617 results about "Spatial registration" patented technology

The invention provides a tunnel crack identification method and system based on multi-source image processing, and relates to the technical field of tunnel engineering, and the method comprises the steps: obtaining multi-source data of a horizontal rock stratum tunnel; performing spatial registration on the multi-source data to generate a multi-modal image under the same reference system; extracting multi-modal features according to the multi-modal image, and constructing a multi-source feature map under the same space grid; performing crack initial detection on the multi-source feature map in combination with multi-scale filtering and structure tensor analysis to obtain a crack candidate region mask; accurate crack identification is carried out through the crack candidate area mask, a crack identification result is obtained by combining bedding direction constraint and a microconditional random field, and the crack identification result comprises a crack segmentation map and a crack category. The method solves the problem that existing crack identification does not consider the bedding characteristics of the horizontal rock stratum tunnel.

The invention discloses a mineral resource intelligent prediction method and system based on multi-source heterogeneous data fusion and deep learning, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and carrying out the standardization processing to form a structured data set; multi-source heterogeneous data fusion: realizing data layer space registration and feature layer weight dynamic allocation through an attention mechanism multi-modal fusion module, and outputting a high-dimensional metallogenic feature vector; constructing a CNN-LSTM mixed deep learning model and completing initialization training, and outputting an initial mineralization probability graph; and establishing a dynamic updating engine, performing model increment training based on transfer learning, correcting the mineralization probability through positive and negative sample reinforcement learning in combination with a newly added data type, and outputting a time sequence dynamic mineralization probability graph. According to the method, mineralization probability dynamic evaluation and risk quantitative updating are realized, the prediction precision and the model updating efficiency are improved, the method is adaptive to a multi-stage exploration scene, and accurate real-time support is provided for exploration decision making.

The invention discloses a double-station robot sorting optimization method and system based on digital twinning and a terminal, double improvement of sorting efficiency and safety is achieved by constructing a digital twinning driven collaborative operation system, and the method comprises the steps that firstly, a laser radar and a polarization camera are used for forming a composite sensing unit; geometric morphology, material reflection characteristics and spatial pose data of a target object are synchronously obtained, and are input into a digital twin engine after time synchronization processing; an engine constructs a virtual sorting scene containing a material attribute database based on a physical rendering technology, sub-millimeter-level space registration is achieved through feature fusion of a binocular stereoscopic vision depth map and geometric parameters, high-precision digital twin stations are generated, a system plans a space-time constraint trajectory of a double-station robot in a virtual environment, and a target object is obtained. And an integrated discrete event simulation engine performs operation time sequence conflict prediction, and when a space overlapping risk is detected, an alternative path containing a dynamic obstacle avoidance strategy is generated through a trajectory re-planning algorithm.

The invention relates to the technical field of three-dimensional imaging, in particular to a four-eye structured light stereoscopic vision imaging method, which comprises the following steps of: arranging four cameras and synchronously acquiring multi-view image data with structured light stripes; carrying out image preprocessing and stripe code identification on the obtained four-view-angle structured light image data, and extracting structured light stripe center line positions and corresponding space projection information under each view angle; generating three-dimensional point cloud data of the high-precision target sample by using parallax calculation and a three-dimensional reconstruction algorithm, and completing spatial registration and filtering optimization of point cloud; fusing the point cloud and the light intensity data by combining the reflection intensity information of the multi-view structured light stripes to generate a composite data set containing space and spectral information; and constructing a dense parallax field and executing three-dimensional consistency verification, and generating a high-fidelity three-dimensional reconstruction model with a complete topological relation and sparse shielding compensation capability. According to the invention, the problems of insufficient view angle coverage, shielding area information loss, difficult edge structure matching and the like of traditional stereoscopic vision imaging can be solved.

The invention relates to the technical field of geological disaster detection, in particular to an unmanned aerial vehicle multi-sensor image fusion slope geological disaster detection system which comprises a data acquisition module, a manifold registration module, a feature fusion module, a weight optimization module, a fusion execution module, a disaster detection module and the like. RGB images, thermal infrared images and laser point cloud data of a slope are collected through an unmanned aerial vehicle, the surface of the slope is modeled as a Riemannian manifold, and high-precision space registration of heterogeneous data is achieved; constructing a feature manifold based on a manifold learning method, and extracting and fusing multi-scale features; evaluating the information amount of different areas by adopting a differential entropy theory, and generating a self-adaptive weight distribution diagram; performing weighted fusion on the registered multi-source data to generate a fused image; geological disaster features such as cracks, abnormal vegetation and water seepage points on the surface of the slope are recognized based on the fused image, and high-precision recognition and early warning of the geological disaster of the slope are achieved.

The invention provides a remote sensing-based land utilization carbon emission evaluation method, device and equipment and a medium, and the method comprises the steps: carrying out the radiometric calibration and spatial registration of obtained multi-source remote sensing data, and obtaining a standardized raster data set; generating a land utilization classification grid map based on the standardized grid data set; generating a land change vector layer through a continuous multi-stage land utilization classification grid map; generating a dynamic evaluation unit vector boundary according to the land utilization classification grid map and the land change vector layer; correcting a vegetation carbon absorption coefficient and an industrial emission coefficient in real time based on remote sensing inversion data, and constructing a spatial carbon transfer model; and inputting the land utilization classification grid map, the dynamic evaluation unit vector boundary and the spatial carbon transfer model into a layered mixed carbon emission evaluation model to obtain a carbon emission intensity distribution map. By adopting the scheme, the carbon emission under different land utilization modes can be accurately evaluated, and the scientificity and policy applicability of regional carbon balance evaluation are improved.

The invention discloses an evaluation and improvement method and device for a tunnel blasting effect and a medium, and relates to the technical field of tunnel engineering.The evaluation and improvement method comprises the steps that a tunnel coupling data set is collected, space registration and three-dimensional shape reconstruction are conducted, fracture space distribution is formed, risk probability mapping is conducted on the fracture space distribution, and a fracture risk thermodynamic diagram is output; the fracture risk thermodynamic diagram and tunnel rock mass data are input into a digital twin blasting model, a load mapping layer carries out load gradient distribution, a rock mass response deduction layer carries out stress wave propagation simulation and damage accumulation quantification, an optimized blasting number instruction set is generated, and a drill jumbo is driven to execute blasting operation and vibration monitoring according to the optimized blasting instruction set. And actual blasting effect parameters are obtained. According to the method, accurate simulation of geological conditions and optimal adjustment of blasting parameters are realized through the digital twin blasting model, and meanwhile, refined and interpretable evaluation of the blasting effect is realized through vibration monitoring and error propagation modeling of the actual blasting effect.

The invention provides a self-adaptive spraying mechanical arm based on multi-mode perception and a control method of the self-adaptive spraying mechanical arm. The method comprises the steps that multi-source perception data of a to-be-repaired area is obtained through a dual-mode vision system and a laser scanning device, a two-dimensional repairing area segmentation map is generated, and space registration and local geometric modeling are achieved in combination with three-dimensional point cloud; on the basis of local geometric features extracted by the model, retrieving matched process parameters from the knowledge graph, generating a digital spraying instruction set containing tracks, postures and dynamic process parameters, and performing simulation verification in a digital twin environment; the mechanical arm state and the virtual model are synchronized in real time in the execution process, the paint film thickness is dynamically monitored, and compensation adjustment is triggered; and after spraying is completed, the coating quality is detected, and a result is fed back to the knowledge graph to update the parameter mapping relation. According to the method, closed-loop control from sensing, decision making, execution, evaluation to learning is achieved, and the spraying uniformity, the self-adaptability and the intelligent level under the complex working condition are improved.

The invention discloses an augmented reality intelligent navigation and operation quantitative evaluation method and system in an operating room, and the method comprises the steps: collecting multispectral image data and depth point cloud data of an operation region in the operating room in real time, and generating a multidimensional perception data set; performing feature extraction and spatial registration on the multi-dimensional perception data set, and decomposing the dynamic three-dimensional augmented reality model into an anatomical structure feature layer and an instrument interaction feature layer; performing dynamic matching and real-time rendering on the anatomical structure feature layer and the instrument interaction feature layer based on a digital twinning technology to generate augmented reality navigation information and an operation guidance scheme; and performing multi-dimensional quantitative analysis on the surgical operation process according to the augmented reality navigation information and the operation guidance scheme, and outputting a quantitative evaluation result. By utilizing the embodiment of the invention, augmented reality navigation guidance which is synchronous with a real operation scene in real time can be provided, meanwhile, accurate quantitative evaluation is carried out on the operation operation, and the accuracy, the safety and the trainability of the operation are improved.

The invention provides a four-dimensional cardiac function imaging method and device based on a biological magnetic signal. The method comprises the following steps: acquiring a magnetocardiogram signal of a to-be-detected object and dynamic structure information of a target trunk part in a full cardiac cycle; wherein the target trunk part at least comprises double lungs, a trunk, a heart cavity of the trunk and a pericardium structure, and the dynamic structure information comprises a contour structure and a multi-frame dynamic image; performing spatial registration on the dynamic structure information and the sensor for acquiring the magnetocardiogram signal to obtain coordinates of the sensor in a coordinate system of the dynamic structure information; according to the dynamic structure information, the coordinate of the sensor and the magnetocardiogram signal, calculating and reconstructing cardiac electrical excitation activity to obtain a calculation and reconstruction result; and converting the calculation reconstruction result into a four-dimensional visual image. According to the method, heart magnetic imaging and heart movement are combined, on the basis of obtained dynamic structure information and heart magnetic signals, the characteristics of biological magnetic signals are fully utilized, and the signal processing technology is integrated through space registration, so that accurate reconstruction of four-dimensional representation of the heart and visual display of functional activities are realized; therefore, the obtained four-dimensional visual image can completely represent the dynamic electrophysiological activities of the whole heart (including epicardium, endocardium and intrawall tissues) in a cardiac cycle, and a technical scheme with different concepts is provided for non-invasive imaging of cardiac electrical excitation activities.

The invention discloses a hydrogen leakage accident holographic perception and disaster situation dynamic prediction system in a tunnel scene, and belongs to the technical field of hydrogen energy traffic safety and intelligent risk management. According to the system, multi-modal information such as environmental parameters, hydrogen concentration, heat source temperature, leakage acoustic characteristics and vehicle states is collected in real time through a multi-modal sensing unit; abnormal detection, time synchronization and space registration are carried out through the information fusion module to generate fusion data in a unified format; the hazard source analysis module locates a leakage source, estimates a leakage rate, locates an ignition source and extracts disaster characteristics based on the fused data; the disaster dynamic prediction module predicts spatio-temporal evolution of diffusion, combustion and explosion by using an agent model and calculates disaster levels; the feedback early warning unit synchronously outputs the prediction information to the tunnel monitoring platform and issues early warning information; and the data storage and self-optimization module uniformly stores historical data and optimizes the prediction model through incremental learning. The system realizes real-time monitoring, rapid evaluation and intelligent disposal of tunnel hydrogen leakage accidents.

The invention provides a dynamic task allocation method for multi-robot collaborative operation, and relates to the technical field of multi-agent reinforcement learning, and the method comprises the steps: a central server carries out the time-space fusion of a real-time order data flow and a dynamic environment perception parameter, and generates a priority-weighted global task coordinate set; the edge computing node receives the global task coordinate set, performs spatial registration with a local real-time grid map, outputs a robot exclusive task subset, and extracts topological relation feature vectors of dynamic entities in a task area; and calculating an instantaneous change rate of relative displacement between the dynamic entities based on the topological relation feature vector, and generating a path correction coefficient matrix of trajectory planning. According to the invention, efficient task allocation and accurate execution in a high dynamic environment are realized.

The invention discloses a multi-mode water-air-land cross-medium cooperative sensing system and method based on an MEMS (Micro Electro Mechanical System). The system comprises an underwater robot, a buoy station, an unmanned aerial vehicle and a ground control station, each node is provided with an MEMS sensor integrating acoustics, optics, magnetism, inertia and other modes, and data transmission and relay are achieved through acoustics, optical communication and radio links. And the ground control station carries out fusion processing, target identification and decision control on multi-source data through time synchronization and space registration, and issues a cooperative instruction to each node to realize a perception-decision-re-perception closed loop. The method comprises five steps of node deployment and calibration, multi-modal data acquisition, hierarchical transmission and sharing, adaptive weighted fusion and closed-loop cooperative control. According to the invention, global coverage, high-precision and multi-mode environment and target perception can be provided in a complex water-air-land environment, and the detection reliability and the communication efficiency are significantly improved.

The invention provides a green vision rate determination method and system based on a vehicle-mounted system, and the method employs multi-source data, improves the sensitivity of a model to vegetation through channel-space double attention and pyramid pooling in a prediction process of a two-dimensional green vision rate, and employs a scheme of combining intermediate loss and model loss. According to the method, convergence is accelerated, edge segmentation precision is improved, two-dimensional segmentation misjudgment is suppressed by using three-dimensional data, robustness is improved, lightweight design is carried out on a decoder, precision and efficiency are balanced, vehicle-mounted edge calculation is adapted, an energy function is used for further refining a segmentation result, and noise is reduced; according to the method, the collected multi-source data are preprocessed, mu s-level time synchronization and millimeter-level space registration can be realized, and the consistency of the multi-source data is ensured; according to the method, the laser radar point cloud is used for predicting the three-dimensional green vision rate, complementary optimization of the two-dimensional image and the three-dimensional point cloud is achieved through the dynamic weight model and semantic verification, and the prediction accuracy is effectively improved.

The invention relates to the technical field of quality detection, in particular to an AI-based liquid crystal glass plate defect detection method and system, comprising multi-modal data acquisition, adaptive preprocessing, dynamic intelligent detection, defect cognition decision, closed-loop feedback execution, a safety guardrail mechanism and digital twin support. Compared with the prior art which adopts a single imaging mode for surface detection and has the defects that the omission ratio of tiny flaws is high and metal and PR defects are difficult to distinguish, the scheme innovatively introduces a multi-mode collaborative acquisition mechanism, and the detection accuracy is improved through millisecond-level synchronization and space registration of four sets of bright field, dark field, ultraviolet and structured light imaging systems. A comprehensive data cube containing textures, metal features, fluorescence response and three-dimensional morphology is constructed, full-dimensional capture and accurate characterization of micron-sized flaws are achieved, and the detection rate and classification accuracy of complex flaws are remarkably improved.

The invention relates to the technical field of sintering evaluation, in particular to a power semiconductor device packaging sintering evaluation method and device based on image analysis. The method comprises the following steps: obtaining a multi-source heterogeneous monitoring image set of a semiconductor device, carrying out multi-modal space corresponding relation identification, carrying out global registration precision compensation, and constructing a multi-modal space registration image matrix; carrying out multi-level voxelization analysis reconstruction based on the multi-modal space registration image matrix, carrying out structure deviation mapping modeling, and constructing a sintering three-dimensional structure deviation distribution map; and performing multi-directional defect detection according to the multi-modal space registration image matrix, performing defect cascade failure modeling, and constructing a multi-defect structure integrity association network. According to the method, accurate three-dimensional sintering structure defect analysis is realized, the quality of the sintering structure is quantified in all directions, and intelligent optimization and rapid iteration of a sintering process are promoted.

The invention relates to the technical field of cabin environment regulation and control, and discloses an immersive intelligent cabin environment regulation and control and energy consumption optimization system. The system multi-source sensing module is used for collecting environment multi-modal data streams through various sensors distributed inside and outside a cabin, and performing time alignment and space registration on the data to generate an environment data set; the dynamic graph generation module is used for performing multi-level feature analysis on the environment data set, extracting environment feature representation, constructing a parameter dependency graph and fusing time sequence dynamic information to generate an environment evolution graph; the core element extraction module is used for deriving an element vector of each dimension from the environment feature representation, calculating an element fluctuation index of each dimension, and screening out a core element sequence in combination with a topological relation in an environment evolution graph; and the intelligent decision-making module is used for generating an optimization control strategy according to the core element sequence and realizing self-adaptive adjustment and energy consumption management of the cabin environment.

The invention relates to the technical field of laser ranging, in particular to an unmanned aerial vehicle dynamic laser ranging method based on multi-source fusion and adaptive compensation, and the method comprises the following steps: obtaining a ranging pulse of a laser ranging module, and carrying out three-axis rotation and coordinate translation in combination with an IMU angle to generate a geographic registration coordinate set; radar and ultrasonic ranging are fused to construct redundant distance compensation data, particle filtering is executed based on IMU acceleration angular velocity to generate an attitude constraint distance, SLAM weight is adjusted according to illumination and meteorological parameters, and an unmanned aerial vehicle dynamic ranging coordinate result set is generated. Spatial registration is executed by combining longitude and latitude height data, by establishing dynamic association between the ranging data and IMU attitude data, smoothness and continuity of the ranging data can be kept when attitude disturbance is severe, the weight proportion of the vision ranging data in multi-source fusion is dynamically adjusted, and the high-precision estimation capacity of the target distance in the dynamic flight state is improved.

The invention discloses a photovoltaic module construction quality identification method based on computer vision and three-dimensional modeling. The method comprises the following steps: S1, collecting an original image sequence of a photovoltaic construction site and carrying out image preprocessing; s2, outputting a semantic tag graph and a two-dimensional space boundary graph through an improved BiSeNet network; s3, using a mask constraint SIFT algorithm to extract cross-view key point features, generating a dense three-dimensional point cloud according to multi-view stereo reconstruction, and outputting a structure three-dimensional geometric model; s4, mapping the semantic tag graph and the two-dimensional space boundary to the structure three-dimensional geometric model to obtain a semantic three-dimensional model; s5, performing spatial registration on the semantic three-dimensional model and the reference model, and calculating spatial error parameters of the photovoltaic module; s6, performing compliance discrimination on the spatial error parameters, and outputting a quality discrimination result; and S7, generating a construction quality evaluation report. According to the invention, automatic identification and accurate evaluation of the photovoltaic construction quality are realized, and the detection efficiency and the discrimination accuracy are improved.

The invention provides a robot environment sensing method, device and system based on multi-sensor fusion. The method disclosed by the embodiment of the invention can comprise the following steps: receiving original observation data of each sensor configured on a robot body, and obtaining equivalent observation data of each sensor under system global time based on a time drift model of each sensor and the original observation data of each sensor, health degree indexes of all the sensors are calculated, time-varying credibility weights of all the sensors are generated according to the health degree indexes, activated sensor subsets are determined according to the health degree indexes of all the sensors, and state vectors of the robot system are determined according to the time-varying credibility weights of all the sensors in the activated sensor subsets and the equivalent observation data; and generating environment representation data based on the state vector of the robot system, the time-varying credibility weight of each sensor and the equivalent observation data. According to the invention, the influence caused by the time synchronization error and the space registration deviation between the sensors can be effectively avoided, and the sensing precision is improved.

The invention discloses a knee bone joint state recognition method and system based on artificial intelligence, and relates to the technical field of image analysis, and the method comprises the steps: obtaining multi-source medical image data of a knee joint region of a patient, carrying out the spatial registration and standardization preprocessing, and constructing a three-dimensional structure model of a knee joint after unifying a coordinate system; establishing a mapping relation between joint movement and an anatomical structure by combining knee vibration and behavior characteristic data collected by a patient in a specific movement state, extracting joint key geometric and mechanical parameters from the three-dimensional structure model, and fusing the joint key geometric and mechanical parameters with synchronously collected vibration behavior characteristics; and performing state recognition on the fused multi-source features based on an intelligent recognition model, and outputting a health state label of the knee joint. According to the invention, more accurate and more comprehensive knee joint health state identification can be realized, the accuracy and reliability of an identification result are effectively improved, and the timeliness of disease discovery and the scientificity of intervention are enhanced.

The invention discloses an emergency rescue real-time human body detection method and device based on time sequence motion feature enhancement, and the method comprises the steps: obtaining visible light and infrared image sequences of a rescue region and environment parameters (including smoke concentration and illumination intensity) in real time, and carrying out the spatial registration preprocessing; respectively carrying out frame difference processing on the two types of image sequences, generating a binary motion mask, calculating an optical flow amplitude, and carrying out adaptive fusion according to smoke concentration to obtain a multi-scale motion energy field; human body micro-motion frequency band energy is extracted through time-frequency transformation, a frequency domain dynamic attention mask is generated, and a saliency motion target area is extracted in combination with a multi-scale motion energy field; constructing a double-branch neural network, extracting time sequence motion features and multi-modal appearance features, dynamically distributing weights and fusing the weights, and outputting a human body bounding box and detection confidence; and calculating environment complexity according to the environment parameters, determining a dynamic confidence threshold, verifying a detection result by combining the average temperature of the human body bounding box region, and generating alarm information if a condition is met. The method aims at solving the problems that a traditional method is high in omission ratio and unstable in recognition in a complex environment.

The invention relates to the technical field of road defect detection, in particular to a road defect detection method and system based on three-dimensional perception, and the method comprises the steps: synchronously collecting two-dimensional image data and three-dimensional point cloud data of a target road surface on a mobile carrier through an integrated detection system; fusing the position information of the GNSS integrated by the detection system and the attitude information of the inertial measurement unit, calculating the time sequence pose data of the detection system in the acquisition process, and generating a target pavement three-dimensional point cloud model with absolute space coordinates; and performing spatial registration on the two-dimensional image data and the three-dimensional point cloud model of the target pavement, and based on the fused two-dimensional and three-dimensional data, identifying and quantifying geometric and texture information of pavement diseases, and generating a disease distribution diagram containing disease positions, types and size parameters. According to the invention, through deep fusion of two-dimensional and three-dimensional data, accurate identification and quantification of pavement diseases are realized, and a CAD-format disease distribution diagram which can be directly delivered can be automatically generated.

The application discloses a rapid batch online detection method and system for micro bearings, and relates to the technical field of bearings.The method comprises the following steps: cooperatively collecting micro bearings to generate a multidimensional detection data set; performing dynamic space registration on key detection areas to locate the key detection areas of the micro bearings; performing defect feature extraction on the key detection areas to generate a defect probability distribution graph, performing dynamic segmentation on the defect probability distribution graph, and generating a defect type label group; performing online sorting on the micro bearings based on the defect type label group to obtain a sorting instruction set, generating a detection quality report, and feeding back the detection quality report to a production control system.The application solves the technical problems of low detection efficiency, insufficient sampling accuracy, and difficulty in real-time online detection and quality control of the micro bearings in the prior art, realizes rapid batch online detection of the micro bearings, and improves the detection efficiency and accuracy of the micro bearings.

The invention provides a BIM-based 3D building digital monitoring method and system, and belongs to the technical field of building engineering monitoring, and the method comprises the steps: carrying out the digital conversion and processing of a building design drawing of a target building, extracting the geometric and attribute information of the target building, and generating a standard BIM model; verifying and calibrating the basic multi-dimensional data and the standard BIM model when the target building is completed, and generating a reference BIM model; performing spatial registration and association mapping on the acquired multi-dimensional monitoring data and the reference BIM model to generate an association data set; comparing based on the associated data set to generate a comparison result; obtaining a potential risk type and a risk evolution trend of the target building through a risk prediction model based on a comparison result, historical monitoring data and a component static attribute in the reference BIM model; and generating a target monitoring strategy based on the potential risk type and the risk evolution trend. The building operation and maintenance efficiency is improved, and the service life of the building is prolonged.

The invention relates to the technical field of smart city and emergency management, in particular to a cross-domain data fusion early warning method for urban inland inundation. The method comprises the following steps: acquiring original multi-source heterogeneous data; performing spatial registration and rasterization processing on the original multi-source heterogeneous data to obtain an urban grid attribute table; constructing an urban bearing bottom plate according to the urban grid attribute table to obtain a multi-dimensional static bearing capacity map; collecting urban real-time state data flow, and performing influence factor superposition analysis on the multi-dimensional static bearing capacity map to obtain a comprehensive influence coefficient distribution map; and extracting a reference drainage capacity value of each grid in the multi-dimensional static bearing capacity map, and performing drainage capacity dynamic correction in combination with the comprehensive influence coefficient distribution map to obtain an instantaneous drainage efficiency matrix. According to the method, the problem that the traditional method cannot dynamically quantify the influence of the urban operation state on the drainage system is solved, and finally accurate tracing and cascade influence early warning of the waterlogging risk are realized.

The invention relates to the technical field of production environment monitoring, in particular to a high-temperature and high-brightness production environment monitoring system based on deep learning, which is characterized in that visible light and thermal infrared images in a high-temperature and high-brightness environment are acquired through cooperation of an industrial camera and an infrared camera, space registration and time synchronization are carried out, and accurate alignment of multi-modal data is ensured. Extracting a highlight overflow mask, a dark part texture guide map and color cast estimation, fusing to generate a joint perception feature tensor, inputting the joint perception feature tensor into a model with a cross-modal attention mechanism and time sequence feature modeling, recognizing a potential distortion region in advance, improving the system robustness, outputting an imaging state evaluation result and an imaging parameter reference value, and improving the imaging quality. And furthermore, a camera adjustment parameter is generated through a strategy network, an image acquisition process is dynamically optimized, a prediction control closed loop is constructed, and adaptive maintenance and optimization of image quality in a complex environment are realized.

The invention discloses an unmanned aerial vehicle dynamic projection map construction method and system based on spatio-temporal data fusion, and belongs to the technical field of unmanned aerial vehicle video monitoring and geographic scene fusion, and the method comprises the steps: obtaining multi-modal flight data in real time based on an unmanned aerial vehicle, and the multi-modal flight data comprise video image data and spatio-temporal reference data; and performing data preprocessing on the multi-modal flight data to obtain route data. And constructing an unmanned aerial vehicle dynamic projection model, and performing space-time synchronous projection rendering on the route data to obtain an unmanned aerial vehicle dynamic projection map. And constructing a dynamic calibration model, and correcting the dynamic projection map of the unmanned aerial vehicle. According to the method, an integrated dynamic geographic information sensing system is constructed through deep cooperation of a spatio-temporal data fusion framework and an intelligent calculation engine, accurate mapping of the geographic space driven by spatio-temporal reference fusion is realized, millimeter-level space registration capability is constructed through multi-modal data intelligent solution and terrain adaptive rendering, and the real-time dynamic geographic information sensing system is constructed. And a technical breakthrough is formed in the dimensions of accurate positioning, data fusion, real-time response and the like.

The invention relates to the field of data processing, and discloses a neurosurgery operation data processing method for precision medical treatment, which is used for solving the problem that mark points cannot be captured due to the fact that a navigation camera is shielded when intraoperative data is collected. The method comprises the following steps: acquiring preoperative image data, intra-operative navigation image data and spatial information of an intra-operative reference mark point, constructing a preoperative three-dimensional navigation model through the preoperative image data, obtaining a navigation coordinate system, obtaining an intra-operative image frame sequence through the intra-operative navigation image data, carrying out spatial registration, extracting intra-operative navigation information, and obtaining an intra-operative image frame sequence. The method comprises the following steps: acquiring intraoperative navigation information, judging whether occlusion occurs in an operation or not according to the intraoperative navigation information, if yes, generating missing image frame data during occlusion through a dynamic trajectory prediction model, and effectively judging the missing image frame data according to a newest instrument space pose, thereby effectively improving data integrity and coherence, improving operation data accuracy and improving operation accuracy. The operation risk is reduced.

The invention discloses a method, device and equipment for generating a head model, a storage medium and a program product. The method is characterized by comprising the following steps of: receiving and verifying original three-dimensional T1 weighted image data and an original three-dimensional grid model; performing tissue segmentation based on the verified three-dimensional T1 weighted image data and the original three-dimensional T1 weighted image data to obtain a cerebral grey matter mask, a cerebral white matter mask, a scalp mask and a skull mask; performing topological structure repair on the brain grey matter mask and the brain white matter mask to obtain a brain mask; respectively converting the scalp mask, the skull mask and the brain mask into a scalp mesh model, a skull mesh model and a brain mesh model; calculating and applying a spatial transformation matrix from the verified three-dimensional grid model to the scalp grid model, and performing spatial registration and fusion on the scalp grid model, the skull grid model and the brain grid model to obtain a head model; the method has the advantages that the efficiency, precision and robustness of head model generation are effectively improved, and the method has good cross-platform deployment capability.