493 results about "Spatial mapping" patented technology

The invention relates to the technical field of geological disaster monitoring, and discloses a point-like landslide and debris flow potential degree adaptive monitoring and early warning method. According to the method, a dot matrix monitoring equipment array is arranged, and displacement data and environmental parameters of multiple monitoring points are collected; analyzing the displacement data timestamp through a timeline analysis module, generating a dynamic timeline, and marking geological event nodes; a geographic information system is combined to carry out spatial mapping on displacement data, and environmental parameters are fused to obtain a preliminary potential degree evaluation result. The pattern recognition engine classifies displacement speed and deformation characteristics in the preliminary result to generate a deformation characteristic spectrum with a space-time label; calculating a dynamic potential degree evaluation map according to the multi-factor weighted evaluation model; and adjusting an early warning threshold according to a user instruction, generating a personalized early warning rule set, and scanning the atlas to output a multi-stage early warning response scheme. On the basis of an early warning feedback iteration updating model and an engine, monitoring data are newly added to trigger and re-analyze in real time, a related scheme is updated in a linkage mode, and different scene requirements are met.

The invention discloses a multi-drive cooperative control method for high-precision electric drive assembly equipment, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of insufficient cooperative control precision of a multi-axis system under parameter mismatch and disturbance. By constructing a multi-source data fusion and dynamic coupling analysis mechanism, the system collects the motion state and control parameter data of multiple driving shafts, generates a multi-shaft collaborative dynamic response sequence, and calculates the dynamic coupling degree between the shafts to identify the uncertainty mode of the system; positioning a performance degradation shaft section by combining the temperature field and the vibration signal characteristics, and generating a system stability evaluation coefficient; and establishing a space mapping relation to construct a dynamic compensation factor matrix, and generating a control parameter adjusting quantity meeting a collaborative matching condition through fuzzy rule reasoning. According to the method, control instability caused by traditional global adjustment is avoided, precise cooperative control of the multi-axis system is achieved, and the control precision, operation stability and reliability of equipment under the dynamic working condition are remarkably improved.

The embodiment of the invention provides an unmanned aerial vehicle inspection method and a rail transit line inspection system, and the method comprises the steps: obtaining multi-source inspection data corresponding to an inspection unmanned aerial vehicle, the multi-source inspection data being obtained based on the collection data of a plurality of sensors carried by the inspection unmanned aerial vehicle and based on the time-space consistency fusion; based on the multi-source inspection data, an anomaly detection result is obtained through an anomaly detection model of at least one level, and the anomaly detection result comprises at least one anomaly area; and on the basis of spatial correlation characteristics, mapping an abnormal region in the abnormal detection result to a corresponding actual geographic position to obtain an inspection evaluation result, the spatial correlation characteristics being used for indicating a mapping relationship between the abnormal region and the actual geographic position of the rail transit line. The multi-source fusion improves the detection integrity, the hierarchical model enhances the environmental adaptability, the space mapping improves the positioning accuracy, and the overall inspection effect is optimized.

The invention provides an optical and acoustic image registration method for underwater structure crack detection, and the method comprises the steps: collecting a multi-mode image through the vision field and time synchronization, building a one-to-one correspondence relation according to the pose and time metadata, and improving the image quality through the denoising, distortion correction and other means; optical and acoustic multi-scale features are respectively extracted through a dual-channel network, and cross-modal semantic alignment is realized in combination with a shared weight and a channel attention mechanism; generating a high-resolution scale offset field by using local cross-correlation and micro-upsampling, and introducing manifold regularization constraint to ensure that a vector field is smooth and continuous; according to the method, the local scale field and the affine parameters are combined, non-rigid space mapping is achieved through thin-plate spline interpolation, feedback iterative optimization based on edge structure consistency is assisted, the registration precision of a key structure is enhanced, the automatic registration effect of the underwater multi-modal image is improved, and the method has high robustness and practical application value.

The invention relates to the technical field of computer vision and three-dimensional modeling, and discloses a 3DGS (three-dimensional ground structure) equipment monomer method based on dynamic segmentation of an SAM2 large model, which comprises the following steps of: calling a visual large model to segment a multi-view two-dimensional image to obtain a component contour mask bearing semantic information; the method comprises the following steps of: establishing a mask of a three-dimensional point cloud, establishing space mapping of the mask and the three-dimensional point cloud, executing structure segmentation on the point cloud under double dimensions of fusing geometry and semantics, and further generating a three-dimensional monomer model for each separated equipment component. The segmentation processing of the three-dimensional point cloud is no longer limited to fuzzy geometric inference, but is carried out under clear semantic affiliation, so that the accuracy and robustness of monomer separation in the component dense adjacent region are improved.

The invention relates to the technical field of intelligent decision, and discloses an intelligent decision support method based on reinforcement learning. The method comprises the following steps: acquiring a historical interaction data set containing a decision action sequence, an environment state sequence and a corresponding instant reward signal in a target decision scene; the data set is input into a state feature extraction network for spatial-temporal feature coding, and a state feature vector set with time sequence relevance is generated; constructing a decision action space mapping table based on the set, wherein candidate decision actions and expected accumulated rewards corresponding to the state feature vectors are recorded in the table; dynamically updating the mapping table by adopting a strategy gradient algorithm to generate an optimized strategy gradient parameter set; and constructing a real-time decision support engine according to the parameter set, wherein the engine can respond to the environment state change and output the optimal decision action. The method adapts to a dynamic decision-making scene, and assists in efficiently outputting decision-making results meeting requirements.

The invention discloses an abnormal data processing method and system for a virtual game, and relates to the technical field of big data processing. Receiving an interaction data frame sequence uploaded by the terminal equipment, and analyzing to obtain an input equipment sampling point set and a displacement coordinate vector; performing feature extraction on the sampling point set of the input equipment to obtain a high-dimensional feature vector; mapping the high-dimensional feature vector to a preset behavior feature space, calculating a multi-dimensional statistical distance of the high-dimensional feature vector in the behavior feature space, and generating a first abnormal confidence coefficient; calling space mapping data corresponding to the virtual scene; performing discretization ray stepping detection in the space mapping data based on the displacement coordinate vector to generate a second abnormal confidence coefficient; performing fusion calculation on the first abnormal confidence coefficient and the second abnormal confidence coefficient based on a dynamic weighting algorithm to obtain a comprehensive abnormal score; and judging abnormal data through the comprehensive abnormal score, and executing a state rollback operation for the virtual object.

The invention belongs to the technical field of hidden danger detection, and more specifically relates to a power transmission hidden danger filtering method and system fusing a visual model and spatial logic correction, and a medium. The method comprises the following steps: hidden danger candidate generation based on a high-performance target detection model: adopting a reference segmentation image dynamic multiplexing strategy to complete background semantic analysis of a power transmission channel inspection image, and generating a final reference segmentation image as a semantic mask; performing spatial mapping and logic correction on hidden danger candidate boxes and semantic masks; performing confidence weighting and soft filtering on boundary cases; identifying the boundary cases, constructing a three-dimensional confidence fusion model to calculate fusion confidence, realizing soft filtering according to the fusion confidence, and outputting a high-credibility hidden danger alarm list. The problems that in the prior art, due to the fact that a background semantic base is unstable and total image re-segmentation is frequently triggered, computing resources are wasted, scenes violating common sense cannot be recognized, and the uncertainty of a boundary area is lack of fine-grained description are solved.

The invention relates to the technical field of multi-modal matching, in particular to a multi-modal input instruction and data analysis matching method and system, and the method comprises the following steps: collecting input multi-modal data and instructions, recognizing texts, images and audios, extracting a time sequence, spatial distribution and statistical parameters, and generating a feature distribution result. According to the method, the modal correlation degree is calculated, information entropy measurement is combined, the correlation features are subjected to differentiation processing, the accuracy and consistency of the high correlation features are ensured through space mapping, the potential deviation of the middle correlation features is reduced through alignment adjustment, and the weights of the low correlation features are re-distributed through normalization; according to the method, the overall feature distribution balance and semantic consistency are enhanced, accurate task matching and classification are realized based on the fitting degree score and the time sequence fitting degree, the association accuracy and the time sequence association recognition capability among complex multi-modal data are effectively improved, and the analysis requirement of diversified data input in a complex scene is met.

The invention discloses a conference sound reinforcement system based on an AI intelligent algorithm and 360-degree omni-directional noise reduction, and relates to the technical field of audio signal processing, the conference sound reinforcement system comprises a conference management center, the conference management center is in communication connection with the following modules: a multi-sound-source sensing module used for capturing 360-degree omni-directional sound field information in a conference environment and constructing a sound source space distribution model; according to the invention, the omnidirectional microphone array unit covers all directions of a conference space, synchronously collects audio data streams, eliminates the limitation of a conventional unidirectional microphone, combines the sound source positioning space mapping unit, constructs a sound source space distribution model based on the time difference of arrival and the phase difference through a deep learning model, and improves the sound source positioning accuracy. The azimuth angle, pitch angle and distance parameters of the sound source are accurately analyzed, the position of the sound source is mapped to a virtual space coordinate system, a dynamically updated 3D sound source distribution diagram is generated, the position change and intensity distribution of the sound source are reflected in real time, and the positioning precision in a complex acoustic environment is remarkably improved.

The invention provides a scene reconstruction and automatic correction method using an unmanned aerial vehicle, and belongs to the technical field of scene reconstruction and correction, and the method comprises the steps: carrying out the global data collection of different sub-regions, synchronously recording the flight path information of each round, and carrying out the timestamp alignment processing of sensor data; constructing a dynamic error compensation model, and correcting motion blur, environmental interference and trajectory deviation errors in the collected data by combining a dynamic parameter set of random flight of each round of the unmanned aerial vehicle; integrating multiple rounds of data after correction, establishing spatial mapping of subarea textures and point clouds, and generating an initial three-dimensional model; and constructing a closed-loop correction mechanism based on the GNSS absolute coordinate of the unmanned aerial vehicle and the ground high-precision control point, calculating the deviation of the initial three-dimensional model, and if the deviation exceeds a threshold value, reversely adjusting the random flight coverage density, the flight round and the error compensation coefficient until the deviation reaches the standard. And the precision of scene reconstruction and correction is improved.

The invention discloses a rock mass structural plane parameter calculation method based on a drilling surrounding rock expansion map. The method comprises the steps that a deep learning semantic segmentation network is built to automatically recognize cracks in a drilling surrounding rock hole wall two-dimensional expansion map; processing the segmented fissure image of the pixel level, and eliminating miscellaneous points which interfere with subsequent operation and small fissures which do not influence the integrity of the surrounding rock; extracting a fracture skeleton line, and eliminating burrs on the skeleton line; for fractures with cross structures, adopting a fracture skeleton decoupling algorithm fusing topological connectivity and vector geometry criteria to adaptively separate the fractures; an implicit space mapping method based on a crack pixel sequence is adopted, non-modeling extraction of structural plane space geometric parameters in a borehole expansion diagram is achieved, and the structural plane space geometric parameters comprise the included angle between the structural plane trend and the hole axis and the structural plane dip angle. According to the method, the geometric features of the internal structural surface of the surrounding rock can be quantitatively represented, and the quantitative results are all within the allowable range of actual engineering errors.

The invention discloses a seismic wave velocity model inversion method based on time-space coupling deep learning, and the method achieves a time-space coupling mechanism through a Fourier neural operator framework of a wavelet transform-attention mechanism, and supports the reconstruction of a spatial two-dimensional velocity model from a time sequence track of multiple seismic sources. Firstly, the local time-frequency representation of each seismic time-domain trajectory is extracted by adopting continuous wavelet transform, and then a wavelet time-frequency spectrogram is coded through a convolution feature extractor. Then, information from multiple sources is fused at each receiver point location through a multi-head attention mechanism, enhancing correlation between multiple sources. And finally, projecting the fused features into a two-dimensional Fourier neural operator to efficiently learn space mapping and reconstruct a bottom-layer velocity model based on sound velocity. According to the method, the time sequence characteristic and the spatial distribution characteristic of the seismic channel are fully utilized, the precision and robustness of an inversion result are improved, and a more reliable speed model is provided for geological structure interpretation and acoustic imaging.

The invention relates to the technical field of three-dimensional reconstruction and rendering, and particularly discloses an underwater long-sight-distance adaptive three-dimensional reconstruction and rendering method based on a Gaussian variable-distance field, and the method comprises the steps: carrying out the initialization point cloud generation based on a motion recovery structure algorithm for RGB image data obtained by underwater image collection equipment; points on the initialized point cloud are combined with a three-dimensional Gaussian distribution model, a three-dimensional Gaussian primitive is defined, and parameter initialization about information such as a Gaussian geometric center, a three-dimensional covariance matrix, opacity and color is carried out on the three-dimensional Gaussian primitive; the method comprises the following steps: establishing a Gaussian cross-space transformation model, performing transformation from a Cartesian coordinate space to a homogeneous coordinate space on a three-dimensional Gaussian primitive, establishing a Gaussian full-parameter space mapping model, mapping color information of the three-dimensional Gaussian primitive after cross-space transformation into a spherical harmonic function, and spatial adaptive three-dimensional reconstruction and rendering in an underwater long-sight-distance environment are realized.

The invention provides a robot operation method and system based on a visual language model and reinforcement learning, and relates to the technical field of robot operation, a mechanical arm of a robot operates a target object according to task description, and the robot operation method comprises the steps that to-be-executed task description, a real-time task observation image and a real-time environment observation image are acquired; based on the task description and the environment observation image, decomposing the task description into executable subtask sequences represented by semantic key points by utilizing a visual language model; performing space mapping on the executable subtask sequence; and generating a reference trajectory by taking the mapped coordinates and directions as targets, and finely adjusting the reference trajectory by using the task observation image to obtain a final task operation trajectory. According to the method, robustness is shown in a long sequence task, diversified objects can be flexibly operated, the potential of combination of multi-modal reasoning and self-adaptive control learning is fully excavated, and a new path is developed for constructing an autonomous robot system with a higher capability level.

The invention discloses a surgical navigation point cloud registration system based on global optimization, and the system comprises the steps: reconstructing a target point cloud based on a preoperative medical image, collecting an intraoperative anatomical surface source point cloud, and converting the point cloud to a patient reference coordinate system; the method comprises the following steps of: selecting a registration point pair with anatomical representativeness to carry out initial coarse registration; on the basis of the coarse registration, executing fine registration, including establishing a point pair relationship, calculating weighting based on a distance residual error and a neighborhood structure, filtering out abnormal point pairs by adopting a bidirectional pruning mechanism based on statistical analysis, jointly estimating rotation and translation parameters through a robust optimization algorithm, and iteratively updating to convergence; and finally outputting a rigid transformation matrix for surgical navigation. According to the method, stable and accurate point cloud registration can be realized under the conditions of sparse point cloud, noise and low overlapping rate in an operation, reliable space mapping support is provided for operation navigation under image guidance, and the method has relatively high robustness and clinical adaptability.

The invention discloses a pathological image recognition system and method, and relates to the technical field of image recognition, and the method comprises the steps: continuously adjusting the relative posture between pathological section images through an iterative optimization algorithm until a local deformation parameter combination enabling a target function between two feature point sets to be minimum is found, and positioning a local region with the highest similarity, the method comprises the following steps: selecting a local region, defining local pathological interest regions of upper and lower A rows and left and right B columns by using a central point of the local region, determining a focus matching region and a matching point F of the focus matching region through multiple times of iteration local deformation adjustment in combination with a minimum tolerance threshold, constructing a universal pathological equation and a contact equation of two pathological section images at the matching point F, and realizing multi-region joint recognition. According to the recognition system, accurate matching and multi-region joint recognition of focus regions among a plurality of pathological section images are realized through the technical means of constructing a space mapping matrix, finely matching feature points, fitting local interest regions and three-dimensional form expressions and the like.

The invention discloses a damage detection method and system based on machine vision, and relates to the technical field of machine vision detection. A polarization structured light-thermal imaging three-mode acquisition framework of a customized machine vision array is adopted, the problems of composite noise superposition and unstable internal thermal signal diffusion caused by dynamic heterogeneity of a target surface are solved, the adaptability of damage detection to a complex environment is greatly improved, and by means of a space attention mechanism and a cascade analysis network, the damage detection accuracy is improved. According to the method, geometric measurement and heat conduction simulation technologies are combined, the problems that damage space distribution is rough in description, and there is no stable corresponding relation between surface and deep abnormity are solved, accurate space mapping and multi-dimensional quantification of damage from the surface to the interior are achieved, and through linkage of a dual optimization mechanism and an edge end improved MobileViT lightweight model, the problem that damage space distribution is rough in description is solved. Pain points where real defects and interference artifacts are difficult to distinguish are broken, stable correspondence between surface signals and deep damage is enhanced, and accuracy and practicability of damage detection in an industrial scene are guaranteed.

The invention relates to the field of vulnerability evaluation of extreme rainfall induced mass landslide, in particular to an extreme rainfall mass landslide vulnerability evaluation method fusing diffusion generation and interpretable learning, which comprises the following steps: firstly, constructing a multi-dimensional factor data set, and realizing rasterization preprocessing; secondly, establishing a feature response analysis module, identifying main control factors of landslide occurrence and eliminating redundant variables; then, a denoising diffusion probability model is introduced, and an enhanced balance sample library is constructed; then, carrying out landslide probability prediction based on a multi-algorithm fusion integrated discrimination model, and outputting a grid-level landslide occurrence probability; and finally, minimizing intra-class variance and maximizing inter-class variance by using a natural breakpoint method, determining an optimal grading threshold set, and realizing landslide susceptibility grade division and spatial mapping output. Under the extreme rainfall condition, the method can effectively characterize the susceptibility zoning influence of the mass-occurring landslide flow slip catastrophe process, and achieves high-precision landslide susceptibility evaluation.

The invention relates to the technical field of environment monitoring, and discloses an air pollution intelligent monitoring method and system based on multi-source data fusion, and the method comprises the steps: obtaining original heterogeneous data, carrying out the preprocessing and multi-modal data fusion, and obtaining a fusion data set; according to the fusion data set, spatial feature extraction is carried out, and pollutant spatial feature representation is obtained; according to the spatial feature representation, performing time sequence dynamic analysis by using a recurrent neural network, identifying and marking an abnormal peak value, and obtaining a pollutant time sequence evolution sequence; according to the time sequence evolution sequence, determining the diffusion trend of the pollutants by constructing a graph model, and carrying out conjoint analysis to obtain a pollutant propagation rule; and finally, according to the propagation rule and abnormal peak information in the time sequence evolution sequence, carrying out space mapping processing, and generating a real-time pollution map. According to the invention, accurate and real-time dynamic monitoring of pollutants can be realized, and technical support is provided for formulating a scientific treatment strategy.

The invention discloses a traffic video abnormity management and control method and system based on a three-dimensional live-action map, and relates to the technical field of big data analysis, and the method comprises the steps: building a target road three-dimensional live-action model; based on the traffic monitoring video stream of the target road and the three-dimensional live-action modeling of the target road, generating spatial mapping of the traffic monitoring video stream and the three-dimensional live-action modeling of the target road, and marking abnormal event information of a dynamic object in the target road; performing real-time three-dimensional rendering according to the abnormal event information of the dynamic object in the target road, and analyzing an abnormal risk index of the dynamic object in the target road; and performing three-dimensional live-action modeling visual labeling on the target road based on the abnormal risk index of the dynamic object in the target road, determining an abnormal event type of the dynamic object, and generating a traffic abnormality management and control scheme of the target road. The method has the advantages that the traffic management efficiency is improved, the accident occurrence probability is reduced, and traffic flow management and emergency response are optimized.

The invention discloses an immediate implantation operation method and system based on augmented reality. The method comprises the steps that CT data of the oral cavity and the head of a patient are acquired and subjected to denoising and normalization processing; the method comprises the following steps: constructing oral digital twins of a patient based on CT data, and completing tissue segmentation, key position marking and gravity center calculation; calculating tooth extraction and implantation paths through operation design software, dynamically predicting stress states of the alveolar bone at different implantation angles and depths based on finite element analysis, and generating an implantation planning scheme; accurately registering the digital twin with the oral cavity of the patient by using multi-point identification and an SLAM (Simultaneous Localization and Mapping) algorithm, and establishing a space mapping relation of an implanting tool; a doctor wears AR glasses to carry out surgery operation, a virtual guiding path and implanting depth and angle information are displayed in real time, and a three-dimensional virtual resistance simulation curved surface is overlaid to prompt bone wall resistance changes; tooth extraction and implantation operations are completed according to AR prompts, stable fusion of the implant and a tooth extraction socket or a bone wall is achieved, and data of the whole operation process are recorded for postoperative analysis.

The invention relates to the technical field of digital twinning, in particular to a drilling and workover equipment digital twinning monitoring system which comprises a deposition path reconstruction module, a trend section recognition module, a differential pressure offset positioning module, an oscillation linkage analysis module and a joint control sequence generation module. According to the method, the corresponding relation between a rock debris return path and a drilling depth time sequence is constructed, particle radial deformation and liquid flow channel positioning information are fused, a space mapping map of a deposition track is formed, a continuous evolution section is extracted and a trend distribution set is constructed in combination with the drilling speed amplitude variation trend of a track dense section, and the drilling speed of the track dense section is obtained. The method comprises the following steps: identifying an interlayer abnormal point location based on pressure sequence difference amplitude comparison change, further superimposing a consistency relationship between a drilling tool oscillation amplitude and a drilling speed change direction, screening a linkage feature group with relevance, extracting a key node according to a trigger frequency and an amplitude variation concentration ratio, generating a corresponding operation instruction, and storing the operation instruction. And driving multi-parameter collaborative identification and intelligent generation of a drilling and workover control path.

The invention discloses a VR panoramic flood disaster analysis method and system combining a knowledge graph and a visual large model, belongs to the technical field of disaster information intelligent analysis, and particularly relates to technologies such as virtual reality panoramic images, knowledge graphs and visual large models. The method is suitable for automatic extraction, structured analysis and visual expression of disaster information in a flood scene, and solves the problems that in the prior art, a unified disaster information recognition rule is lacked, accurate extraction and spatial mapping of disaster parameters and scene objects are affected, and automatic recognition and dynamic labeling of flood disaster elements of VR panoramic images cannot be achieved. According to the method, a vertical vision large model of a flood disaster scene constrained by a knowledge graph is constructed and trained; and performing automatic identification and risk labeling on disaster information in the preprocessed VR panoramic image based on the trained vertical vision large model, and performing multi-dimensional visualization after labeling. The method is used for VR panoramic flood disaster analysis.

The invention discloses a simulation image generation method, device and system and a storage medium. The simulation image generation method can comprise the following steps: acquiring a scanning image of a frozen section of the diseased tissue; processing the scanning image by using a pre-trained diffusion model to generate a simulation image of the paraffin section; wherein the diffusion model is embedded into a feature space mapping function to encode clinical feature information of the diseased tissue, and image features related to the scanned image are fused by using a multi-end attention mechanism. According to the method, the diffusion model is used for processing the frozen section image of the lesion tissue, and the realistic restoration of the paraffin section can be obtained, so that the accuracy of intraoperative frozen pathological diagnosis is improved.

The invention relates to the technical field of multi-modal registration, in particular to a neurosurgery visual operation navigation system based on multi-modal fusion, which comprises a feature fusion module, an image correction module, a deformation correction module, a layer labeling module and a navigation interaction module. According to the method, a DTI image and a CT image are aligned through three-dimensional pairing, white matter fiber and temporal bone edge directions are extracted, feature integrity is enhanced, edges are corrected through a structural similarity algorithm, space mapping precision is improved, a blood vessel center line and an ultrasonic image edge point track are combined, deformation is corrected, and drift and artifacts in an operation are restrained; the structure continuity of the blood vessel and the fiber is guaranteed, the positions of the white matter fiber and the blood vessel are integrated, a consistent directional diagram layer is formed, the navigation response sensitivity is improved through three-dimensional distance evaluation, a self-matching adjustment mechanism is set, the feature fusion and correction precision is enhanced, image deviation interference is reduced, and the navigation real-time performance and the analytic performance are improved.

The invention provides a method for predicting and evaluating the service life of a steel structure building, and the method comprises the steps: achieving the real-time collection, preprocessing and spatial mapping of structure and environment data through the deployment of a multi-type sensor network, fusing the structure state and environment variable time sequence characteristics, and mining a key degradation factor through principal component analysis and an attention mechanism; on this basis, an environment driving factor is used as a time-varying parameter to be embedded into a physical basic degradation model, Bayesian reasoning and an online learning mechanism are combined, model parameters are dynamically adjusted and optimized, and confidence interval evaluation of a structure degradation path and residual life is output; according to the method, the accuracy and generalization ability of steel structure degradation mechanism understanding and life prediction can be improved, and the adaptability in a complex environment is enhanced.

The invention relates to the technical field of imaging detection, in particular to a microscopic imaging system for detecting a microlens, which comprises a light source path establishing module, an initial image position acquisition module, a peak coordinate recording module, a stability offset analysis module and an imaging quality judgment module. According to the invention, the numerical aperture normalization processing is carried out on the direction of the light passing through the specific aperture area, so that the precise control of the spatial distribution characteristic of the light beam is realized, the spatial mapping of the focus position is completed on the basis of the light in the unified direction, the direction consistency and the imaging stability in the image forming process are improved, and the imaging quality is improved. By combining continuous peak coordinate search of a light spot focusing position, a light spot change trend can be dynamically obtained, imaging deviation caused by infinitesimal displacement can be effectively distinguished, and an accurate marking mechanism for light spot stability is constructed by means of absolute difference extraction of three-dimensional offset data and Rayleigh criterion tolerance judgment. And the response sensitivity and the analysis reliability of weak instability factors in the imaging performance of the micro lens are improved.

The invention discloses a bridge section temperature field calculation method fusing infrared imaging and finite element analysis, and belongs to the technical field of bridge temperature measurement. According to the method, unmanned aerial vehicle acquisition equipment with an infrared lens is used for performing surface infrared temperature image acquisition on upper, lower, left and right sections of a to-be-measured cross section of a bridge at multiple moments; spatial mapping of temperature information is realized through format conversion and temperature information extraction of the infrared image; on the basis of a finite element analysis platform, the extracted surface temperature data are imported into a bridge cross section numerical model, temperature field distribution solving is carried out through a solid heat transfer and stress analysis function, and accurate reconstruction and temperature effect analysis of a bridge cross section temperature field are achieved. According to the invention, non-contact rapid monitoring and accurate calculation of the bridge temperature field are realized, and reliable technical support is provided for health assessment of a bridge structure.

The invention provides a cowshed drivable area detection method based on fusion of a laser radar and a monocular camera, and the method comprises the steps: synchronously collecting real-time point cloud data and image data, carrying out the matching of the real-time point cloud data and a point cloud map constructed offline, and carrying out the calculation to obtain the pose state of a vehicle in a current map; the method comprises the following steps: inquiring and acquiring key points of a global induction area around a vehicle from a priori map constructed offline, and performing spatial mapping to form an image region of interest; pixel-level classification is carried out through a pre-constructed lightweight semantic segmentation model so as to output and obtain a binary segmentation mask; and mapping the binary segmentation mask from the image pixel coordinate system to the vehicle coordinate system through inverse perspective transformation, and generating a drivable area map under the vehicle coordinate system. According to the invention, through the core thought of priori map guidance, semantic fine recognition and coordinate system unified restoration, the drivable area detection of the cowshed is solved, and a basis is provided for a subsequent path planning module.