signal and transmits the UWB signal to the data fusion and analysis module; the data fusion and analysis module carries out deep fusion on data acquired by the camera monitoring system and the UWB system; the weight evaluation module is used for comprehensively evaluating factors such as the safety set distance, the walking direction and the walking speed of the personnel according to a preset weight standard; and the early warning module triggers safety early warning of a corresponding level according to the safety risk score. The safety of transformer substation personnel is effectively guaranteed, and safety accidents caused by improper walking of the personnel are reduced.

The invention provides a power management chip high-low side driving signal time sequence optimization method and system. The method comprises the following steps: acquiring temperature data of a power management chip under a switch switching transient working condition through an infrared thermal imaging technology, and synchronously acquiring three-dimensional temperature field distribution of junction temperatures on the surface and inside a packaging layer; based on the space continuity of a temperature field, extracting the temperature change characteristics of a power device area, converting the temperature gradient into Seebeck voltage by using a thermoelectric material embedded in a hot spot area, and constructing a dynamic thermoelectric feedback network; according to Seebeck voltage data, a coupling relation between hot carrier concentration and driving signal phase deviation is established, and disturbance of hot carrier mobility change on MOSFET switching delay is quantified; the rising / falling edge slope of the grid voltage is adjusted based on the coupling relation, the phase deviation caused by the hot carrier effect is eliminated through dynamic compensation, and the switching performance is optimized. The dynamic response precision of the power management chip is improved.

The invention discloses a hidden camera detection method and system based on a cat eye effect, computer equipment and a storage medium, and relates to the technical field of laser imaging, and the method comprises the steps: sensing ambient light, carrying out the initialization, and carrying out the parameter adjustment in real time; transmitting a signal through a multispectral light source, and receiving a reflected signal for multiband imaging; carrying out adaptive binarization processing on the image, and calculating a local gray scale probability distribution matrix of the image; and calculating a suspected degree for target detection, and giving an alarm when a camera is recognized. According to the hidden camera detection method based on the cat eye effect, through automatic switching of multi-band light sources, the types of the light sources are optimized, detection light of different wavelengths is emitted, the broad spectrum range is covered, the reflection characteristics of various hidden imaging devices are dealt with, detection is carried out according to imaging of different light sources, and the detection accuracy is improved. The method can be used for detecting the anti-reconnaissance hidden imaging equipment.

The invention belongs to the technical field of industrial detection, and discloses a defect detection method and system based on honeycomb catalyst stacking. Omnibearing image data of honeycomb catalyst stacking are obtained through a multi-angle polarization imaging technology, pixel-level polarization degree parameters are calculated to construct a global polarization feature map, and accurate distinguishing between an intrinsic porous structure and suspected defects is achieved. A blind area identification and virtual view angle reconstruction mechanism is introduced, so that the problem of a stacked edge detection blind area is solved; and a layered reflectivity compensation function is adopted, so that the optical interference of an interlayer overlapping region is eliminated. Texture features are extracted through multi-scale morphological filtering, multi-dimensional feature fusion is carried out in combination with polarization features, edge continuity indexes and correction reflection intensity, and a high-precision defect discrimination model is established. And for a low-confidence region, dynamically adjusting detection parameters and performing iterative optimization to form an adaptive detection closed loop. According to the invention, the detection precision and reliability are improved, and the defect position, type and severity can be accurately output.

The invention relates to the technical field of commodity anti-counterfeiting, in particular to an intelligent identification and analysis method for an anti-counterfeiting mark, which comprises the following steps: firstly, acquiring environmental parameters in real time, acquiring a holographic image of the anti-counterfeiting mark through a multispectral imaging technology, and correcting the image by using environmental data to generate a standardized hologram; then microcosmic anti-counterfeiting features in the dynamic dot matrix hologram are extracted to construct holographic feature vectors; and synchronously collecting encrypted bar code data, dynamically adjusting an error correction parameter and a decoding threshold value of a bar code decoding algorithm through the environment compensation model, and obtaining a first optimized bar code decoding algorithm for decryption. And checking the aging matching between the hologram timestamp and the bar code dynamic key by using an asymmetric encryption algorithm, and calculating the matching degree between the real-time holographic feature vector and a pre-stored standard vector by using a feature similarity algorithm. And when the matching degree is greater than a preset threshold value and the dynamic key verification is passed, determining that the product is a genuine product and updating the dynamic key.

The invention discloses a real estate information verification method and system based on an image technology, and relates to the technical field of real estate information verification, and the method comprises the following steps: image collection: employing a collection device with multiple functions, and through the means of adjustable illumination, automatic paper flattening, multi-mode scanning, real-time quality monitoring, etc. Obtaining a high-quality and comprehensive paper house property certificate file image; acquiring a paper house property certificate file image by utilizing acquisition equipment which is provided with an adjustable lighting system and has an automatic paper flattening function; according to the method, comprehensive information is acquired through multi-mode scanning, the risk of information omission is effectively reduced, noise is accurately removed through image preprocessing, the contrast ratio is enhanced, information missing is repaired, a clear image is provided for follow-up processing, multiple kinds of accurate feature analysis are newly added in the feature extraction link, rich and accurate feature vectors are formed, and the image quality is improved. And the capability of judging the authenticity and integrity of the file is greatly enhanced, so that the accuracy and reliability of verification are remarkably improved.

The invention relates to the technical field of agricultural information, discloses a corn precise topdressing prescription map generation method based on unmanned aerial vehicle multispectral remote sensing, and effectively solves the key problems of insufficient monitoring precision, poor environmental adaptability, low decision intelligent level and the like in the traditional fertilization technology. Through the unmanned aerial vehicle multispectral imaging technology, efficient and accurate monitoring of the corn nitrogen nutrition condition is achieved; in combination with a machine learning algorithm, an intelligent fertilization decision model adapted to different environmental conditions is established, and the fertilization precision in a complex farmland scene is remarkably improved; and a multi-source data fusion technology is adopted, and crop growth, soil characteristics and meteorological factors are comprehensively considered, so that the fertilization scheme is more scientific and reasonable. Compared with a traditional method, the method has the advantages that accurate variable fertilization can be realized according to actual needs of crops, the utilization rate of the fertilizer is increased, environmental pollution is reduced, and reliable technical support is provided for sustainable development of modern agriculture.

The invention relates to the technical field of three-dimensional imaging, in particular to a three-dimensional imaging detection method for surface defects of a connector, and provides the following scheme: collecting a point cloud image of the connector; extracting a suspected disturbance region through a morphological operator to generate a target point; constructing an axial symmetry mapping relation of the target points, and generating a symmetric residual distribution map; based on the disturbance space gradient field, eliminating a disturbance convergent point and reserving a disturbance divergent point; and further extracting a normal abrupt change relation and a curvature minimum value region through a defect identification network, and outputting a three-dimensional coordinate set of a defect region. According to the method, the artifact elimination accuracy and the defect identification precision are effectively improved, and the method is suitable for a high-robustness three-dimensional defect detection task under a complex inner wall structure.

The invention relates to the technical field of identification model construction, in particular to a method for constructing a medicinal material identification model based on an image technology. The method comprises the following steps: acquiring a medicinal material sample image and extracting features, constructing a medicinal material identification map, acquiring an original medicinal material image and performing texture remodeling, generating a standard medicinal material image, performing multi-dimensional feature extraction and fusion, forming comprehensive feature representation, and performing growth extension simulation based on the representation. Carrying out initial identification according to the generated track and the identification map to obtain an initial result, carrying out map matching based on the initial result, carrying out edge comparison, generating edge comparison parameters, carrying out centralized contour reconstruction according to the parameters, carrying out detail texture retrieval to generate a final matching result, and carrying out connection marking to generate corresponding result data. And performing model training by using the result data to generate an intelligent medicinal material identification model. According to the invention, the efficient method for constructing the medicinal material identification model based on the image technology is realized.

The invention discloses a boiler inner wall crack detection method based on a laser ultrasonic wave and infrared thermal imaging technology. The method comprises the following steps: checking detection equipment; cleaning the inner wall of the boiler; performing preliminary visual inspection on the inner wall of the boiler; laser ultrasonic detection equipment is arranged on the inner wall of the boiler; systematically performing laser ultrasonic scanning on the inner wall of the boiler; an infrared thermal imaging camera is used for scanning the inner wall of the boiler; performing infrared thermal imaging scanning; comparing and analyzing the data; and compiling a detailed detection report. According to the invention, the laser ultrasonic detection technology can detect deep micro cracks on the inner wall of the boiler with high precision, even the micro cracks can be found before the micro cracks become significant problems, and the infrared thermal imaging technology can provide visual information about crack positions and sizes by capturing thermogram differences of crack areas, so that the accuracy of the thermal imaging is improved. This technique is particularly suited for the discovery of surface cracks and for the assessment of the scope of crack impact.

The invention relates to the technical field of three-dimensional imaging, in particular to a four-eye structured light stereoscopic vision imaging method, which comprises the following steps of: arranging four cameras and synchronously acquiring multi-view image data with structured light stripes; carrying out image preprocessing and stripe code identification on the obtained four-view-angle structured light image data, and extracting structured light stripe center line positions and corresponding space projection information under each view angle; generating three-dimensional point cloud data of the high-precision target sample by using parallax calculation and a three-dimensional reconstruction algorithm, and completing spatial registration and filtering optimization of point cloud; fusing the point cloud and the light intensity data by combining the reflection intensity information of the multi-view structured light stripes to generate a composite data set containing space and spectral information; and constructing a dense parallax field and executing three-dimensional consistency verification, and generating a high-fidelity three-dimensional reconstruction model with a complete topological relation and sparse shielding compensation capability. According to the invention, the problems of insufficient view angle coverage, shielding area information loss, difficult edge structure matching and the like of traditional stereoscopic vision imaging can be solved.

The invention relates to the technical field of three-dimensional measurement, and discloses a double-zoom system and a three-dimensional information acquisition method thereof, and the three-dimensional information acquisition method comprises the steps: adjusting a projection unit and a first micromirror array, and enabling a projection end to work in a coaxial light mode; controlling the second micromirror array to zoom by adopting a preset second zoom parameter, and processing the two-dimensional image data of the imaging unit in real time to generate a two-dimensional ultra-depth-of-field image; adjusting the parameters of the second micromirror array until the image acquired by the imaging unit is clear; a preset first zooming parameter is adopted to control zooming of the first micromirror array, and scanning information and image data are obtained in real time and processed to obtain surface height information; generating three-dimensional information containing texture information in combination with the two-dimensional super field depth image and the surface height information; according to the method disclosed by the invention, the problem of high calculation error in the absence of 2D texture information in the traditional 3D imaging technology is solved, and the height information can be accurately acquired even in an area with scarce texture information.

The invention relates to the field of power transmission line forest fire monitoring, and discloses a power transmission line multi-mode fusion intelligent forest fire monitoring method which is used for constructing an all-dimensional power transmission line forest fire prevention monitoring system and remarkably improving the intelligent level of power transmission line forest fire risk prevention and control. Comprising the steps of reasoning and identifying a smoke and fire target for a fused feature set by utilizing an improved YOLOv8 algorithm model, realizing accurate positioning of a fire point by adopting a haze penetration thermal imaging technology and a smoke cooperative monitoring technology in combination with spatial positioning, and establishing a temperature and humidity and forest fire occurrence probability correlation model based on historical data. According to the real-time temperature and humidity parameter dynamic adjustment algorithm model, the confidence threshold and the sensor acquisition frequency are detected, a dynamic threshold control method is adopted, an early warning threshold is adaptively adjusted according to parameters such as environment illumination, a final early warning result is output after cross validation of a multi-modal data fusion strategy, and the safety of the power transmission line is effectively guaranteed.

The invention relates to the technical field of remote sensing imaging, in particular to a marine organism monitoring method and system based on a remote sensing technology, and the method comprises the following steps: constructing an environment layer based on a remote sensing image and sensing data, analyzing the relation between a spectrum and an environment, monitoring changes, recognizing the spatial distribution of species, extracting a migration path, and superposing environment factors to analyze ecological response. And outputting a remote sensing indication species ecological response interaction layer. According to the method, the remote sensing image and environmental data are sorted and denoised, multi-dimensional factors are effectively integrated, data precision is enhanced, noise interference is eliminated, spectral change and water temperature gradient deviation are combined, abnormal pixels are accurately screened, sea area change trend is insight in advance, key wave bands and hot salt structures are extracted, target organisms are accurately positioned, and identification efficiency is improved. Analyzing a space offset track, restoring a migration path, constructing a dynamic evolution view angle, superposing water temperature and algae changes, analyzing time sequence synchronization characteristics, revealing ecological response turning points, and strengthening early warning and dynamic monitoring capabilities.

The invention relates to the technical field of edge computing, in particular to an intelligent parking unattended vehicle access management system and method based on edge computing. The method comprises the following steps: a vehicle detection positioning unit obtains multi-modal data based on a multi-modal sensor array module, and realizes real-time positioning of a vehicle and real-time updating of a parking space state through a Kalman filtering algorithm in combination with a QBCN information interference variable; the license plate recognition unit completes license plate feature extraction and recognition on the basis of a multispectral imaging technology and a lightweight CRNN model under the condition of abnormal illumination. The edge calculation decision unit combines a real-time rule engine and a space-time anomaly detection algorithm, optimizes a resource allocation strategy, and realizes low-delay decision and localization control; the payment authentication unit adopts a block chain intelligent contract, dynamic rate calculation, multi-factor identity authentication and abnormal payment fusing protection; and the edge cloud collaboration unit realizes model differential updating, cross-domain task scheduling and energy efficiency optimization through federated learning and space-time data compression technologies.

The invention relates to the technical field of image super-resolution, and discloses an image super-resolution method and system, computer equipment and a storage medium, and the method comprises the steps: carrying out the sampling of a low-resolution image according to a sampling strategy based on region complexity, and obtaining a shallow feature; the shallow-layer features are input into a deep-layer feature extraction model to obtain deep-layer features, and the deep-layer feature extraction model is constructed by adopting a self-attention-based visual state space model; fusing the shallow-layer features and the deep-layer features to obtain image features; the image features are input into an image enhancement model for up-sampling and residual connection, a high-resolution image corresponding to the low-resolution image is obtained, and the image enhancement model is constructed by adopting a convolution-based pixel recombination method. According to the method, through multi-scale shallow feature extraction, global and local combined depth feature extraction and convolution residual error-based image reconstruction, the detail definition and the image resolution of the reconstructed image are effectively improved, and the method is suitable for various complex scenes.

The invention, which relates to the technical field of battery fault detection, discloses a lithium battery fault detection system comprising a magnetic field sensor, a data acquisition and processing module, a magnetic field imaging unit and a fault diagnosis and positioning module. The magnetic field sensor is provided with a high-precision magnetic field sensor array around the outside of the lithium battery according to the size and the shape of the lithium battery, the three-dimensional image of the magnetic field in the lithium battery can be visually displayed by using the magnetic field imaging technology, and the details of the distribution of the magnetic field in the battery are revealed; by comparing and analyzing the magnetic field image and the preset fault feature library, the system can automatically identify and judge the fault type and accurately position the fault position according to the abnormal area in the magnetic field image, and the high-precision positioning capability enables technicians to quickly and accurately know the condition of the internal fault of the battery, so that the fault detection accuracy is improved. And powerful support is provided for subsequent fault processing and maintenance.

The invention provides a virtual imaging method and system applied to virtual shooting, computing equipment and a storage medium, and relates to the technical field of virtual imaging. The method comprises the following steps: synchronously acquiring object surface reflection characteristic data by using a multi-wavelength light source, and generating various imaging parameters through an image feature extraction network; the dynamic attitude data and binocular vision depth information are fused, and virtual and real scene dynamic space coordinate mapping is constructed; calculating the surface reflection intensity distribution of the virtual object in real time through a light propagation simulation module in combination with the specular reflectance and deformation correction parameters, and dynamically adjusting the relative position of the virtual object and the real light source; generating a continuous and smooth shadow boundary transition effect based on a material attenuation model; and performing space-time calibration on the shadow effect by using the dynamic attitude sequence to realize frame-level synchronous rendering of virtual and real light source positions and shadow changes. According to the technical scheme provided by the invention, through optical parameter modeling and dynamic path optimization, the problem of light and shadow distortion of the virtual object in a real scene is solved.

The invention relates to a multi-source monitoring and dynamic regulation and control method and system for a mill, a terminal and a medium, and belongs to the field of mill equipment control. According to the method, multi-source data such as spindle vibration, grinding cavity acoustic emission, gearbox temperature and barrel rotating speed are collected, and multi-dimensional features are extracted by combining first-order difference, energy density spectrum and thermal imaging technologies; and constructing a regression model fusing a linear term and a Gaussian kernel function, and synchronously predicting the operation efficiency, the energy consumption and the grinding particle size. And parameters such as the rotating speed and the feeding amount are dynamically adjusted based on the prediction result, and a three-level early warning mechanism is established. The system comprises a template management module, a data acquisition module, a feature extraction module, a modeling and prediction module, a dynamic control module and an early warning management module, and supports scenarized parameter configuration and adaptive regulation and control. Through multi-source data fusion and an intelligent prediction model, the problems of parameter stiffness and response lag of a traditional mill are solved, the working condition adaptability is remarkably improved, and efficient and safe operation of equipment is guaranteed.

The invention discloses a server temperature control method and system based on deep learning, and belongs to the technical field of server temperature control, and the method comprises the steps: firstly, synchronously collecting server hardware temperature, business load and environment parameters, capturing a heat conduction relation through combining a thermal imaging technology, and constructing a dynamic relation network through time-space fusion processing; a heat flow prediction model is constructed based on the network, model parameters are dynamically corrected in combination with thermal response characteristic attenuation coefficients generated by service load characteristic analysis, and temperature distribution changes are predicted; generating a candidate control strategy set, screening an optimal strategy through composite scene simulation, and generating a control instruction; and comparing the actual temperature after the control instruction is executed with the model prediction temperature in real time, switching to a safety control mode when the deviation exceeds a threshold value, and extracting an incremental sample to update model parameters after the deviation is recovered, thereby realizing accurate temperature control.