4698 results about "Imaging technology" patented technology

The embodiment of the invention discloses a visual inspection and quality evaluation method for a small structural component of an airplane, and the method comprises the steps: S1, obtaining three-dimensional point cloud data, and carrying out the preprocessing of the three-dimensional point cloud data; s2, carrying out multi-angle two-dimensional image acquisition; s3, mapping the two-dimensional image texture obtained in the S2 to the surface of a three-dimensional model, and generating the three-dimensional model comprising surface information; s4, extracting geometric features and surface texture features from the three-dimensional model; s5, fusion and comparative analysis are carried out to identify potential anomalies and defects; s6, outputting specific abnormal types and positions; and S7, performing comprehensive quality evaluation according to an anomaly detection result in the step S6, and generating a quality evaluation report. According to the technical scheme provided by the embodiment of the invention, by fusing three-dimensional scanning and high-resolution two-dimensional image technologies and adopting the classification model based on machine learning, the comprehensiveness, accuracy and efficiency of detection are remarkably improved, so that the production quality and safety are effectively improved.

The invention belongs to the technical field of remote sensing image imaging, and provides a remote sensing image unsupervised domain adaptation method based on comparative learning and multi-prototype alignment, and the method specifically comprises the steps: constructing a teacher-student network, and setting a cross-domain multi-prototype alignment module and a pixel-level cross-domain comparative learning module which are needed in training in the teacher-student network; during teacher-student network training, a cross-domain multi-prototype alignment module is used for extracting prototypes of all categories in a source domain and a target domain and minimizing the feature distance of the prototypes, and a pixel-level cross-domain contrast learning module is used for explicitly modeling the global semantic relationship of cross-domain pixel samples; and an embedding space with high intra-class compactness and high inter-class separability is established.

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.

Three-dimensional outdoor positioning can be considerably enhanced by using various 3D imaging techniques, LiDAR ranging measurements of surrounding landmarks, and Inertial Measurement Units (IMUs) reaching accuracies in the centimetres range. These technologies, in conjunction with global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, and BeiDou, provide autonomous geo positioning. It is crucial to acknowledge the constraints of dead reckoning, which may result in mistakes in positional precision. Integrating modern imaging and correction techniques can significantly improve the accuracy and dependability of outdoor localisation. The amalgamation of various 3D imaging methodologies with LiDAR enhancements improves the comprehension and ranging of the environment, tackling the difficulties presented by GNSS constraints. The integration of IMUs with satellite systems ensures the provision of continuous traveling positioning data, even in instances of obscured or faulty satellite signals. This comprehensive approach improves outdoor location precision and expands the use of these technologies across other domains, such as autonomous navigation, robotics, and geospatial analysis, resulting in more effective and dependable positioning solutions. The hardware system consists of a swarm of sensors, including GNSS, INS, IMU, LiDAR, stereo imaging, and RGB technologies, which are combined to generate three-dimensional colored representations of natural landscapes and urban environments. This system reconstructs topographical features, enabling detailed mapping and analysis. It produces comprehensive three- dimensional sensory data, including geo-location and POI identification, and incorporates advanced image processing techniques, such as edge detection, for precise spatial awareness and environmental understanding. The system is designed with a compact form factor, making it easily integrated into wearable devices and autonomous vehicles. The integration of these technologies forms a sophisticated network of swarm intelligence, crucial for autonomous vehicle positioning and navigation. Particle swarm optimization (PSO) is used to address non-deterministic navigation, with different coupling fusion methods available. The Neuro-fuzzy PSO AI Positioning and Navigation System generates three-dimensional sensory data, providing detailed information about geographical distribution and dead reckoning areas.

The invention provides an electromagnetic photoacoustic synchronous integrated brain function imaging method and device, and relates to the field of multi-mode brain function imaging research. According to the invention, electroencephalogram, magnetoencephalogram, functional near infrared spectrum and functional ultrasonic imaging technologies are fused, important advantage complementation is formed in signal specificity and temporal-spatial resolution, and neural activity can be described from the angle of human cranial nerve electrophysiology and neural metabolism can be observed from hemodynamic information at the same time. And electrophysiology and hemodynamic activities of the brain can be synchronously recorded.

The invention relates to the technical field of intelligent agriculture, in particular to a mulberry field monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring environment and soil data of a mulberry field through a sensor network to obtain original sensor data; performing time sequence data integration on the original sensor data to obtain an environment parameter data set; the method comprises the following steps: performing multispectral image acquisition on mulberries in a mulberry field through a multispectral camera, and performing image preprocessing to obtain preprocessed image data; estimating the chlorophyll content according to the preprocessed image data to obtain chlorophyll content data; and performing growth health parameter analysis according to the preprocessed image data and the chlorophyll content data to obtain growth health parameters. By combining the sensor technology, the multispectral imaging technology and the intelligent algorithm, comprehensive, accurate and real-time monitoring of the mulberry field is achieved, and an effective technical means is provided for fine management of the mulberry field.

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 provides a fine-grained phytoplankton microscopic image enhancement classification method and a model building method thereof, and belongs to the technical field of underwater image enhancement classification, and the method comprises the following steps: firstly, collecting phytoplankton samples from a plurality of sea areas, and obtaining image data through a microscopic imaging technology; screening, preprocessing and data enhancement are carried out on the images, and a high-quality training set and a high-quality test set are constructed; according to the method, a double-branch deep learning model HySwinFormer is developed, the double-branch deep learning model HySwinFormer comprises a multi-scale context capture branch based on hierarchical vision TransFormer and a local detail enhancement branch designed by adopting a parallel convolution kernel, and joint expression of features is realized through a designed weighted fusion strategy; and training and optimizing the model to obtain a final model. Experimental results show that the classification performance of the HySwinFormer model on multiple data sets is superior to that of an existing mainstream deep learning model, and the classification effect is remarkably improved.

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 photovoltaic power station management method and system based on artificial intelligence and big data analysis, and relates to the technical field of photovoltaic power generation. The photovoltaic power generation group comprises a plurality of photovoltaic group strings used for connecting a plurality of photovoltaic panels, at least one combiner box, a direct current cable, a central inverter and an alternating current cable; according to the method, the running state of a photovoltaic power generation set is monitored in real time, abnormal conditions are rapidly recognized based on comparison of predicted power generation capacity and first photovoltaic power generation capacity, faults are subjected to hierarchical management through a comprehensive fault evaluation index, a proper rectification scheme is matched, in addition, the state of a photovoltaic panel is subjected to nondestructive testing through visible light and infrared imaging technologies, and the reliability of the photovoltaic power generation set is improved. According to the method, meteorological data and historical operation parameters are combined, fault reasons and damage degrees are comprehensively evaluated, fault data are updated and uploaded to a photovoltaic data platform after maintenance is finished, model performance is optimized, and the management method improves response speed, reduces fault processing time and reduces maintenance and management cost.

The invention relates to the technical field of medical image reconstruction, and discloses an ultrasonic tomography enhanced opto-acoustic image reconstruction method, which comprises the following steps that: each array element of a transducer sequentially transmits an ultrasonic signal, other array elements receive the ultrasonic signal, and sound velocity information of a tissue is reconstructed through an ultrasonic tomography technology; irradiating the tissue by using pulse laser, and receiving a photoacoustic signal emitted by the tissue by using a transducer; and establishing a linear equation set to describe the relationship between the initial pressure distribution and the photoacoustic signal, and iteratively solving to reconstruct the initial pressure distribution. Dual-mode imaging of ultrasonic tomography and photoacoustic tomography is realized by using the same equipment, a sound velocity distribution diagram and a photoacoustic original signal are respectively obtained, and sound velocity information of ultrasonic tomography is introduced into reconstruction of a photoacoustic image, so that the reconstruction quality of the photoacoustic image is effectively improved. Experiment and simulation results show that the method has high operability and application effectiveness.

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 discloses a method for analyzing mineral trace elements by utilizing a micro-Raman spectrum, which comprises the following steps of: fixing a mineral sample on a rotary sample table of a micro-Raman spectrometer, selecting an area to be detected by combining a micro-imaging technology, collecting a Raman background signal under the pre-illumination of low-power laser, and analyzing the trace elements in the area to be detected according to the Raman background signal. The laser focal length, the spot size and the incident angle are adjusted accordingly, the laser is focused on a target area, under the calibrated laser condition, through a multi-angle excitation micro-area, after an algorithm is applied to optimize signals, reduce impurity interference and integrate multi-angle Raman signals, the characteristic spectrum information of the microelements can be extracted, and the microelements can be accurately detected. High-resolution analysis of sample components is realized, and a three-dimensional distribution model of microelements in a microcell is constructed in combination with microscopic imaging data. The method has the characteristics of non-destructiveness, high resolution and rapid detection, and is suitable for mineral component analysis in the fields of geology, material science and the like.

The invention provides an intelligent assembling and welding manufacturing method and system for an open-type pressure machine tool body, and relates to the technical field of open-type pressure machine tool body assembling and welding. The method comprises the steps that according to the structural characteristics of the open-type pressure machine tool body, the machine tool body is divided into a plurality of independent functional modules, and the functional modules are positioned and clamped; the assembled open type pressure machine tool body is obtained; the method comprises the steps that a welding sequence optimization model of welding seams is established according to a key stress structure of an open type pressure machine tool body, and the optimal welding sequence of the welding seams is obtained by solving the welding sequence optimization model; welding the assembled open-type pressure machine tool body by utilizing the optimal welding sequence, and in the welding process, identifying a welding seam type and defect characteristics by adopting a phased array ultrasonic imaging technology, outputting a welding defect ultrasonic imaging result, predicting an expansion evolution behavior of the defect according to an intelligent detection result, and forming a quantitative defect evaluation system; and multi-information fusion intelligent evaluation of the welding quality is realized.

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 wall flatness correction method and system for building construction, and relates to the technical field of building construction quality detection.The method comprises the following specific steps that S100, joint detection preparation is conducted, specifically, a wall is cleaned, a high-precision thermal imager and a flatness measuring tool are prepared, and preparation is made for joint detection; s200, thermal imaging and flatness synchronous detection, wherein a thermal imager is used for conducting comprehensive scanning on the wall face, and meanwhile a flatness measuring tool is used for conducting surface flatness detection; the thermal imaging technology and the flatness detection technology are fused, the problems of internal temperature abnormity and external flatness of the wall surface can be detected at the same time, comprehensive and deep evaluation of the wall surface quality is achieved, the defect that a traditional flatness detection means is insufficient in internal defect detection is overcome, and through data fusion analysis, the detection accuracy of the wall surface is improved. And areas with temperature anomaly and flatness problems can be accurately identified, so that the comprehensiveness and the accuracy of detection can be obviously improved.