1666 results about "Blind spot" patented technology

The invention discloses a photovoltaic cleaning robot based on automatic path planning and an operation method thereof, relates to the technical field of robots, and solves the problem of cleaning blind spots in an existing cleaning robot and the endless loop risk of a path planning algorithm. According to the invention, the surface information of the photovoltaic panel is accurately captured through the multispectral acquisition module, a high-resolution and high-definition comprehensive image is generated through the data fusion processing module, then dirt is accurately identified on different scales by using a multi-scale pyramid structure and an adaptive feature fusion mechanism, and an attention mechanism and environmental perception data are combined, so that the real-time performance of the system is improved. The recognition threshold is dynamically adjusted, and misrecognition and missing detection are reduced; and finally, enabling the robot to autonomously learn in a complex environment and optimize an obstacle avoidance strategy through a dynamic obstacle avoidance algorithm based on deep reinforcement learning. The intelligent level and the cleaning effect of an existing photovoltaic cleaning robot are greatly improved.

The invention relates to the field of rail detection, in particular to a rail transit vehicle obstacle detection method based on multi-modal data fusion, which comprises an environmental perception classification step, a credibility calculation and mode decision step, a detection data acquisition step and an obstacle detection step. A full-scene coverage detection system is constructed by integrating a visual image sensor, a millimeter-wave radar sensor and an ultrasonic radar sensor and utilizing complementary advantages of the three sensors in different environments. When a certain sensor is interfered by the environment, other sensors can still provide effective data support, a detection blind area caused by failure of a single mode is avoided, and the robustness and the detection accuracy of obstacle detection are improved.

The invention discloses a constructional engineering quality data management method based on artificial intelligence, particularly relates to the field of constructional engineering quality data management, and is used for solving the problems that multi-source heterogeneous quality data is unbalanced in spatial distribution and correlation analysis, monitoring blind areas and abnormal information are difficult to accurately capture. According to the method, by dynamically integrating multi-dimensional quality data of a construction site, unified processing and associated mining of different monitoring indexes in different areas are achieved, and deviation caused by uneven data distribution and local information loss is eliminated; internal association and potential abnormal fluctuation characteristics are extracted from multi-source data by using a space-time sequence analysis and spatial difference analysis method, so that more accurate and comprehensive basic data are obtained under complex conditions; the abnormal or missing data is finely reconstructed and complemented through a data enhancement technology, so that the monitoring data is kept continuous and complete in the space-time dimension, and the overall quality judgment is not influenced by local blind areas or missing points.

The invention discloses a variation test method and device based on an intelligent automation script. The method comprises the following steps: extracting a statement execution sequence and a branch dependency relationship in an original source code by using a context-aware syntax tree, and generating a control flow diagram and a data flow diagram; performing code semantic analysis on the multi-dimensional code features by using a large language model, identifying potential defect types and variation rules, predicting the test efficiency of variants in combination with a deep Q learning model, and generating to-be-processed variants; performing grammar check and equivalence analysis on the to-be-processed variants to obtain effective variants, and testing the effective variants; variation test indexes are calculated, code weak points are positioned, and test blind areas are identified according to survival variants, so that a visual analysis report is generated; and iteratively generating a new test case according to the current survival variant and the visual analysis report, and testing the current survival variant by using the new test case. According to the invention, the variation test efficiency can be improved.

The invention discloses a PCB mainboard defect detection method and system based on multi-sensor fusion, and the method comprises the steps: synchronously collecting multi-modal data through optical, ray and thermal imaging sensors, and achieving the feature fusion through a multi-branch feature extraction network in combination with a cross-modal attention mechanism (a mutual information algorithm dynamically distributes weights). Defect detection is completed through a feature pyramid network and sub-pixel positioning, and finally sensor parameters, models and production processes are adjusted in a closed-loop mode based on detection results. The problems of detection blind areas of a single sensor, insufficient fixed weight fusion precision, missing detection of small defects and the like are solved, all-directional detection from the surface to the inner layer and from the form to thermal anomaly is realized, the detection precision is improved, the defect occurrence rate is reduced through process linkage, and the method is suitable for a high-density PCB mainboard.

The invention discloses a smart park security and protection system and a control method thereof, and relates to the technical field of smart security and protection, the system comprises the following components: a diversified sensing module which integrates a video monitoring sensor, a face recognition sensor and a behavior analysis sensor and constructs an omnibearing sensing network. According to the technical scheme, all-dimensional and multi-angle monitoring and recognition of the park are achieved, the information acquisition capacity of the security and protection system is remarkably improved through the technological innovation, the comprehensiveness and accuracy of data are ensured, the diversified sensing module integrates multiple sensors of video monitoring, infrared detection, face recognition and behavior analysis, each sensor performs own functions and complements each other, and the monitoring and recognition efficiency is improved. According to the system, blind areas and errors possibly generated by a single sensor are effectively eliminated, the multi-angle sensing network enables the system to more accurately capture abnormal conditions in the park, and a solid foundation is provided for subsequent intelligent analysis and early warning.

The invention relates to the technical field of intelligent monitoring, in particular to a landslide mass displacement monitoring method and related products, and the method comprises the steps: dividing a landslide mass into a plurality of sub-regions; arranging a reference point in each sub-region; acquiring a displacement change characteristic parameter of the reference point, and judging whether the reference point has significant displacement or not; judging the deformation state of the landslide mass; integrating the displacement data of the plurality of reference points, and performing data fusion to obtain a comprehensive evaluation index of the landslide mass displacement; through zoning processing based on a geological structure, topographic features and a sliding direction, the landslide mass can be scientifically and reasonably zoned, pertinence and uniformity of reference point layout are ensured, and monitoring blind areas and unnecessary resource waste are avoided; and the layout positions and the number of the reference points are determined by adopting an optimization algorithm, so that the layout density and the construction maintenance cost are considered, and the economical efficiency and the practicability of the monitoring scheme are improved.

The invention relates to the technical field of defect detection, and discloses a battery FPC defect detection method and system based on laser imaging. The method comprises the following steps: carrying out full-coverage scanning on the FPC surface of the battery, and collecting a first surface laser reflection image and a second surface laser reflection image; performing environment interference compensation processing to obtain a first surface scattering signal image and a second surface scattering signal image; performing multi-scale feature extraction to obtain first feature data and second feature data; analyzing a light intensity change relation of adjacent areas to obtain first surface defect distribution correlation information and second surface defect distribution correlation information; and performing double-angle light intensity change feature classification identification on the first surface defect distribution associated information and the second surface defect distribution associated information, and outputting a comprehensive defect detection result. According to the method, the blind area of single-angle imaging is effectively eliminated, the comprehensiveness and reliability of defect detection are remarkably improved, and high-precision positioning and classification of defects are realized.

The invention discloses a vehicle driving assistance system and method based on AR glasses. The system comprises an environment sensing module, a driver state monitoring module, a danger early warning and avoiding module, an automatic driving and auxiliary driving module, a driving simulation and training module, an abnormal behavior recognition module, a road information and navigation module and a health and emergency response module. The environment perception module is used for solving the problem of poor sight in extreme weather and road perception in a complex environment; by means of sensors such as the laser radar and the camera, road obstacles, pedestrians and vehicles can be accurately recognized, highlighted marking in the AR visual field is achieved, accidents caused by sight blind areas or distraction are reduced, indexes such as the blinking frequency and the head posture of a driver can be monitored, immediate reminding is conducted when fatigue or distraction is found, and parking and resting are forcibly suggested when necessary; and when the driver does not respond in time, the system can automatically brake or adjust the direction to avoid collision.

The invention provides a multi-camera linkage tracking control method and system, and relates to the field of cameras, and the method comprises the steps: determining the prediction probability that a corresponding target object enters a visual blind area of each camera based on the predicted future movement track of each target object; selecting the visual blind area of which the prediction probability exceeds a preset probability threshold value, marking the selected visual blind area as a target visual blind area, determining all cameras of which the view field overlapping area contains the target visual blind area, and constructing a candidate camera set; when at least two cameras in the candidate camera set lock the same shooting area at the same time, selecting the camera with the highest shooting priority as a target camera; and controlling the target camera to adjust the shooting view angle. According to the method, the camera with the highest priority is selected, the subjectivity of randomly distributing the cameras is eliminated, shooting conflicts caused by the fact that the multiple cameras scramble for the same target object in the shooting area at the same time are avoided, it is kept that the spare cameras can track other target objects, and seamless relay of tracking of the multiple cameras is guaranteed.

The invention relates to the technical field of construction automatic pay-off control, in particular to a positioning blind area deviation rectification control system of an automatic pay-off robot. When the paying-off robot encounters an obstacle and causes signal loss, the system records loss and recovery positions, and an obstacle area is determined through a connecting line. The robot keeps moving around an obstacle by a constant distance, and coordinate data are collected in real time and mapped to a digital drawing. The system processes a signal blind area which is completely shielded by an obstacle, ensures that the robot continues to execute accurate paying-off operation in the blind area by correcting paying-off coordinates in a drawing, and corrects errors in the blind area. The system optimizes blind area entry points by using a path optimization algorithm in combination with steering times and path length, and error superposition is reduced. After each time of obstacle avoiding or blind area paying-off, the robot returns to a calibration point, position deviation is adjusted in real time through a Kalman filtering algorithm, errors are corrected, the paying-off precision in blind area operation is improved, and deviation accumulation is reduced.

The invention is suitable for the technical field of 3D scanning, and provides a figure model 3D scanning and dynamic attitude capturing method and system, which dynamically triggers 3D scanning by monitoring joint movement intensity in real time, continuously collects continuous movement tracks by using an inertial sensor in a non-scanning period, and reversely deduces space-time parameters of a shielded part through multi-light-source shadow boundary evolution. And constructing a spatio-temporal joint evaluation function to adaptively adjust the scanning frequency and the sampling rate, and finally performing spatio-temporal alignment and confidence weighted fusion on the discrete point cloud, the continuous trajectory and the reverse reckoning data to generate a dynamic biomechanical model. The scheme breaks through the bottleneck of resource waste and blind area shielding of traditional fixed frequency scanning, realizes space-time complementation and precision optimization of motion monitoring, and is suitable for dynamic scenes such as movie and television animation, medical rehabilitation, physical training and the like. According to the system, through multi-modal data fusion and self-adaptive resource scheduling, the data integrity, the real-time performance and the biomechanical analysis capability are remarkably improved, and an efficient solution is provided for human motion digitization.

The invention relates to the technical field of target identification, in particular to an aerial target identification tracking method and system based on a multi-band electromagnetic fusion sensing technology. According to the invention, through the cooperative work of the infrared high-definition camera, the event type camera and the multispectral camera, the advantages of each sensor are fully played, and the recognition blind spots of a single sensor in a complex environment are effectively overcome, so that the accuracy and reliability of target recognition are greatly improved, and meanwhile, the accuracy and reliability of target recognition are greatly improved by introducing a laser ranging technology. The distance information between the target and the pod can be acquired in real time, so that the system accurately tracks the motion state of the target and timely adjusts the tracking strategy, and the real identity of the target can be deeply discriminated and recognized through the multiband data acquired by the multispectral camera in combination with the spectral characteristics of the known target in the data storage module. In addition, the method has the capability of coping with various environments such as low-illumination and high-dynamic scenes, and is suitable for wide application fields.

The invention discloses an image denoising and fringe removing method based on blind spot regularization, which comprises the following steps of: constructing a double-output blind spot network, respectively taking image reconstruction branch output and fringe estimation branch output of the double-output blind spot network as regularization constraint terms, and carrying out joint modeling on a clear image and fringe components through implicit network prior, random noise is removed by using blind spot regularization; a direction representation shuffling technology meeting J-invariance is introduced, and image and stripe separation is realized through multi-direction feature decomposition and vertical direction feature enhancement; in the model reasoning stage, scale-adjustable feature resampling operation is carried out on the input features, the blind spot receptive field is expanded, and the image reconstruction quality is optimized; and outputting the denoised clear image and the estimated stripe component by alternately optimizing the joint loss function of the dual-output blind spot network. According to the method, the advantages of a traditional denoising method based on a model and a method based on self-supervised learning are combined, and a new thought is provided for denoising and stripe removing tasks.

The invention discloses an assembly type refrigerating machine room energy-saving monitoring system based on data analysis, and relates to the technical field of energy-saving control, and the system comprises an interval module which is used for dividing the space of a machine room into a plurality of monitoring intervals, and is bound with a structural unit; the excitation module is used for applying power disturbance to the structural unit to obtain structural excitation data; the identification module is used for generating a heat influence coefficient matrix according to the excitation data and the temperature change of the adjacent monitoring interval; and the calculation module is used for calculating a blind area temperature and outputting an estimated temperature and a covariance based on the heat influence coefficient matrix, the structure energy consumption data and the adjacent interval temperature. According to the invention, the space of the machine room is subjected to three-dimensional grid division through the interval module, and each monitoring interval is bound with the function nodes in the structure of the machine room, so that the temperature monitoring grid with the corresponding relation between the space and the structure is formed, the space expression precision of monitoring data is improved, and the monitoring efficiency is improved. And a structural support is provided for positioning and temperature compensation of a blind area position.

The invention discloses a blind area monitoring and lane changing auxiliary intelligent safety detection system. The system comprises a data acquisition subsystem, a risk calculation subsystem and a lane changing early warning subsystem. The data acquisition subsystem acquires speeds and collision distances of parallel vehicles in dead zones right behind and on two sides of a vehicle through millimeter-wave radars, the camera module acquires front image information, the data preprocessing module fuses multi-source data, and the environment sensing module adjusts a collision threshold according to illumination, rainfall and the like. And the risk calculation subsystem calculates a collision risk value by using a dynamic threat integral and a space threat potential field model, predicts a lane changing trajectory through an LSTM neural network and optimizes risk pre-judgment in combination with a driver intention. And the lane changing early warning subsystem triggers early warning in a grading manner through a multi-modal output unit based on the collision risk value and the lane changing conflict probability. According to the system, through multi-sensor fusion and an intelligent algorithm, blind area monitoring accuracy, environmental adaptability and driver response efficiency are improved, and lane changing safety is guaranteed.

The invention relates to the technical field of semiconductor manufacturing equipment detection, and discloses a wafer boat defect monitoring method and system based on image process.The wafer boat defect monitoring method based on image processing comprises the steps that a miniature autonomous movement detection robot is constructed, acquiring a diffraction pattern, a multi-focal-plane image sequence, a multispectral image and other multi-modal data on the surface of the wafer boat; image processing is carried out by using a phase recovery algorithm, a focal plane stacking algorithm and a multispectral information fusion technology; planning an optimal observation path based on information entropy calculation and observation value evaluation; performing quantitative characterization and risk assessment on the defects; according to the invention, the resolution limit of traditional optical detection is broken through, the detection capability of nanoscale defects is realized, the detection blind area is eliminated, the three-dimensional form and multi-dimensional feature information of the defects are obtained, and a more accurate technical means is provided for wafer boat quality control.

The invention discloses an edge calculation optimization and compression algorithm based on distributed optical fiber sensing data, and the algorithm achieves the full-section continuous monitoring of an embankment through the layering burying of a distributed optical fiber network along the axis of the embankment, and eliminates the blind area of a conventional point sensor. Strain data and temperature data are analyzed by using Brillouin scattering and Raman scattering, time sequence characteristics are analyzed through an LSTM neural network, and piping and crack hidden dangers are identified. External environment parameters such as water level and rainfall are fused to construct a multiple regression model, and the seepage risk level is evaluated. Through combination of BIM three-dimensional modeling and a seepage simulation algorithm, hidden dangers are accurately positioned, and spatial distribution of the hidden dangers is visualized. Based on edge calculation optimization, a data compression technology is adopted to reduce transmission requirements, a self-repairing material injection and high-pressure plugging scheme is triggered through a grading early warning system, closed-loop management from monitoring to repairing is formed, the real-time performance and accuracy of hidden danger recognition are improved, and efficient and reliable monitoring support is provided for dike engineering.

The invention relates to the field of unmanned aerial vehicle surveying and mapping and building three-dimensional modeling, and discloses a high-rise building three-dimensional modeling surveying and mapping method based on unmanned aerial vehicle laser scanning, and the method comprises the following steps: deploying a laser radar, a millimeter wave radar, a polarized light camera and an intelligent blind area supplement unmanned aerial vehicle for cooperative scanning, and generating an optimal path through topological manifold learning; multi-source point cloud high-precision registration is achieved through geometric algebra and Lie group optimization, multi-sensor heterogeneous data are fused in combination with tensor decomposition, blind area dynamic adjustment paths are detected in real time, and finally a complete three-dimensional building model is generated. According to the method, the modeling precision and data integrity of a complex building structure (such as a glass curtain wall) can be remarkably improved, the problems of scanning blind areas and equipment single-point failure are effectively avoided, and an efficient and robust solution is provided for high-rise building three-dimensional modeling.

The present application provides an array-type image detection device, including two columns of imaging arrays arranged at intervals along a first direction, each column of the imaging array includes a plurality of imaging modules arranged at intervals along a second direction, each imaging module includes a magnifying lens and an imaging chip arranged in sequence along its optical axis; each imaging module magnifies the image to be detected located in its detection area and images it in its imaging area and is acquired by the imaging chip located in its imaging area; and except for the detection areas located at the head and tail sides of the second direction, each detection area has overlapping parts with other detection areas at both ends along the second direction. The array-type image detection device provided by the present application can effectively solve the problems of low resolution of existing contact image sensors, difficulty in detecting high-resolution images, and detection blind spots when performing magnified detection, and can obtain a clear magnified image of the image to be detected within the full-frame range.

The invention provides a vehicle blind area monitoring device and method based on a rear camera. According to at least one implementation mode, the invention provides a vehicle blind area monitoring device based on a rear camera, and the device comprises an image acquisition unit, a control unit and a display unit. The image acquisition unit is configured to acquire a rear image from a rear camera mounted on a vehicle. The control unit is configured to set a region of interest in the rear image based on a steering signal or a lane departure signal of the vehicle, determine whether there is a risk of approaching an object in the region of interest to the vehicle, and generate a blind spot image based on whether there is a risk of approaching. The display unit is configured to display a blind spot image.

The invention discloses an intelligent monitoring method and device for a video monitoring system based on cloud edge collaboration, and relates to a video monitoring technology. An edge computing platform is deployed on one side of the video monitoring system, so that the computing load of a central cloud platform is reduced; through combination of video fault data, historical operation data and real-time operation data, a correlation index between a picture parameter and a video fault type is calculated so as to accurately determine a video picture fault reason. Constructing a panoramic canvas through equipment parameters, and constructing a panoramic background model based on historical operation data; the real-time operation data and the panoramic background model are combined to continuously monitor faults of the holder and the zoom lens, and monitoring blind areas or picture loss caused by faults of the video monitoring system are avoided; the fault evaluation platform receives the detected suspected fault data and suspected fault indexes and feeds back the suspected fault data and suspected fault indexes to the fault alarm system in time, corresponding processing measures are taken, and the reliability of the monitoring camera system can be ensured.

The invention discloses a port internal and external fleet positioning system and method, and relates to the technical field of intelligent positioning. The method is used for solving the problems of frequent occurrence of positioning blind areas, sensitive dynamic interference, uncontrollable error accumulation and low anchor point resource scheduling efficiency in a complex scene of a port. Anchor point coverage is dynamically calculated through metal environment signal attenuation characteristics, blind area information is generated in combination with vehicle trajectory clustering, and signal resource allocation is optimized. Ground texture gray scale entropy segmentation and cross-frame Bayesian correlation are carried out in a blind area, interference is eliminated, synchronous pose data are generated, and visual positioning robustness is improved. An error propagation chain model is constructed based on communication topology among vehicles, accumulated errors are corrected by fusing confidence coefficients of multiple vehicles, and the motorcade positioning consistency is enhanced. And finally, analyzing a port scheduling plan and a vehicle motion state, constructing a space-time prediction model, dynamically generating an anchor point instruction, and realizing on-demand scheduling of anchor point resources. The port vehicle positioning precision and the resource utilization efficiency are remarkably improved, and reliable support is provided for port automatic operation.

The invention relates to an unmanned aerial vehicle illumination control method and system based on three-dimensional model data. The method comprises the steps of obtaining three-dimensional model data, environment perception data and flight positioning data, and analyzing a target scene and an unmanned aerial vehicle illumination demand. And based on the data, planning the flight path of the unmanned aerial vehicle, and generating flight path planning data. And in combination with environmental perception information, performing illumination analysis on the main illumination end, calculating main illumination power density distribution and a main illumination angle, and identifying a main illumination blind area. And for the blind area, light supplement analysis is performed by using environmental perception data, and blind area illumination power density distribution and a blind area illumination angle are calculated. And finally, synthesizing the main illumination data and the blind area light supplementing data to generate illumination control information of the unmanned aerial vehicle. By adopting the method, the illumination strategy can be intelligently adjusted under different environmental conditions, the illumination effect, the illumination precision and the illumination uniformity of the unmanned aerial vehicle can be effectively improved, the energy utilization efficiency is also improved, and unnecessary energy consumption is reduced.

The invention discloses an artificial intelligence denoising method for a speckle shearing interference image based on self-supervised learning. The method is a self-supervised image denoising method based on blind spot learning. According to the method, an improved self-supervised network architecture is adopted, pixel points of an image are input through a random mask and are replaced by neighborhood pixel values, and a blind spot training sample is constructed. The network only learns and predicts an original noise value of a masked pixel point in a training process, and does not depend on a clean image as a supervision signal, so that real self-supervised learning is realized. The de-noising method comprises the steps of constructing a data set meeting training requirements, performing de-noising model training by using a blind spot learning method and a self-supervised network, performing de-noising calculation on a speckle interferogram through a fully trained model, and finally obtaining a de-noised interference image. The method has the characteristics of simplicity and convenience in calculation and high processing speed, and can meet the requirement of large-data-volume image processing.

The invention discloses a tumor image segmentation method and system based on multi-modal image fusion, and the method comprises the steps: firstly extracting the dimension features of CT, MRI and PET, expanding the dimension features into a four-dimensional model, and achieving the initial segmentation of a feature region; performing registration fusion, group collaborative optimization and blind spot compensation to obtain a fused image with high consistency and complete boundary; then texture morphological features are extracted, and suspicious types are intelligently discriminated; dynamically evaluating the reliability of each modal and weighting to generate initial segmentation; and finally, carrying out inclination angle correction three-dimensional reconstruction, carrying out high-sensitivity detection on an invisible boundary, and outputting an accurate target segmentation result. According to the method, the precision and reliability of tumor suspicious region segmentation are remarkably improved, and efficient technical support is provided for subsequent accurate diagnosis and treatment planning of doctors.

The invention discloses a visual guidance positioning system and method in the machine tool flexible shaft assembling process, and relates to the technical field of flexible shaft assembling, a visual system obtains three-dimensional pose data of the tail end of a flexible shaft and a target connector, a six-dimensional force sensor is combined to monitor contact force / torque in real time, the limitation of a single sensor is broken through, and the blind area of the single sensor is solved; visual and force sense coordinate systems are aligned in real time, and coordinate drifting caused by movement of the mechanical arm is eliminated; generating an approximation path based on the pose difference, introducing a compensation amount formula, inhibiting oscillation in combination with an attenuation coefficient, and improving the path smoothness; when the contact force exceeds the dynamic threshold value or the pose difference converges, path correction is automatically triggered, overload damage is avoided, and the assembly damage rate is effectively reduced. The method breaks through the limitation of traditional static path planning, has high precision, high reliability and complex working condition adaptability, and is especially suitable for precise instrument and heavy load machinery assembly scenes.

The invention belongs to the field of road vehicle control systems, and relates to a blind area recognition system and a road vehicle control system and method based on blind area recognition, and the blind area recognition system comprises a laser emission module, a photon-level echo receiving module, an algorithm processing module, a target reconstruction module and a recognition and classification module. The algorithm processing module represents the echo signal as a linear combination of a series of sparse basis functions, reconstructs reflectivity information of the non-vision-field target surface, and extracts geometric information of the non-vision-field target surface; the target reconstruction module is used for performing joint optimization on the non-vision-field target surface information and the echo signal by using a regularization technology, and recovering a sparse coefficient by solving a joint regularization problem to obtain a reconstruction signal; and the identification and classification module is used for judging the type and probability of the non-vision target. According to the system, the hidden target is observed in real time through the non-vision field imaging technology, the physical characteristics of the non-vision field target are combined during signal processing, the recognition accuracy is high, and the safety risk caused by a blind area is reduced.

The utility model discloses an OTDR (Optical Time Domain Reflectometer) optical module capable of eliminating a blind area at an initial section of an optical fiber. The OTDR optical module comprises an upper shell, a lower shell, a frame, a PCBA (Printed Circuit Board Assembly), the optical fiber and an electric connector, wherein the frame is installed between the upper shell and the lower shell, the middle of the frame is open to form a window, and a fiber coiling ring is formed on the top surface of the frame and surrounds the periphery of the window; the PCBA board is installed on the bottom face of the frame, and the electric device, the optical device and the lens unit which are installed on the PCBA board are located in the area of the window. After the optical fiber is butted with the lens unit, a section of optical fiber is wound on the fiber winding ring to form an internal fiber winding and then is led out of the upper shell; the internal coiled fiber is longer than the blind area of the optical fiber initial section; the electric connector is installed on the lower shell, serves as an electric interface and a data interface of the optical module and is electrically connected with the PCBA board. According to the utility model, the internal disc fiber is additionally arranged in the optical module, and the blind area of the initial section of the optical fiber is positioned in the internal disc fiber, so that the problem that a section of optical fiber behind the starting point of the optical fiber cannot be subjected to OTDR detection is solved.

The invention discloses an intelligent and safe hoisting monitoring method for a large-tonnage I-beam, relates to the field of construction safety, is based on a monitoring system consisting of a sensing layer, a transmission layer, a data layer, a decision layer and an execution layer, and comprises the following steps: deploying the sensing layer, constructing the transmission layer, configuring the data layer, constructing the decision layer and responding to the execution layer. Structural stress is monitored through a high-precision strain gauge and a distributed optical fiber sensor, the posture is monitored through an MEMS tilt angle sensor and a six-axis gyroscope, the state of the lifting appliance is monitored through a tension sensor and a visual recognition sensor, the environment is monitored through an ultrasonic anemograph and a laser radar, and multi-dimensional data collection is conducted through a displacement sensor monitoring device. Full-scene monitoring of the state of the I-shaped beam, the performance of a lifting appliance, the working environment and crane equipment is achieved, potential risks are captured without dead corners, the accuracy and reliability of data under complex working conditions are ensured, the problems that traditional monitoring data are prone to being interfered by the environment and insufficient in precision are solved, and the safety coefficient is increased.