669 results about "Spatial positioning" patented technology

The invention relates to the technical field of building data interaction, in particular to a building engineering interaction method and system based on a BIM model and a medium. The method comprises the following steps: collecting construction site environment parameters and structure response data by using a distributed sensor network; preprocessing the data by an edge computing node; generating standardized environment data and key structure indexes; comparing environment threshold values to identify abnormal events; the method comprises the following steps of: displaying spatial positioning and risk levels through a visual interface, receiving a regulation and control instruction input by a user, dynamically adjusting construction parameters of a BIM model, generating an optimized construction progress scheme, comparing the optimized scheme with an original model to identify a construction conflict area, and generating a conflict resolution report. Through the advanced monitoring technology and construction management technology, the response speed and decision-making efficiency of the building engineering project are improved, and the safety guarantee and resource utilization efficiency in the construction process are improved.

The invention discloses a welded pipe conveying abnormity prediction method and system based on large model reasoning, and aims to solve the problems that multi-source data is difficult to align, cross-station false correlation is caused, prediction lacks executable positioning and time sequence, and linkage control reliability is insufficient. Event alignment is carried out by taking a controller edge signal and an encoder zero position as time anchor points, a production line topology semantic graph containing time delay, capacity and interlocking attributes is constructed, and topology reachability and physical time delay constraints are applied in a self-attention long sequence model to carry out multi-step rolling prediction. And outputting a risk probability, refining the risk probability to spatial positioning of a roller way section or a shaft and the minimum executable intervention time, and generating a risk interval in combination with uncertainty estimation and calibration so as to drive an upstream beat self-adaptive speed reduction, shunting or stopping strategy. The technical effects of improving accuracy and interpretability, reducing false alarm and missing alarm, ensuring that linkage can be executed in advance and meeting edge time delay budget are achieved.

The invention discloses a multi-source image data fusion-based fine decoration air crack seepage quality problem detection method and device, and solves the technical problem of how to carry out comprehensive, high-precision and intelligent detection on the fine decoration surface air crack seepage quality problem. Comprising the following steps: 1) receiving visible light image data, thermal infrared image data and three-dimensional laser point cloud data of a target refined decoration surface acquired from visible light acquisition equipment, thermal infrared imaging equipment and three-dimensional laser scanning equipment respectively; 2) carrying out feature extraction on the visible light image data, the thermal infrared image data and the three-dimensional laser point cloud data; 3) obtaining point cloud projection coordinates, and then performing association fusion on the first two-dimensional feature and the second two-dimensional feature with corresponding three-dimensional features to generate fusion point cloud data containing multi-source features; and 4) based on the fused point cloud data, carrying out defect classification identification and spatial positioning to obtain a detection result. And comprehensive and high-precision detection of the quality problem of air crack seepage of the finely-decorated surface is realized.

The invention belongs to the technical field of augmented reality (XR) and computer vision crossing, relates to fusion application in immersive digital performance, and provides a virtual actor reconstruction and immersive presentation system based on 4D Gaussian splash modeling and XR space positioning. The system comprises a set of spherical multi-camera-position high-synchronization camera shooting matrix used for capturing dynamic images of actors; performing dynamic modeling on the multi-angle image through a 4D Gaussian splashing technology, and outputting a virtual actor point cloud model which can be used by XR equipment; vPS visual positioning and an SLAM tracking module are combined, and precise mapping positioning of a performance space is achieved in AR / MR equipment. The method supports the immersive watching of the actor image at the audience end at a 360-degree free visual angle, and realizes the natural presentation of the virtual actor without dead angles and wearing in cooperation with shielding judgment and a real-time rendering engine. The system is widely applicable to on-site entertainment scenes such as immersive theaters, text travel performances, brand activities, concerts and television programs.

The invention belongs to the technical field of geophysical monitoring, and relates to an electromagnetic interference detection method for earthquake monitoring, which comprises the following steps: deploying a multi-dimensional electromagnetic field sensing unit and an earthquake signal acquisition unit and realizing high-precision synchronization; synchronously acquiring multi-dimensional electromagnetic field and seismic physical signal data; performing multi-feature and noise feature extraction on the two; inputting the features into a pre-trained machine learning / deep learning model for identification, and outputting information such as interference type, intensity and azimuth angle; and carrying out influence evaluation and quality marking on the seismic data according to the output, and selectively inhibiting or compensating. Through the above scheme, multi-dimensional perception and characterization, high-precision synchronization and correlation analysis, intelligent identification and spatial positioning are realized, and the reliability and accuracy of earthquake monitoring data are significantly improved.

The invention belongs to the technical field of pipe network monitoring control, and particularly relates to an urban pipe network leakage detection method and system.The urban pipe network leakage detection method includes the steps that firstly, data are collected at key nodes and pipe sections of a pipe network through a data collection module, and a four-dimensional original data set with spatial positioning attributes is formed; the preprocessing module adopts an improved Kalman filtering algorithm containing a pipe network material attenuation coefficient to reduce noise, and outputs high-quality data; the space-time fusion module is combined with GIS pipe network topological data, the topological weight is calculated through a node degree centrality algorithm, and a time attenuation mechanism and adjacent node features are fused to generate a comprehensive feature matrix; the dynamic judgment module judges suspected leakage based on a multi-parameter weighting model and a sliding window dynamic threshold value; the positioning engine module positions a leakage point through a two-factor algorithm of signal time difference and improved hydraulic model residual error; and finally, the linkage control module starts secondary acoustic verification, valve control, early warning and maintenance scheduling instructions are automatically generated after confirmation, and closed-loop management from detection to disposal is achieved.

The invention relates to the technical field of robot control, and discloses an industrial robot adaptive control method and system based on multi-modal sensor fusion, and the method comprises the steps: collecting multi-modal original data, and carrying out the time-space alignment; capturing space-time semantic association of visual textures, tactile pressure distribution and force sense fluctuation in the multi-modal data through a multi-head attention mechanism guided by a physical model, and performing space-time registration; a CNN-LSTM hybrid model is adopted to extract visual texture features and time sequence tactile features in the physical information enhanced multi-modal feature matrix; and carrying out dynamic weight distribution on the fusion feature vectors with physical consistency by utilizing a weight distribution model driven by element reinforcement learning to generate dynamic weighted fusion features. According to the method, the spatial positioning precision of the industrial robot in a precise assembly scene is greatly improved, the contact force control stability is greatly improved, and the control robustness in a complex operation scene is remarkably enhanced.

The invention relates to an implicit voxel Boolean subtraction optimization method in five-axis machining simulation. According to the method, an implicit tool geometric expression model based on a symbolic distance function is constructed, and a dynamic voxel modeling technology with a layered structure is combined, so that efficient interactive modeling of a material removal process between a tool and a workpiece is realized. The whole process of the method comprises four key stages of spatial positioning, vertex interference detection, projection correction and grid reconstruction, and can cope with geometric modeling challenges caused by continuous motion of a multi-degree-of-freedom and multi-attitude cutter. A dynamic block loading mechanism and a local state synchronization strategy are introduced, so that the real-time response capability of Boolean subtraction operation under complex working conditions is effectively improved, and meanwhile, the continuity and integrity of machining geometric boundaries are kept. The method has good expandability and robustness, obviously reduces space-time complexity of calculation on the basis of keeping geometric surface continuity and topological correctness, realizes millisecond-level response time in an experiment, and has a good application prospect. The method is widely applied to virtual machining simulation and intelligent process verification systems of complex free-form surface parts such as aero-engine blades and automobile structural parts.

The invention relates to the technical field of auxiliary training, and discloses an AR auxiliary training system for improving the diagnosis and treatment capability of general practitioners on undifferentiated diseases, and an immersive training environment is constructed by adopting an augmented reality technology. The system comprises an environment sensing module, a space positioning module, a case data module, a virtual rendering module, an interactive acquisition module, an intelligent response module, a state control module and an evaluation calculation module. The environment sensing module adopts an RGB-D camera and an inertial measurement unit to collect environment and head movement data of a user; the spatial positioning module determines a virtual patient placement position based on an SLAM algorithm; the virtual rendering module renders a three-dimensional virtual patient in a real environment; the interaction acquisition module supports voice and gesture multi-mode interaction; the intelligent response module generates an intelligent patient answer; the evaluation calculation module carries out comprehensive evaluation from three dimensions. The invention provides a highly immersive and intelligent training environment, and has the advantages of low cost, reusability and high standardization degree.

The invention relates to the technical field of data processing, in particular to an engineering geological survey data processing method and system, and the method comprises the following steps: obtaining hole site space positioning and lithologic boundary information, collecting a structure segment sequence, extracting resistance change direction classification disturbance behaviors, recognizing node sequence conflicts, and judging hydrological turning positions. Identifying the corresponding relation between the structural section and the water level track, and generating a structural section water level merging mapping table. According to the method, the spatial information, the lithology identification, the sampling level and the penetration resistance of the drilling structure sections are collected and serialized, disturbance identification and non-sampling section classification are performed based on the resistance change trend between the sections, and node conflicts are identified in combination with the numbering relation and the lithology combination; continuous modeling and classified merging of geological information on the spatial level are achieved, the linkage adaptability of data in a multi-dimensional analysis scene is enhanced, misjudgment interference caused by information isolation is avoided, and dynamic recognition and hydrological response analysis of a geological structure are supported.

The invention provides a time-space evolution data processing method and system applied to tunnel deformation analysis, and the method comprises the steps: obtaining a time sequence monitoring sequence of different monitoring dimensions in a tunnel construction process, constructing a time-space evolution correlation model of tunnel deformation based on the time sequence monitoring sequence, and analyzing a dynamic coupling relation between structure response data and geological environment action data, generating a space-time correlation feature matrix, generating a feature evolution sequence of tunnel deformation based on the space-time correlation feature matrix, and performing nonlinear mutation feature extraction on the feature evolution sequence to obtain a mutation feature index set; according to the sudden change characteristic index set and a preset accumulated deformation rate threshold value, performing dynamic division of tunnel deformation safety levels, generating a safety level division result, and based on the safety level division result and the spatial distribution data of the tunnel construction section, executing spatial positioning and boundary contour extraction of the potential risk damage area. And obtaining a risk damage area distribution map. According to the invention, the tunnel construction safety risk can be accurately evaluated and effectively controlled.

The invention relates to the technical field of monitoring systems, and discloses a logistics storage monitoring system based on artificial intelligence, and the system comprises a data collection module which is composed of an environment sensor array, a medicine batch identification unit and a space positioning device which are disposed on a storage three-dimensional shelf, the environment sensor array collects temperature, humidity and vibration parameters in real time, and the medicine batch identification unit stores unique codes and batch attribute data of medicines. According to the method, the dynamic association model of the medicine batch-environment sensor is established, and the spatial interpolation algorithm and the three-dimensional coordinate mapping technology are combined, so that the accurate association of the abnormal event and the medicine batch is realized. When an environment abnormity occurs in a certain area, the system can quickly position the batch of the affected medicine and generate the tracing map, and compared with a scheme in which environment data is disjointed from the position of the medicine in the prior art, the problems that the abnormity tracing efficiency is low and the high-value medicine is easy to deteriorate are solved.

The invention provides a rock and soil construction quality monitoring and diagnosing method, which comprises the following steps of: modeling historical engineering events and expert rules, constructing an event causal atlas prototype containing weights, and realizing event sequence feature extraction and real-time causal atlas dynamic updating in combination with on-site multi-modal sensing data and construction logs; neural symbol reasoning and tensor completion technologies are adopted to predict a novel causal relationship, and a causal atlas structure is perfected through space, time and logic consistency verification; a multi-layer risk early warning mechanism is set, a causal map local risk assessment and event chain propagation are combined, spatial positioning early warning signals are generated in a grading manner, closed-loop backtracking optimization is supported, causal reasoning accuracy and early warning efficiency are improved, construction process risk identification and dynamic early warning can be realized, and the engineering safety management level is improved.

The invention relates to a gesture recognition and projection fusion non-contact interaction method and device, equipment and a medium. The method comprises the steps of obtaining multi-user gesture original image data, extracting geometric features and boundary features of a hand contour after image preprocessing and noise removal, and generating recognition result data; secondly, on the basis of three-dimensional coordinate information in the recognition result, a tracking trajectory is analyzed through trajectory continuity, a user identity identifier is matched in combination with a user identity feature library, and user space positioning information is generated; then, a three-dimensional track point sequence is extracted from the user space positioning information, an initial projection area is divided by adopting a dynamic area segmentation algorithm, and an operation area mapping relation is formed; and finally, analyzing the operation area mapping relation to extract gesture action features, generating a control instruction set in combination with a preset rule, and scheduling a conflict instruction to generate an instruction execution sequence. By adopting the method, the scene limitation of traditional contact type interaction can be broken through, and the stability and practicability of non-contact interaction in a multi-user environment are improved.

The invention relates to an X-ray weld defect intelligent detection method based on weld line perception and reversible domain mapping, which comprises the following steps: collecting a gray X-ray image of a weld region, and carrying out joint labeling on a weld center line track and a defect mask to construct a standardized training data set; designing a weld line sensing hybrid neural network architecture; performing end-to-end training on the network by using a joint loss function formed by detection task loss and center line consistency constraint to realize collaborative optimization of defect category discrimination and spatial positioning; and deploying the trained model in an automatic welding quality detection system, performing forward reasoning on X-ray images acquired in real time, and continuously outputting defect detection results with accurate category, position and form information in combination with a post-processing strategy of weld direction clustering. According to the invention, stable and accurate identification of slender and weak-texture weld defects under the condition of bent welds is realized, and automation and reliability of nondestructive detection of welds are remarkably improved.

The invention provides a three-dimensional target detection method and device based on radar mixing points and image fusion. The method provided by the invention comprises the steps of obtaining radar point cloud data and image data collected under the same timestamp; constructing a mixed point cloud based on the radar point cloud data, and extracting radar aerial view features based on the mixed point cloud; determining image aerial view features based on the radar point cloud data and the image data; and fusing the radar aerial view features and the image aerial view features based on a channel attention mechanism and a space attention mechanism to obtain comprehensive features, and performing three-dimensional target detection based on the comprehensive features. The method and the device provided by the invention are used for solving the problems of radar sparsity, insufficient image fusion, inaccurate spatial positioning and the like in the existing method, and the overall performance of three-dimensional target detection is remarkably enhanced.

The invention discloses an electric power system multi-source forced oscillation positioning method based on a graph attention network and variational mode decomposition. The method comprises the following steps: acquiring power grid topology data of a target electric power system and attribute data of each node; inputting the power grid topology data and the attribute data of each node into a pre-trained target network model, wherein the topology perception space decoupling network processes the power grid topology data and the attribute data of each node and outputs the energy contribution degree of each node; the frequency domain time decoupling network processes the attribute data of each node and the corresponding energy contribution degree and outputs a synchronous machine component and a fan component; and the physical constraint oscillation classification network processes the synchronous machine component and the fan component and outputs a multi-source forced oscillation positioning result. According to the invention, the modeling of the electrical connection relation between the power grid nodes is realized through the topology perception space decoupling network, and the decoupling of the synchronous machine and the fan component is realized through the frequency domain time decoupling network, so that the precision of oscillation source space positioning is improved.

The invention discloses a farmland soil moisture content real-time surveying system and method based on unmanned aerial vehicle multispectral imaging. The method comprises the steps that multispectral sensor parameters are configured, and an unmanned aerial vehicle flight scanning path is planned; a space coordinate system and time synchronization mechanism is established, and high-precision positioning and time synchronization are achieved; collecting and calibrating visible light, near-infrared and short-wave infrared original image data to form a spectral signal set; calculating an image signal-to-noise ratio, screening a low-quality image, and executing noise suppression; extracting timestamp information from the suppressed image, performing spatial positioning, and establishing a spatial corresponding relation database; calculating time synchronization precision and performing image correction; evaluating the spatial resolution according to the distortion coefficient; calculating a comprehensive quality parameter, comparing the comprehensive quality parameter with a threshold value, and obtaining a soil moisture content distribution parameter when a condition is met; a farmland moisture state map is generated, partition marking is carried out according to a multi-level standard, a monitoring result is output, and high-precision and real-time monitoring of the farmland soil moisture content is achieved.

The invention relates to the technical field of underwater positioning, in particular to an AUV (Autonomous Underwater Vehicle) three-dimensional pose joint estimation method and system based on multi-modal layering, and the method comprises the steps: distributing a historical observation sequence to corresponding independent convolutional neural network branches according to modals, carrying out the local time sequence feature extraction through each branch, and carrying out the local time sequence feature extraction; outputting local time sequence characteristics of each mode; performing hierarchical feature fusion based on local time sequence features of each mode, inputting global fusion features into a double-branch regression head, respectively decoding through a position regression branch and an attitude regression branch, and outputting a three-dimensional position increment and an attitude increment; and constructing a loss function by using the three-dimensional position increment and the attitude increment, training to obtain an optimal model, and deploying the optimal model to an AUV platform, thereby breaking through the limitation that the traditional method only focuses on a two-dimensional plane or separately estimates the attitude, completely covering the full-space positioning demand of the AUV three-dimensional maneuvering task, and improving the integrity and consistency of the attitude estimation.

The invention provides a GIS insulation defect evaluation method and device and electronic equipment, and relates to the field of data processing. According to the method, a cross-modal synchronous data stream formed by an ultrahigh frequency sensor and a mechanical vibration sensor on a GIS (Geographic Information System) is obtained, event slicing is firstly carried out, multi-dimensional features are extracted, and candidate events are screened in combination with a sparse labeling library; and generating a propagation time delay window and a path priority table by using a three-dimensional geometric model, completing cross-modal pairing and time registration, outputting a registration result, performing array manifold sparse reconstruction separation, and eliminating multipath interference to obtain purification components. And constructing a cross-modal event chain graph based on the purification components, executing spatial positioning and physical quantity inversion under the constraint of the chain graph, and finally generating a defect evaluation report in combination with a historical labeling result, thereby realizing high-precision detection and evaluation of GIS insulation defects. By implementing the technical scheme provided by the invention, the accuracy of insulation defect evaluation can be improved.

The invention relates to the technical field of radar testing, in particular to a radar testing system and method based on multi-mode dynamic reconstruction. The system comprises a multi-mode radio frequency link dynamic reconstruction module, a baseband signal processing module, a space positioning module, an intelligent diagnosis and visualization module and a closed-loop crosslinking control module. According to the invention, integration of three working modes of target simulation, power measurement and T / R assembly test is realized through multi-mode radio frequency link dynamic reconstruction, automatic identification and diagnosis of T / R assembly faults are realized through the intelligent diagnosis and visualization module, and the test precision is ensured through high-precision space positioning. Seamless switching among the modes is realized through closed-loop cross-linking control, and the efficiency and precision of radar testing are remarkably improved.

The invention relates to the technical field of intelligent management, in particular to an intelligent management system and a resource cooperative scheduling method for an entrepreneurship space, and the system comprises a dynamic resource topological graph module which is used for collecting resource distribution data in real time through a space IoT positioning system, and generating a dynamic resource topological graph; when the system is used, a resource distribution diagram is drawn in real time through a space IoT positioning system, three-dimensional coordinates and coupling weight are marked for each resource node, and the node state is dynamically updated, so that the coupling relationship between the space path cost and the resources is fully considered; according to the method, the situation of cross-region movement of members is greatly reduced, the resource cooperative utilization rate and the resource average turnover rate are improved, conflict propagation prediction and resource redistribution are carried out through establishment of a resource dependency relationship matrix and dynamic time window splitting operation, delay caused by conflicts is shortened, the resource vacancy rate is reduced, and the emergency response speed is increased.

The invention discloses a tunnel process realistic management system based on real-time data acquisition and intelligent analysis, and belongs to the technical field of tunnel engineering construction management. Comprising a hardware layer, a data layer and an application layer, all the layers cooperatively achieve real-time realistic management of the tunnel process, and the hardware layer is used for collecting data of the whole process and comprises the steps that an Internet of Things terminal with a GPS and a sensor interface is installed on tunnel construction equipment, and equipment start-stop time, working parameters and position information are collected; the data layer is used for data real-time transmission and structured storage; and the application layer realizes a realistic management function and comprises a real-time realistic panel, an intelligent report generation module, a tracing query function module and a permission and collaborative management module. According to the invention, multi-terminal cooperative acquisition of "UWB positioning card + equipment + material" is adopted, and through accurate binding of space positioning and time nodes, process data is upgraded from "manual recording" to "space-time linkage realistic", and the problem that traditional equipment is difficult to accurately associate personnel and processes is solved.

The invention provides a construction project quality detecting and monitoring method and system, and relates to the technical field of data processing.The method comprises the steps that spatial positioning sensors are deployed at steel structure beam column welding joints, a concrete core shear wall root pouring area and a curtain wall top cantilever connecting end; welding acoustic emission frequency domain signals of beam-column joints, concrete vibration energy distribution waveforms of the root of the shear wall and dynamic load stress spectrums of the cantilever connecting ends are collected respectively, and a spatial heterogeneous original data set is generated; acquiring three-dimensional coordinates of positioning sensors on a steel structure beam column welding node, a concrete core shear wall root pouring area and a curtain wall top cantilever connecting end so as to construct a dynamic space reference frame, and calculating a deformation feature vector of the dynamic space reference frame; the deformation feature vector comprises a reference frame area change gradient value, a frame side vector fluctuation coefficient and a space included angle offset entropy value. According to the invention, the real-time performance of construction project quality monitoring is improved.

The invention discloses a dam leakage detection method and system based on an unmanned aerial vehicle and deep learning. The method comprises the following steps: acquiring dam surface image data through the unmanned aerial vehicle carrying image acquisition equipment; preprocessing the obtained dam surface image data; performing feature extraction and fusion on the preprocessed image data based on a deep learning model to obtain a multi-modal coupling feature image related to the leakage area; performing target detection on the multi-modal coupling feature image to identify a leakage area; and carrying out spatial positioning on the identified leakage area to determine a positioning coordinate of the leakage area. Dam leakage is detected by combining the unmanned aerial vehicle technology and the deep learning algorithm, the automation and intelligence level of detection is improved, manual intervention and errors are reduced, and the detection precision and efficiency are improved.

The invention relates to the technical field of ship underwater positioning, in particular to a dynamic space positioning method and system for a sensor chain body of a ship drag chain, and the method comprises the steps: dispersing the drag chain into a plurality of hinged rigid sections in a two-dimensional inertial reference system, connecting the sensor chain body to the tail end, and taking the motion of a ship as a traction point boundary; according to the given traction speed, the initial pitch angle, depth and tension distribution are obtained through static balance; calculating local speeds in the sections according to translation and rotation states of upstream nodes of all the sections, obtaining distributed hydrodynamic loads in combination with fluid parameters, establishing moment balance equations at all the upstream nodes, and forming a second-order ordinary differential equation set with a pitch angle as an unknown quantity; and numerical solution is carried out under a unified time step length, and the mass center positions of each node and a sensor chain body are obtained according to geometric recursion, so that dynamic positioning in an inertial reference system is realized. According to the invention, the precision and reliability of underwater target motion analysis and target tracking can be improved.

The invention relates to the technical field of image processing, and discloses a house quality detection method and system based on a live-action three-dimensional model. The method comprises the following steps: converting an OSGB format live-action three-dimensional building model into a body coordinate system and carrying out quality evaluation, carrying out semantic segmentation on a standardized model to obtain a component geographic entity set, establishing an independent local coordinate system of each component to form a single three-dimensional model set, generating a multi-view projection feature image set based on a normal vector distribution planning view angle, and carrying out multi-view projection on the single three-dimensional model set. And detecting defects by adopting a 3D-YOLO-BuildingDefect algorithm, and carrying out inverse transformation to obtain three-dimensional space defect distribution and a quality grade evaluation result. The technical problems that a traditional two-dimensional detection method is insufficient in spatial positioning precision, incomplete in detection coverage rate and insufficient in geometric information utilization are solved, and the spatial positioning accuracy of house quality detection and the comprehensiveness of defect recognition are improved.

A power transmission tower grounding resistance non-contact measurement method based on the electromagnetic coupling principle comprises the following steps that transmitting and receiving electromagnetic coupling coils are arranged around an iron tower grounding body, and geometric calibration and spatial positioning of a measurement area are completed by combining grounding grid structure parameters and soil conduction characteristics; injecting a high-frequency alternating-current excitation signal into the transmitting coil, and synchronously acquiring the amplitude and phase response of the induced voltage at a receiving coil end; carrying out filtering processing on the induction signal by adopting a time-frequency analysis and phase decoupling algorithm, and solving a function relationship between electromagnetic response and grounding impedance by combining a coupling equivalent model; based on experimental calibration data and scene parameters, a nonlinear mapping model of induction response and grounding resistance is constructed, and real-time inversion calculation is carried out by combining a solving result obtained in the third step. According to the invention, the real-time accurate measurement of the grounding resistance under the conditions of no power failure and no wire breakage is realized, and the safety, the operation convenience and the anti-interference capability in a complex environment in the measurement process are obviously improved.

The invention relates to the technical field of focus labeling, in particular to a medical image focus labeling method and system based on deep learning, and the method comprises the steps: collecting a multi-modal medical image, and carrying out the noise reduction and contrast enhancement preprocessing to obtain a medical enhanced image; segmenting a focus area through a deep convolutional network and generating a focus labeling mask image; and performing post-processing optimization on the mask image, inputting an optimization result into the sub-pixel level annotation generation model, and outputting a high-precision focus annotation coordinate sequence. According to the method, image quality and focus visibility are improved through multi-modal preprocessing, accurate segmentation is realized by using a deep convolutional network, traditional pixel-level precision limitation is broken through in combination with post-processing and a sub-pixel-level model, the problems of noise interference, contrast difference and boundary blur are effectively solved, and focus labeling fineness and spatial positioning precision are remarkably improved.

The invention provides a pallet position identification system and method, and relates to the technical field of image identification, and the method comprises the steps: carrying out the Gaussian filtering and Canny edge detection of image data, extracting the edge features of pallets, judging whether there is an overlapped pallet in combination with depth information, and carrying out the layered identification of an overlapped region to mark a target pallet. Calculating a contour vector of the target pallet, and mapping the contour vector to a three-dimensional space by using depth information to obtain a spatial position coordinate; when the position meets a safe distance threshold value, angular point detection is carried out, coordinates of four angular points are extracted, orientation angles are calculated, and complete position features are output. According to the method, the problems of incomplete pallet edge detection, large space positioning error and inaccurate posture recognition are effectively solved, the pallet detection precision and reliability are improved, and meanwhile, the carrying safety in a storage environment is ensured.