110results about How to "Accurate focus" patented technology

The invention relates to an underwater image detection system and method based on a YOLOv8s model, and the system comprises an underwater image collection and preprocessing subsystem which is used for collecting underwater image data and carrying out the preprocessing; the target detection subsystem is used for carrying out target detection by adopting an improved YOLOv8s model, and the improved YOLOv8s model is used for obtaining a feature map by adopting a mode of combining a denoising mechanism and a dynamic adjustment receptive field mechanism based on the preprocessed underwater image data, carrying out feature enhancement by adopting a dual attention mechanism based on the feature map, and carrying out target detection by adopting an improved YOLOv8s model; performing feature fusion and detection to obtain a target detection result; the performance monitoring and evaluation subsystem is used for monitoring and evaluating the target detection result; and the detection result display and output subsystem is used for outputting and displaying the target detection result. Compared with the prior art, the method has the advantages of improving the depth and precision of feature extraction, improving the underwater image detection performance and the like.

The invention discloses a voice noise reduction system in a multi-noise environment, and particularly relates to the technical field of voice noise reduction, which comprises a basic division module for dividing voice data into a plurality of basic voice segments with the same duration; the rationality analysis module judges whether segmentation points of adjacent fragments are reasonable or not, and if the segmentation points are not reasonable, the optimization marking module searches for new segmentation points for optimization; the loop module repeatedly verifies the optimization result until all the voice segments are reasonably segmented; the noise reduction module extracts voice and noise features, and performs noise reduction processing by using a noise reduction strategy selection model; the recombination module recombines the voice segments after noise reduction into complete voice output; according to the invention, the voice signal is divided into a plurality of segments, the most suitable noise reduction algorithm can be matched for each segment according to the noise feature of each segment, and the method can cope with a more complex and unstable noise environment through a dynamically adaptive noise reduction strategy, and guarantees that the voice quality is better improved in various scenes.

The invention discloses a liver tumor segmentation method based on SCAF-TransUNet, and the method comprises the steps: firstly, segmenting a liver image into semantically related superpixel regions through SLIC superpixel preprocessing, enabling a model to be more accurately focused on a tumor and other key regions, reducing the calculation redundancy, and improving the efficiency and precision of feature extraction; secondly, a CECA module introduced in jump connection replaces a traditional full connection layer through one-dimensional convolution, the attention mechanism between channels is simplified, capture of the channel dependency relation is enhanced, and meanwhile the feature extraction precision is kept; finally, the FPN is added in the decoder, and the fusion capability of the multi-scale features is further enhanced. According to the FPN, feature maps with different resolutions are fused layer by layer, so that the model can effectively combine global semantic information and local detail features before up-sampling, and the processing capacity for complex liver boundaries and small tumors is improved. Finally, the improvements significantly improve the accuracy and robustness of the liver tumor segmentation task.

The invention relates to a variable sampling start-based satellite-borne sliding spotlight SAR data processing method, which comprises the following steps of: carrying out distance direction blocking on echo data of variable sampling start, carrying out Fourier transform on sub-block data along the distance direction, multiplying by a phase compensation function, and carrying out sampling start alignment; performing two-step azimuth frequency domain aliasing removal on the sub-block data after sampling initial alignment; performing two-dimensional frequency domain matched filtering on the sub-block data after the azimuth frequency domain aliasing is removed; carrying out resampling on the sub-block data subjected to two-dimensional frequency domain matched filtering; and splicing the resampled sub-block data to obtain an imaging result of the whole imaging scene. According to the method, sampling initial alignment is carried out through a distance frequency domain azimuth time domain phase compensation function, zero padding does not need to be carried out on echo data, and the data processing difficulty is reduced; the focusing quality is improved by eliminating azimuth frequency domain aliasing, two-dimensional frequency domain matched filtering and two-dimensional frequency domain resampling.

The invention relates to the technical field of lens testing, and discloses a multifunctional optical lens testing lens device which comprises a connecting frame. A casing; a lens body; the positioning assembly is used for fixing the position when the lens body is detected; the detection assembly is used for carrying out multidirectional detection on the lens body; the detection assembly comprises an air pressure detection part, an impact detection part and a vibration detection part; and a driving assembly. According to the multifunctional optical lens testing lens device, multi-direction detection of the lens can be realized through the arranged detection assembly, and multi-direction synchronous detection in vibration, impact, air tightness, waterproofness and high-temperature environments is realized, so that each detection is in a linked state, the detection effect of the lens in different high-complexity environments is improved, and the detection efficiency is improved. And the whole detection process is carried out in one device, and single position conversion is not needed, so that a high-intensity environment is provided for the test of the lens, and the stability of the lens is finally detected.

The invention provides an infrared weak and small target detection method and system, electronic equipment and a storage medium, and belongs to the technical field of target detection, and the method comprises the steps: improving a YOLOv8 model for the features of an infrared weak and small target image; and performing target detection on the infrared weak and small target image by using the improved YOLOv8 model. According to the invention, a stronger infrared weak and small target detection capability is provided for the YOLOv8 model, and the problem of target detection precision bottleneck of a traditional model in a complex scene is effectively solved.

The invention relates to a high squint SAR imaging processing method under Doppler frequency distance time varying. The method comprises the following steps: firstly, carrying out linear walking correction and distance compression according to a squint angle; then distance partitioning and azimuth upsampling are carried out according to the pulse repetition frequency, the instantaneous Doppler bandwidth and the Doppler frequency time-varying quantity along with the distance, azimuth spectrum ambiguity caused by the change of the Doppler center frequency along with the slope distance is eliminated through azimuth spectrum extension, and sub-block splicing is carried out after upsampling of all sub-blocks is completed; carrying out azimuth space-variant resampling in a two-dimensional frequency domain, and eliminating azimuth space-variant in a range gate; then consistent compression and stot interpolation are carried out, and two-dimensional accurate focusing is completed; and finally, correcting image distance position time shift caused by linear walking correction, and intercepting an effective imaging area to obtain a focused SAR image. According to the method, the Doppler spectrum fuzzy problem caused by Doppler frequency distance time variation can be effectively eliminated, so that effective application of a traditional large squint algorithm is ensured, and a correctly focused SAR image is obtained.

The invention discloses a three-dimensional laser post-ionization-secondary neutron particle mass spectrometer which comprises a vacuum pool, a sample introduction system, a primary ion emission system, a three-dimensional laser post-ionization system, an ion optical system, a time-of-flight mass spectrometer, an observation system and a computer control system. By adjusting the wavelength, the frequency and the energy value of the laser, resonance between the energy of laser photons and the electron energy level of target molecules can be realized to realize selective ionization of substances, and the method has extremely high element, nuclide and molecule selectivity, can provide the sensitivity of an instrument, and can be used for mass spectrometry. The device can be applied to single-cell imaging analysis test of lipid, metabolites, small-molecule drugs and part of elements with the molecular weight smaller than 1000 Da, the problems of space metabolome small-molecule metabolite test and ultrahigh spatial resolution imaging are solved, and the device can be independently used as an SIMS when laser post-ionization is not used.

The invention relates to the technical field, in particular to an evaluation system and evaluation method for dynamic thermal stability of a power supply cable, and the method comprises the steps: determining a short-circuit detection point of the power supply cable based on the state of the power supply cable; determining the maximum loss point of the buried power supply cable based on the soil thermal conductivity of the buried power supply cable in a preset time period and the buried depth change rate of the power supply cable in the preset time period, and determining the maximum loss point of the buried power supply cable based on the dust accumulation thickness of the exposed power supply cable and whether the completely exposed power supply cable has cracks or not. Determining a maximum loss point of the exposed laid power supply cable; and performing a short circuit stability verification test on the short circuit detection point of the power supply cable, and determining whether the dynamic thermal stability of the power supply cable is qualified based on whether the short circuit detection point of the power supply cable is damaged or deformed after the test. According to the invention, the accuracy of short-circuit detection point analysis is improved, so that the energy waste is reduced, and the energy utilization efficiency is improved.

The invention belongs to the technical field of software engineering, particularly relates to an automatic driving system performance enhancement method based on an expert hybrid architecture, and aims to solve the core problems that an end-to-end automatic driving system is confronted with semantic fuzziness to cause unreliable decision, multi-task interference hinders optimization planning, too long reasoning delay increases driving risks and the like. According to the method, an ExpertAD framework is provided, task key features are amplified through a perception adapter (PA), and the relevance of scene context understanding is guaranteed; related driving tasks are dynamically activated through a sparse expert mixture (MoSE), and task interference is minimized; and in combination with a customized training loss function, collaborative optimization of planning effectiveness and reasoning efficiency is realized. Experiments show that compared with an existing method, the method has the advantages that the average collision rate is reduced by 20%, the reasoning delay is reduced by 25%, higher multi-skill planning capacity is achieved in rare scenes (such as accident handling and first-aid vehicle avoiding), and good generalization is achieved for unseen urban environments.

The invention discloses composite material laser welding equipment and a welding method, and belongs to the field of laser printing, the composite material laser welding equipment comprises a base plate, a wire feeding mechanism, a rotary shearing mechanism, a rolling mechanism and a laser heating mechanism are installed on the base plate, the laser heating mechanism comprises a first dovetail groove sliding table and a second dovetail groove sliding table, and the second dovetail groove sliding table is vertically and fixedly installed on the base plate; the first dovetail groove sliding table is located below the second dovetail groove sliding table, a first connecting plate is fixedly arranged on a carriage of the second dovetail groove sliding table, the first dovetail groove sliding table is fixedly installed on a second connecting plate in an obliquely downward mode, and the end, close to the rotary shearing mechanism, of the second connecting plate is hinged to the first connecting plate through a first connecting rod. And the other end of the second connecting plate is connected with the first connecting plate through a telescopic adjusting structure, a laser head is fixedly arranged on a dragging plate at the bottom of the first dovetail groove sliding table, the angle, height and focusing position of a laser beam can be controlled, and the welding requirements of composite materials of different shapes and sizes are met.

The invention discloses a millimeter-wave radar and camera combined multi-target deblurring monitoring method and system, and relates to the technical field of target tracking, and the method comprises the steps: erecting a millimeter-wave radar and a camera on the same vertical plane for monitoring; a checkerboard is used for recording the corresponding focusing time of the camera under different distances, and the function relation between the target distance and the focusing time under different focal length segments is obtained; distance, speed and angle information of multiple targets is obtained according to millimeter wave radar detection, tracks of the multiple targets are obtained, a multi-target tracking algorithm is used for conducting unified prediction and management on the tracks, and a track list is obtained; circularly selecting targets in different periods, and selecting an unfocused fuzzy target in the current period for monitoring; determining a corresponding focal length section according to the distance of the fuzzy target, finally calculating the focusing time according to the function relation, and adjusting the camera to the corresponding focal length, so that the imaging of the current fuzzy target is clear. According to the invention, accurate focusing of a plurality of targets in different distance intervals is realized.

The invention relates to the technical field of image processing, and discloses a license plate detection and recognition method and system based on YOLOv8, and the system comprises an image detection module and a license plate recognition module. The method comprises the following steps: step 1, a high-definition camera shoots vehicle images, and images meeting a screening standard are reserved to form an image data set; step 2, marking a central point ZD of each image, and establishing a two-dimensional coordinate system after the central points ZD of all the images are overlapped and calibrated to be original points; 3, dividing the image in the two-dimensional coordinate system into grids, and calculating the total number B of bounding boxes of all the grids; 4, calculating a confidence score ZX of each bounding box by using a YOLOv8 algorithm, filtering the bounding boxes by using an NMS, and keeping a high-score bounding box for continuous recognition, so that the robustness of an algorithm optimization model is more stable; and step 5, the RNN algorithm model calculates and generates a target character group Mzf, license plate characters are output in combination with a preamble and a subsequent sequence, and the license plate character recognition precision is high.

The invention discloses an automatic focusing method and device, computer equipment and a computer readable storage medium, and relates to the technical field of focusing. The method comprises the following steps: performing multi-target tracking detection on a preview image frame to obtain a face frame set, a human eye frame set and a multi-target tracking parameter; performing scale measurement aperture unification on the face frame set and the eye frame set to obtain a target face frame width and a target eye frame width; calculating a human face available threshold value and a human eye available threshold value; determining a depth-of-field coverage judgment result of the focusing target according to a depth map corresponding to the preview image frame; and according to the target face frame width, the target eye frame width, the multi-target tracking parameter after smoothing normalization, the depth-of-field coverage judgment result, the face available threshold and the eye available threshold, determining a focusing strategy to perform focusing switching. Therefore, the stability of the exported picture, the accurate focus and the smooth switching are ensured through automatic focusing.

The present disclosure provides a depth confidence map processing method, a depth confidence map processing device, a computer readable storage medium and an electronic device, and relates to the technical field of computers. The depth confidence map processing method comprises: determining a target region on a depth map, the target region being a region of interest of a user; determining an average value of pixel depth values in the target region; adjusting the pixel depth values of each pixel point in the depth map based on the average value to obtain a confidence weight map; and optimizing an initial depth confidence map using the confidence weight map. The present disclosure can improve the accuracy of the depth confidence map.

The invention relates to an acoustic signal processing and biological recognition technology, in particular to a method for fusing sound source localization and voiceprint recognition, a chip and electronic equipment. The method comprises the following specific steps: synchronously acquiring an environment audio signal through an audio acquisition module array and processing the environment audio signal to obtain audio data; and analyzing the audio data to obtain deep fusion voiceprint features. And judging whether a registered user feature library contains the deep fusion voiceprint feature or not, if so, outputting a sounder identity, and if not, marking and endowing an identification number of the sounder identity. And positioning the sound source based on the audio data to obtain a space coordinate thereof. And establishing an identity-position association model, binding identity information output by voiceprint recognition with space coordinates obtained by sound source positioning, and generating identity-position association data. According to the method, the technology is fused and innovated, accurate linkage of identity-position is realized, high real-time performance and strong anti-interference capability are realized, and the method is adaptive to complex scenes.

The invention discloses a quantitative detection device and a quantitative detection method for multiple types of side walls of a silicon carbide crystal bar, and belongs to the technical field of silicon carbide crystal bar detection. The quantitative detection device comprises a three-dimensional adjusting table, a distance measuring mechanism, a Raman detection mechanism, a conveying mechanism, a visual judgment mechanism and a control mechanism, wherein the three-dimensional adjusting table is used for placing a silicon carbide crystal bar to be detected and driving the silicon carbide crystal bar to be detected to rotate in a three-dimensional manner; the ranging mechanism is used for testing the thickness of the to-be-tested silicon carbide crystal bar; the Raman detection mechanism comprises a Raman detector, a detection pipeline, a corner lens and a detection port; the transmission mechanism is used for adjusting the distance between the three-dimensional adjusting table and the detection port; if focusing is abnormal, the visual judgment mechanism transmits information to the control mechanism, and the control mechanism controls the three-dimensional adjusting table and the conveying mechanism to achieve focusing. According to the device, the visual judgment mechanism, the three-dimensional adjusting table and the conveying mechanism are matched to realize automatic focusing of Raman laser, so that the detection precision and the detection efficiency are improved, and multi-type quantitative detection on the side wall of the silicon carbide crystal bar is realized.

The application discloses a deep-sea biological image processing method based on a neural network, belongs to the technical field of image processing, and is used for deep-sea biological image processing and comprises the following steps: preparing a target data set, constructing a neural network, inputting a processing result of a Backbone layer into an improved Neck layer, obtaining a deep-sea biological image processed and optimized after being processed by the improved Neck layer, and inputting the deep-sea biological image processed and optimized into a Detect layer; the improved Neck layer comprises a dynamic self-adaptive up-sampling module, a multi-level feature fusion module, an HT_C2f module based on HyperTransformer, and a down-sampling convolution module. The application realizes accurate focusing of target features under a low signal-to-noise ratio condition by improving the Neck layer, improves the feature consistency of multi-scale target processing, reduces the loss of key information in the dimension reduction process while improving the small target feature retention rate.

The utility model discloses an optical lens group and a vehicle lamp using the same. The optical lens group comprises a first lens, a second lens and a third lens which are sequentially arranged from an object side to an image side along an optical axis direction, the first lens is a glass lens, and the second lens and the third lens are plastic lenses. The image side surface of the first lens, the object side surface and the image side surface of the second lens and the object side surface and the image side surface of the third lens are all of aspheric surface structures; the object side surface of the first lens is a plane and the image side surface is a convex surface; the object side surface of the second lens is a concave surface and the image side surface is a convex surface; the third lens element has a concave object-side surface and a convex image-side surface. The optical lens group provided by the utility model is beneficial to miniaturization and light weight of a lamp, the production cost is reduced, better image definition and details can be obtained, and the imaging quality is high.

The utility model relates to a device for measuring the precision of a reading microscope, which belongs to the technical field of measurement and detection, solves the technical problem of lack of a special device for detecting the precision of the reading microscope, and comprises a basic platform, a two-dimensional working table, a rotary working table, a stand column and an arm support, the rotary workbench comprises an outer ring, an inner ring, a gear ring, a lamp strip, a slide glass and an adjusting gear shaft, and the glass linear scale is placed above the rotary workbench; the stand column is vertically and upwards arranged on the foundation platform, and the arm frame is installed on the stand column in a sliding mode and comprises a coarse adjustment part and a fine adjustment part. The device is specially used for detecting the precision of the reading microscope, not only can light rays in a visual field be supplemented, but also the glass linear scale can be moved through the two-dimensional workbench and the rotary workbench, and the height of the reading microscope can be adjusted through the arm support, so that the reading microscope and the glass linear scale can be quickly and accurately focused.

The utility model provides an optical fiber head adjusting device and an optical fiber head adjusting system. The optical fiber head adjusting device comprises a horizontal adjusting piece and a vertical adjusting piece. The horizontal adjusting piece comprises an optical fiber fixing section and a horizontal adjusting section and is provided with a first through hole. The vertical adjusting part comprises a containing section, a vertical adjusting section and a positioning section, threads are arranged on the outer side of the vertical adjusting section, and the positioning section is cylindrical. The vertical adjusting part is provided with a second through hole and a third through hole, the center lines of the second through hole and the third through hole coincide, the diameter of the second through hole is larger than that of the third through hole, and therefore a step structure is formed at the junction of the containing section and the vertical adjusting section, and the horizontal adjusting part is placed on the step structure. A plurality of horizontal adjusting holes are formed in the side wall of the containing section, and horizontal adjusters are arranged in the horizontal adjusting holes correspondingly and used for pushing the horizontal adjusting pieces in different directions. According to the technical scheme, the position of the optical fiber head can be adjusted in the horizontal plane and the vertical direction, and therefore it is effectively ensured that the transmission path of optical signals is accurate.

This invention provides an intelligent analysis system for photoelectric detection spectral data based on deep learning, belonging to the fields of photoelectric detection, spectral data analysis, and deep learning application technology. The system includes core units such as a spectral data acquisition unit, a preprocessing unit, a feature extraction unit, and a deep learning analysis unit, as well as auxiliary units for anomaly detection and data storage, forming a closed-loop architecture of acquisition-processing-analysis-output-optimization. It improves data quality through step-by-step preprocessing algorithms, reduces computational load by adaptively selecting key features, deeply mines spectral patterns using a model that integrates convolutional neural networks and attention mechanisms, achieves rapid scenario adaptation through transfer learning, and dynamically optimizes model parameters based on a feedback mechanism. The system solves the problems of low accuracy, poor adaptability, and long training cycles in traditional spectral analysis, achieving accurate, efficient, and intelligent spectral data analysis, and is suitable for various photoelectric detection scenarios such as qualitative and quantitative analysis of substances and environmental monitoring.

The invention discloses a speech noise reduction system in a multi-noise environment, and specifically relates to the technical field of speech noise reduction, comprising a basic division module that divides speech data into a plurality of basic speech segments of the same duration; a rationality analysis module that determines whether the segmentation points of adjacent segments are reasonable, and if not, an optimization marking module that finds new segmentation points for optimization; a loop module that repeatedly verifies the optimization result until all speech segments are segmented reasonably; a noise reduction module that extracts speech and noise features, and uses a noise reduction strategy to select a model for noise reduction processing; a reorganization module that reorganizes the noise-reduced speech segments into complete speech outputs; the invention divides a speech signal into a plurality of segments, and can match the most suitable noise reduction algorithm for each segment according to its noise features, and can cope with a more complex and unstable noise environment through a dynamically adapted noise reduction strategy, and ensure that the speech quality is well improved in various scenarios.

The application discloses a vibration wave measuring system for traditional Chinese medicine shallow needle manipulation analysis, and belongs to the technical field of vibration wave measurement, and specifically comprises the following steps: a first beam splitter in a laser Doppler vibrometer is used to divide outgoing laser into a measuring light beam and a reference light beam, the measuring light beam is vertically focused on the surface of a needle handle, the reference light beam is vertically focused on an adjacent unoperated skin area, after the two light beams are reflected, interference occurs when the light beams return to a probe to form a first interference light signal, meanwhile, a part of the returned reference light beam is separated to interfere with local light of the system to form a second interference light signal, after photoelectric conversion, common-mode background noise is eliminated through a differential amplifier, and finally, a pure needle handle vibration wave is output, and high signal-to-noise ratio non-contact measurement of high-frequency weak vibration signals generated by traditional Chinese medicine shallow needle manipulation is realized.

The utility model relates to an automatic focusing three-dimensional five-axis laser cutting head which comprises an optical fiber QBH automatic collimation assembly, a C-axis rotating assembly, an A-axis rotating assembly, a light path reflection assembly and a cutting assembly, and has the advantages that the structure is compact, focusing is accurate, the optical fiber QBH automatic collimation assembly and an automatic focusing lens assembly are adopted, the center position and the focal length of a light beam can be accurately adjusted, and the cutting efficiency is improved. Stable and consistent cutting quality is guaranteed, and cutting precision is improved. The protection assembly is internally provided with pressurized gas to prevent dust pollution, and the service life of the optical element is prolonged; the anti-collision assembly is in magnetic attraction connection with an annular magnet through a contact type sensor, the cutting head is effectively protected against collision damage, the equipment reliability is improved, and the service life is prolonged. The laser machining device is high in adaptability, can adapt to various types of lasers, meets different machining requirements, has good universality and flexibility, and expands the application range. The automatic focusing function reduces manual intervention, accelerates the cutting speed, improves the production efficiency and reduces the production cost.

This invention provides a high-heat-dissipation battery component for new energy vehicles, belonging to the field of new energy vehicle heat dissipation battery technology. It includes a protective shell, inside which a cooling module is installed. The cooling module is used to cool the entire battery module. The battery module consists of a mounting box and battery cells. The battery cells are installed in mounting slots within the mounting box. A top plate is installed on the top of the battery module. The cooling module is connected to a serpentine cooling pipe, which has several adjusting components. Several adjusting components are arranged between adjacent sections of the serpentine cooling pipe. This invention, through the corresponding combination design of the cooling module, the serpentine cooling pipe and adjusting components, and the serpentine cooling pipe and adjusting components, achieves dual heat dissipation functions: overall cooling of the battery module and precise heat dissipation of individual battery cells, thus improving maintenance convenience.

The embodiment of the invention discloses an epoxy material defect repairing device and method based on infrared focusing heating and a medium. The device comprises an image collector, a defect locator, a console, a focusing controller and a focusing device, and the image collector is used for collecting epoxy material image data; the defect locator is used for detecting the state information of the epoxy material and acquiring the information of the epoxy material according to the image data of the epoxy material; the console is used for acquiring epoxy material defect data according to the epoxy material information, and matching optimal repair data according to the current epoxy material defect data; the focusing controller is used for outputting a repairing signal for repairing the defective epoxy material by the focusing device according to the optimal repairing data; and the focusing device is used for generating infrared radiation according to the repair signal and focusing the infrared radiation to the defective epoxy material so as to perform defect repair on the defective epoxy material. And infrared radiation is generated through the focusing device and is focused on the defective epoxy material, so that accurate repair of the defect is realized.

The present application relates to a pathological image cell segmentation method based on a U-Net model, belonging to the field of deep learning and medical image segmentation. The present application adopts an improved U-Net model, and in the preprocessing stage, converts the original point label data into a continuous density map. A multi-scale attention module is added in the decoder stage of the network. The module effectively combines multi-scale feature fusion and soft attention mechanism, can comprehensively capture global and local features in the image, and significantly enhance the representation ability of key cell features, thereby improving the accuracy of cell segmentation. In addition, the present application adds a pixel channel fusion module between each layer of the decoder, which further enhances the model's ability to identify details by integrating local information and pixel-level attention into the channel attention mechanism. The present application can effectively segment Ki-67 positive cells, Ki-67 negative cells and tumor infiltrating lymphocytes in IHC stained pathological images.

The present application relates to radar detection and intelligent processing technical field, especially in kind of phased array radar dangerous area intelligent prediction adaptive scanning system and method, including data acquisition module, data fusion and situation awareness module, dangerous area prediction and management module, scanning strategy optimization module and early warning release and optimization module, it constructs through multimodal data acquisition, fusion and situation awareness model, realizes the intelligent prediction and dynamic management of three-dimensional dangerous area, and then drives the adaptive optimization scanning strategy of phased array radar.The present application solves or at least alleviates the problem of insufficient situation cognition, risk prediction and initiative of existing phased array radar in geological disaster monitoring, provides a kind of phased array radar dangerous area intelligent prediction adaptive scanning system and method, realizes efficient, forward-looking initiative monitoring and early warning, improves the timeliness and accuracy of early warning.

The application belongs to the technical field of laser processing, and discloses a laser processing device and a focusing mechanism thereof. The focusing mechanism comprises a mounting frame and at least two focusing members. The mounting frame comprises at least two mounting holes arranged in the mounting frame along the length direction of the mounting frame. Each focusing member is detachably arranged in the corresponding mounting hole, and each focusing member is provided with a focusing position. The laser processing device comprises a laser processing head with a focusing optical member and a mounting mechanism for detachably mounting the laser processing head. The focusing mechanism is used for pre-focusing the laser processing head of the laser processing device. The predetermined optical axis position of the focusing optical member in the laser processing head is adjusted by adjusting the straight line where the connecting lines of the focusing positions are located, so as to obtain the target mounting position and the target mounting angle of the laser processing head. Therefore, the laser processing head can be simulated by the focusing mechanism for pre-focusing, which is convenient to operate and accurate in focusing, and is beneficial to improving the processing efficiency and the yield of the laser processing device.