8528 results about "Ray" patented technology

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 relates to the technical field of industrial product defect detection, and discloses a defect detection method combining vision and X-ray detection technologies, which comprises the following steps of: synchronously acquiring vision image data and X-ray transmission data in a surface coverage area of a detection object, performing combined preprocessing to complete noise suppression, artifact elimination and time alignment, and then acquiring the X-ray transmission data; according to the method, surface texture features and internal structure features are extracted respectively, cross-dimensional information mapping is performed through a multi-modal association module to generate a fusion feature set, surface damage, internal holes and boundary discontinuous defects are identified, and finally, a hierarchical detection report is generated according to defect space distribution and severity and is updated continuously. According to the method, the advantages of vision and X-ray detection are integrated, cooperative detection of surface and internal defects is achieved, the comprehensiveness, accuracy and real-time performance of defect detection are improved through multi-modal data synchronous collection, feature fusion and dynamic report generation, and the method is suitable for part quality detection in the industrial manufacturing field.

The invention provides a weight metering automatic classification and calibration method and system based on image recognition, and relates to the technical field of weight metering, and the method comprises the steps: carrying out the imaging of the surface of a weight through dual-light-path high-speed camera shooting, carrying out the gamma correction, and extracting contour features and surface defect features; internal density distribution is obtained through X-ray imaging; performing multi-scale feature fusion on the surface features and the density distribution to construct a holographic feature model; analyzing the dynamic change trend of the characteristic parameters based on a deep variational Bayesian network, and performing evaluation and scoring in combination with a gradient boosting decision forest to obtain an initial classification; establishing a dynamic evaluation model by adopting a self-organizing competitive learning network, determining a weight grade and setting calibration parameters; selecting a corresponding reference weight to establish a grading calibration compensation model, and determining an adaptive weight coefficient for dynamic adjustment by combining defect distribution; and predicting a performance degradation trend by adopting a Shenchang differential equation network, and outputting calibration parameters and generating early warning information when the calibration precision meets a threshold value requirement.

The invention discloses a PCB detection method and device based on multi-modal fusion and an adaptive algorithm, and relates to the field of electronic manufacturing detection. The method comprises the following steps: acquiring optical imaging data by using an optical imaging unit, acquiring tomography data by using an X-ray unit, and acquiring three-dimensional point cloud data by using a laser unit; collecting a reference signal, and updating detection parameters of the optical imaging unit, the X-ray unit and the laser unit; performing feature level fusion to obtain multi-modal fusion data; and inputting the multi-modal fusion data into a detection model, and performing defect comparison in combination with a historical database to obtain a defect detection result of the PCB. According to the method, optical imaging, X-ray penetration and laser three-dimensional modeling technologies are combined, and full-dimensional defect detection of the surface, the interior and the three-dimensional structure of the PCB is covered, so that the defect accuracy is improved. Meanwhile, a traditional fixed parameter detection mode is broken through through a self-adaptive intelligent algorithm, dynamic parameter optimization and multi-scene self-adaption are achieved, and the detection efficiency is effectively improved.

The invention discloses a GIS (Gas Insulated Switchgear) tiny defect detection method and system based on X-ray multi-effect fusion, and the method comprises the following steps: firstly, exciting a latent defect in a GIS to generate partial discharge by using an X-ray photoionization effect, detecting a discharge signal by using an ultrahigh frequency method and an ultrasonic method, and after the discharge signal is processed by a noise reduction model, carrying out noise reduction on the discharge signal; and respectively obtaining an ultrahigh-frequency discharge phase spectrogram and an ultrasonic discharge phase spectrogram. Then, the imaging plate is placed on the back face of the GIS, digital DR imaging is carried out through X rays, and an X-ray image is obtained; the ultrahigh frequency discharge phase spectrogram and the ultrasonic discharge phase spectrogram are fused after being denoised, an X-ray image is combined to establish a GIS health state diagnosis model, and reliable defect detection is carried out on the GIS according to the evaluation score of the diagnosis model. According to the method, multiple effects of X-rays are fused, so that smaller GIS latent defects can be found, the detection sensitivity is remarkably improved, and the problem that the traditional method is difficult to identify the tiny latent defects is solved.

Disclosed in the present invention is a three-dimensional Gaussian splatting optimization method for an unposed input. The method comprises: for an input image, predicting a ray bundle distribution by using a ray prediction model, to obtain distribution features in the form of ray bundles; on the basis of the distribution features in the form of ray bundles, calculating a camera pose; for the ray bundle distribution, performing sampling on the basis of the volumetric density of light rays, to obtain an initial spatial distribution of a three-dimensional Gaussian point cloud focused on a visual center area; for the input image, obtaining a visible shell by means of view frustum projection and object-mask computation; on the basis of the initial spatial distribution of the three-dimensional Gaussian point cloud and the visible shell, performing three-dimensional Gaussian splatting scene training, to obtain a three-dimensional scene reconstruction model satisfying a preset loss function criterion, the loss function comprising a training regularization term for a camera pose parameter. The present invention provides important scene initialization information for three-dimensional Gaussian splatting training, and significantly improves the quality and the richness of detail of the final three-dimensional structure.

The invention relates to the technical field of ophthalmology optics, in particular to a myopia control lens optical system integrating ray tracing and aberration correction, which comprises a wavefront phase acquisition module, a wavefront feature statistics module, a ray tracing simulation module, a microstructure form regulation and control module and a feedback control module. According to the method, accurate identification and datamation description of wavefront change under a dynamic vision condition can be realized by acquiring an eyeball wavefront phase matrix, performing gridding partition analysis and extracting local phase offset amplitude and time sequence characteristics, and a light propagation path model is constructed by combining statistical characteristics of an abnormal wavefront region; the self-adaptive compensation is carried out on the local focus deviation, and the spatial distribution optimization of the refractive index and the curvature is further realized based on the microstructure form regulation and control, so that the real-time matching of the optical parameters and the wavefront state is maintained during the processing and use of the lens, and therefore, the imaging quality is stabilized, the visual definition is improved, and the myopia control reliability is enhanced.

The invention discloses a curved surface stress detection method, which comprises the following steps of: 1, fixing a detected piece on an object placing table by using a self-adaptive clamp; step 2, scanning the measured piece to generate three-dimensional shape data of the measured piece; 3, calibrating the initial position of the measured piece by a displacement sensor, and establishing a coordinate system; step 4, adjusting the position of the measured piece by using a self-adaptive clamp, emitting X-rays to the measured piece by using an X-ray tube, and collecting diffraction signals by using a second detector; and step 5, the calculation module calculates the stress distribution state of the measured piece. According to the curved surface stress detection method, the problems of low irregular surface positioning precision and poor adaptability of traditional equipment are solved, and the detection efficiency and reliability are remarkably improved.

Apparatus and method for efficient storage of BVH nodes in blocks. For example, one embodiment of an apparatus comprises: bounding volume hierarchy (BVH) construction circuitry to construct a BVH based on primitives of a graphics scene; and block allocation hardware logic coupled to or integral to the BVH construction circuitry, the block allocation hardware logic to allocate a plurality of nodes of the BVH into a plurality of blocks for storage in a cache or memory subsystem, the block allocation hardware logic to maximize a number of blocks which include a leading parent node and one or more corresponding child nodes of the plurality of nodes.

The invention provides a container multi-angle imaging quality detection method and system, and the method comprises the steps: carrying out the illumination and imaging of a target container from a plurality of preset visual angles through employing light sources with different wavelengths, and obtaining a refraction displacement fingerprint data set; carrying out regional division on the surface of the container according to the data set, calculating the refraction influence degree of each region under different visual angles, and generating a regional visual angle weight distribution diagram; performing optical path reverse tracking processing on the images acquired at each view angle by using the distribution map, constructing a light propagation path model, and performing position mapping and distortion compensation to obtain a corrected image; and finally, according to the corrected image and the regional view angle weight distribution diagram, defect detection is carried out by adopting a regional adaptive fusion algorithm. According to the method, through technical means such as region adaptive division, optical path reverse tracking and region adaptive fusion, multi-view images can be accurately aligned and fused, meanwhile, observation characteristics of different regions are considered, and the reliability of a defect detection result is ensured.

The invention relates to the technical field of scintillator materials and X-ray detection, in particular to a rare earth organic-inorganic hybrid complex scintillator and a preparation method and application thereof. The general formula of the rare earth organic-inorganic hybrid complex scintillator is [RE (L) 4] <->. [A] < + >, wherein RE is selected from Eu, Tb, Sm or Dy; l is a beta-diketone ligand; [A] < + > is selected from long-chain hydrophobic quaternary ammonium salt cations or heterocyclic quaternary ammonium cations which have the carbon atom number of C8-C20 and do not contain active functional groups. The rare earth organic-inorganic hybrid complex scintillator provided by the invention has good solubility and molecular-level dispersion capability. The scintillator film based on the rare earth organic-inorganic hybrid complex prepared by using the rare earth organic-inorganic hybrid complex has the characteristic of compatibility of high scintillator load and high light transmission, overcomes the problems of low scintillator load rate, poor transparency and limited imaging resolution of the traditional scintillator film, and has excellent practical value and popularization prospect.

The invention discloses a marine surveying and mapping method based on multi-beam sounding correction, and belongs to the technical field of marine surveying and mapping. The method comprises the following steps: acquiring sounding echo signals, sound wave propagation time delay data and temperature data through a multi-beam sonar array, an acoustic chromatography transceiving array and a laser Raman probe; and fusing the data to construct a dynamic refractive index field model, correcting sound ray refraction distortion by adopting an adaptive variable step size algorithm, carrying out weight allocation and data fusion on space-time adjacent beams to obtain high-confidence water depth, and generating an isobath topographic map. The problem of refraction distortion is solved through multi-source data fusion, the sound ray tracking step length is dynamically optimized, noise interference is suppressed through weighted fusion, and the precision and reliability of topographic surveying and mapping in complex environments such as a deep sea hydrothermal region are remarkably improved.

A method for target position estimation based on a distributed reconfigurable intelligent surface (RIS) transmissive array is provide, including: configuring distributed RISs as an RIS transmissive array, wherein the distributed RISs include two RIS transmissive array surfaces, a target source and receiving radio frequency (RF) chains are respectively disposed on two sides of the RIS transmissive array, the receiving RF chains are connected to receiving antennas, and the target source emits a pilot signal into space; transmitting the pilot signal through the RIS transmissive array to the receiving RF chains to obtain a received signal; determining an azimuth angle β and an elevation angle α of the target source relative to each of the two RIS transmissive array surfaces by processing the received signal; and constructing two rays respectively passing through centers of the two RIS transmissive array surfaces based on the azimuth angles β and the elevation angles α of the target source relative to the two RIS transmissive array surfaces, determining a median of a shortest distance between the two rays, and determining a position of the median as a position of the target source.

The invention belongs to the field of cable eccentricity detection, and particularly relates to a cable eccentricity detection method and system based on X rays. The method comprises the following steps: calculating a gradient magnitude diagram and a gradient direction diagram through an X-ray image of a cable, screening a point with the local maximum gradient magnitude and low neighborhood divergence as a contour starting point, performing contour tracking to generate a contour point set, and selecting a next contour point based on a tangential prediction direction; performing ellipse fitting on the contour point set to obtain a geometric center, long and short axis parameters and a root-mean-square error of a fitting ellipse, dividing the fitting ellipse into an inner candidate ellipse and an outer candidate ellipse according to a long axis, and matching the inner candidate ellipse meeting the condition for each outer candidate ellipse; and screening out an outer candidate ellipse and an inner candidate ellipse which meet conditions from the candidate pairs, and calculating the eccentricity of the cable to be measured based on geometric center coordinates of the outer candidate ellipse and the inner candidate ellipse. According to the invention, the accuracy and reliability of cable eccentricity measurement results can be improved.

The invention discloses a nut defect detection method based on X-ray imaging, and particularly relates to the field of X-ray nut material internal defect imaging, which comprises the following steps: generating and adjusting an initial light beam of X-rays through a synchronous radiation source, and constructing uniform irradiation field data for nut material detection; acquiring angle scattering light intensity matrix data based on the uniform irradiation field data; and performing phase inversion and sparse constraint optimization on the angle scattering light intensity matrix data to generate electron cloud density optimization distribution data, and constructing density connectivity relation graph data based on the electron cloud density optimization distribution data. By introducing a detection principle of combining high-coherence X-ray scattering imaging based on a synchronous radiation source and electron cloud density disturbance analysis, lossless identification of an early microcosmic physical deterioration region in a nut is realized; the problem that an existing method based on absorption imaging is insufficient in defect detection capacity under the background of weak density gradient and homogeneous component is solved.

The invention discloses a diversified epiphyseal development map generation method and device based on a potential diffusion model. The method comprises the following steps: making a data set, covering and collecting an original X-ray film image, preprocessing data, labeling a skeleton maturity level and desensitizing the image; an original image is encoded to a potential space by using a variational auto-encoder, and the image detail generation capability is improved by optimizing a loss function; a fuzzy learning module is introduced in the de-noising stage of the diffusion model, and the diversity of generated skeleton features is enhanced through operations such as batch normalization and random disturbance; a progressive alignment strategy is adopted, skeleton basic structure learning is emphasized in the initial stage, a skeleton grade evaluation network is introduced in the later stage, and skeleton detail features are emphasized; optimizing the generation model by using the skeleton recognition model; and inputting development levels, ages and genders of 14 skeletons, and generating a corresponding left-hand skeleton X-ray film image. According to the method, diversified and high-quality skeletal development images are generated through a potential diffusion model framework in combination with VAE optimization, an FLM module and a progressive alignment strategy, skeletal development features are truly reflected, the problems of detail loss and insufficient diversity in the prior art are solved, the method is particularly excellent in performance when sparse data and long-tail distribution are processed, and the method is suitable for large-scale popularization and application. And the authenticity of the generated image and the practicability of medical diagnosis are improved.

The invention discloses a cosmic ray muon scattering imaging method, system and device, and relates to the field of ray imaging, and the method comprises the steps: obtaining the direction information of muons in a to-be-imaged region; calculating the most probable three-dimensional scattering track of the muons in the to-be-imaged region based on Bayesian inference and a particle multiple coulomb scattering distribution function according to the direction information of the muons in the to-be-imaged region; and reconstructing a scattering density image in the to-be-imaged region according to the direction information of the muon in the to-be-imaged region and the most probable three-dimensional scattering track. By calculating the most probable three-dimensional scattering track, the scattering points can be effectively reconstructed, the utilization efficiency of muon events is improved, the scattering points are prevented from being separated from the to-be-imaged area, the most probable three-dimensional scattering track better fits the real scattering track, and then the quality of the scattering density image is improved.

The invention discloses a fire-fighting non-destructive detection method based on multi-modal detection, and relates to the technical field of fire-fighting engineering, and the method comprises the following steps: carrying out the rapid scanning of fire-fighting equipment through an ultrasonic array, analyzing and positioning a suspected defect region based on a time domain reflection signal, and generating a three-dimensional coordinate mapping model; and performing multi-angle X-ray projection acquisition on the positioning area, and generating an internal structure cross-sectional image through a three-dimensional reconstruction algorithm. According to the fire-fighting non-destructive detection method provided by the invention, by integrating various technical means such as ultrasonic array scanning, X-ray tomography, thermal imaging analysis and microwave sealing detection, comprehensive and accurate identification of potential defects of fire-fighting equipment is realized, and the dimensionality and precision of defect detection are improved; the structural integrity of the equipment is kept to the maximum extent, extra risks caused by destructive detection are avoided, and the reliability and accuracy of detection are improved through a data fusion algorithm.

When x-ray fluorescence (“XRF”) spectroscopy is utilized to classify materials transported on a moving conveyor belt, there is the possibility of the x-ray beam only partially irradiating the material piece, which can result in the capture of an inaccurate XRF spectrum needed to classify the material piece. This can lead to an improper (erroneous) classification and resultant sortation of material pieces (e.g., aluminum alloys). In a material handling system, the area of the intersections between the x-ray beam spots from an x-ray fluorescence system and the material pieces are measured and correspondingly used to correct the measured XRF spectrum associated with each material piece. The material pieces can then be sorted according to the corrected XRF spectrum.

The invention belongs to the technical field of nondestructive testing, and discloses a gold ore and iron ore metal content detection method. The method comprises the following steps: acquiring an XRF energy spectrum by using an X-ray tube, acquiring an LIBS spectrum by using double-pulse Nd: YAG laser, and acquiring a Raman spectrum by using a semiconductor laser; performing baseline correction on the LIBS spectrum, performing Savitzky-Golay smoothing pretreatment on the Raman spectrum, and respectively extracting three types of spectrum characteristics; constructing a physical constraint network model, inputting XRF, LIBS and Raman features, and then outputting the correction content, fusion weight and LIBS dominant factors of Fe / Au elements; and finally, through a dynamic weight fusion strategy, combining XRF, LIBS fusion weights and network output parameters, calculating to obtain the content of the gold ore and the content of the iron ore. The technical breakthrough of multi-element cooperative detection and complex matrix interference suppression is realized, and the detection precision of Fe and Au is effectively improved.

The invention discloses a double-domain artifact correction method based on multi-energy-level data and physical prior fusion, and the method achieves the efficient and precise removal of dispersive metal artifacts through the construction of a complete technical scheme of the combination of multi-energy-level data collection, double-domain cooperative correction and physical model constraint. The system has the beneficial effects that the system covers multiple fields of medical treatment, industry, security and protection, aerospace and the like, and has extremely high universality and adaptability. On the basis of a multi-energy-level slow switching scanning protocol, full-angle scanning of at least two energy levels is completed by dynamically adjusting radiation source parameters, and obtained complete multi-energy-level projection data is converted into a high-dimensional tensor through a channel dimension splicing image fusion method. The constructed multi-channel virtual image completely retains attenuation characteristics and structure information of a target object under each energy, provides a more comprehensive input source with discrimination for a deep learning network, builds a data basis for accurate correction in different fields fundamentally, and adapts to various imaging scenes containing metal targets.

Three-dimensional Gaussian splatting mechanisms that initialize a set of 3D Gaussian distributions, un-project pixels from two-dimensional (2D) planes to 3D space by applying queries to the 3D Gaussians at expected un-projected ray depth positions, and splat the 3D Gaussian distributions on the 2D planes based on the expected un-projected ray depth positions.

The invention relates to the technical field of material detection, and discloses a material multi-modal X-ray foreign matter detection method, which comprises the following steps of: acquiring a transmission gray-scale time sequence array and a scattered photon energy spectrum distribution matrix output by a multi-modal X-ray acquisition system, generating a composite characteristic body through cross-modal characteristic interactive coding, constructing a material-contour associated nodal topology network, and obtaining a material-contour associated nodal topology network; simulating a foreign matter feature conduction path and outputting a correlation confidence value set, performing space-time dimension mapping transformation to generate a foreign matter-detection coupling prediction curved surface, calculating a sensitivity adjustment factor and generating a standardized coupling coefficient thermodynamic cloud chart, extracting a detection quantitative index set and dynamically matching the detection quantitative index set with a preset reference range; and when the indexes exceed the boundaries, generating a regulation and control scheme. According to the method, the accuracy, reliability and real-time performance of foreign matter detection are improved, and the detection requirements of complex scenes can be met.

The invention discloses a multi-scattering SAR (Synthetic Aperture Radar) imaging simulation method, system and device based on physical consistency modeling and a medium. The method comprises the following steps: constructing a three-dimensional electromagnetic model of a target; constructing a geometric model under the working mode of the ray tracing auxiliary transmitting-receiving co-located SAR; a high-frequency electromagnetic scattering algorithm based on ray tracing is adopted, multiple scattering paths of a first order, a second order and a higher order are considered, superposition and amplitude modulation are carried out on scattering contributions of all orders, scattering field distribution of a target under the far field condition is calculated, and a far-field scattering field is obtained; performing spatial propagation mapping on the total far-field scattering field, and mapping a far-field scattering result into a radar platform near-field echo signal; correcting the amplitude of the echo signal to obtain a two-dimensional SAR near-field echo matrix; sAR imaging processing is carried out, wherein the SAR imaging processing comprises distance compression, range migration correction (RCMC) and azimuth compression. According to the invention, the authenticity and precision of SAR imaging simulation in a complex target scene are improved.

The invention discloses a surface defect detection device and method based on polarized optical dark field imaging, and belongs to the technical field of industrial defect imaging detection, the device comprises a polarization camera, an imaging lens, a polarization annular LED light source and a sample to be detected which are coaxially arranged; wherein the polarization camera and the imaging lens form a polarization imaging system, the polarization annular LED light source emits linearly polarized light with an inclination angle, and the linearly polarized light is reflected and scattered on a sample to be detected; the linearly polarized light enters the surface of a to-be-detected sample, a small amount of reflection and most of scattered light of the to-be-detected sample are collected by the imaging lens and the polarization camera through a circular hole in the center of the polarization annular LED light source, and finally imaging is achieved. The defect depth can be calculated according to the defect width and the defect inclination angle, so that the defect depth is inversed by the low-frequency linear polarization degree, and the problem that the defect depth information cannot be effectively extracted in the prior art is solved.

The invention relates to the technical field of nuclear radiation imaging, in particular to a radiation array detector, a back scattering imaging system and a back scattering imaging method. The radiation array detector comprises an X-ray photon absorption structure which comprises high-strip-shaped scintillators and low-strip-shaped scintillators which are alternately arranged, the high-strip-shaped scintillators are used for detecting back scattering X-ray photons incident at all angles, the low-strip-shaped scintillators are used for accurately determining the incident direction of the X-ray photons through the collimation effect, and the high-strip-shaped scintillators and the low-strip-shaped scintillators are used for detecting the incident direction of the back scattering X-ray photons; after X-rays enter the photon absorption structure, the high-strip-shaped scintillators and / or the low-strip-shaped scintillators receiving X-ray photons generate scintillation photons. The silicon photomultiplier array is connected with the high strip-shaped scintillator and the low strip-shaped scintillator through a light guide and is used for converting scintillation photons into photoelectric signals; and the position and energy information coding circuit is used for carrying out position and energy information coding on the photoelectric signals generated by the silicon photomultiplier array. According to the invention, the detection efficiency and the direction identification capability of the back scattering X-ray can be effectively improved.

The invention provides a self-adaptive dual-energy pulse X-ray detection system and a detection method. The self-adaptive dual-energy pulse X-ray detection system comprises a dual-energy X-ray emission module, a pulse modulation and high-voltage power supply module, a detector array and a self-adaptive control system, wherein the dual-energy X-ray emission module is in signal connection with the pulse modulation and high-voltage power supply module; the detector array is positioned on the opposite side of a detected object; and the self-adaptive control system is in signal connection with the pulse modulation and high-voltage power supply module. The self-adaptive dual-energy pulse X-ray detection system and the self-adaptive dual-energy pulse X-ray detection method have high penetrating power, and the problems of fuzzy recognition and generation of image blind areas can be effectively avoided.

The invention relates to the technical field of component management, and discloses an electronic component material identification management method, which comprises the following steps: synchronously acquiring a grayscale image, a three-dimensional point cloud and weight data through X-ray imaging, a laser radar and a weight sensor, and fusing and generating a multi-dimensional feature matrix based on a dynamic weight distribution algorithm; extracting physical characteristic parameters corresponding to the electronic components based on the multi-dimensional characteristic matrix, generating unique identification codes through multi-parameter matching in combination with a material physical characteristic database, and outputting material types and specifications; for a high-temperature or strong electromagnetic interference scene, the X-ray detector adjusts gain and sampling frequency in real time, image distortion is corrected through point cloud spatial correlation, and a high-fidelity image is output; the OCR technology automatically identifies label information to realize inventory dynamic updating; and through geometric consistency verification of the high-fidelity image and the multi-dimensional features, if conflicts occur, an early-warning material physical characteristic database is triggered. According to the invention, the precision and adaptability of electronic component identification management can be improved.

An optical detector system providing electrical output signals. The system includes an apertured surface and a transparent window, with a metasurface on the window's opposite side. The metasurface imparts spatially varying phase delay and / or amplitude or polarization modulation to deflect ILR based on their angle of incidence, producing exiting light rays. These rays are detected by a two-dimensional tetralateral position-sensing detector (TLD) with a single resistive photo-absorption layer and four electrodes. The TLD generates X-Y position signals proportional to the azimuth and elevation of the incoming ILR. A computer may be included to calculate these angular values from the position signals.

The invention discloses a power distribution station multi-point environment control system and method, and relates to the technical field of power system automation, and the method comprises the following steps: building a time-frequency phase-locked observation baseline based on the scattering relation between an infrared band and water vapor under the condition of sudden humidity increase, inverting aerosol particle size distribution and concentration distribution, and generating a multi-dimensional scattering phase residual field; according to the multi-dimensional scattering phase residual field, a real radiation field is reconstructed by adopting multi-path reverse ray variation solution and combining polarization dual-angle scanning, and a reflection saturation risk spectrum is generated. According to the method, a real radiation field is reconstructed through multi-dimensional observation and inversion, a consistency graph is generated by fusing multi-source data, a misjudgment boundary is accurately identified, a control margin and a switching condition are constructed based on residual playback and time correction, and a steady-state closed-loop control chain is formed in combination with a dynamic regulation and control mechanism. The self-adaptive precise regulation and control of the power distribution station environment and the stability guarantee of the thermal field are realized.