942 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 relates to the technical field of urban infrastructure intelligent detection, and discloses an urban drainage pipe network damage detection system based on intelligent analysis. A multi-source data acquisition module acquires pipe network static structure parameters and real-time operation monitoring data through a distributed sensor network; generating a topological characteristic value, a dynamic operation state characteristic value and a dynamic detection threshold set of each damage type; the data preprocessing module filters noise of the monitoring data, eliminates abnormal values and extracts time domain and frequency domain feature vectors; the feature fusion module fuses and generates a multi-dimensional fusion feature matrix based on the topology and operation feature values; the intelligent analysis engine calculates and outputs a damage type diagnosis result through mode matching; the spatial positioning module generates three-dimensional coordinate positioning data of the damaged area in combination with the topological characteristic value; and the dynamic learning module optimizes the detection rule according to the maintenance data. The system realizes intelligent damage detection and accurate positioning, improves the detection accuracy and efficiency, adapts to a complex environment, and is high in intelligent level.

The invention relates to the field of virtual reality positioning, and discloses a VR large-space positioning interaction system based on multi-modal perception, and the system comprises the steps: deploying a multi-modal sensor to obtain sensing data, carrying out the visual feature extraction and preprocessing, building a sparse point cloud map in a matching manner, carrying out the scale calibration, and constructing an environment model; pre-judging a UWB signal path based on an environment model, performing error optimization compensation on an NLOS state, and performing observation updating and fusion through degradation detection to obtain a predicted state change; constructing an interactive perception network, and tracking the hands and the whole body; tactile feedback is realized by using a layered tactile system, and a tactile effect is generated by using vibration frequency mapping; the transmission efficiency is improved by using a beam forming technology, an edge cloud server cluster renders a virtual scene, and the scene is pre-rendered in advance to offset network and rendering delay; an online calibration mechanism is designed, and system errors are corrected through visual loopback detection, UWB beacon dynamic correction and IMU drift compensation.

The invention relates to the technical field of automatic debugging, in particular to a digital twinning-based chip fixture debugging system, which comprises a state sensing fusion module, a positioning correction judgment module, a contact adjustment execution module, a closed-loop feedback correction module and a data linkage optimization module. According to the method, through time axis synchronous matching of multiple types of sensing data, extraction of position offset and pressure sudden change intersection features, enhancement of direction identification and dynamic precision, angle direction trend quantification through included angle interval analysis, improvement of spatial positioning continuity, and guarantee of effective response of an adjustment window through driving rate difference comparison, the accuracy of the adjustment window is improved, and the accuracy of the adjustment window is improved. Stretching and deformation data backtracking are introduced to improve the path correction capability, a virtual and real link is constructed in combination with a node log and a delay identifier, the consistency of simulation and actual behaviors is constrained, the overall debugging process is optimized through data linkage and time sequence control collaboratively, and higher response timeliness, posture recognition precision and data fusion stability are achieved.

The invention relates to the technical field of image processing, in particular to an image fusion driven anti-collision identification method and system. The method comprises the following steps: acquiring multi-source image data, and performing semantic guidance image fusion according to the multi-source image data to obtain image fusion data; performing scene segmentation according to the image fusion data to obtain scene segmentation data, and performing target detection on the scene segmentation data to obtain scene target data; performing confidence hierarchical space positioning according to the scene target data to obtain target positioning data, and performing motion trail prediction according to the target positioning data to obtain motion trail prediction data; and performing collision area propagation risk assessment according to the motion trail prediction data to obtain collision risk data. By fusing image perception depth, semantic understanding dimension and trajectory evolution modeling, the method has higher environmental adaptability, weak target recovery capability and system interpretability.

The invention discloses an intelligent monitoring system for deformation and stress of a deep foundation pit supporting structure, relates to the technical field of deep foundation pit supporting monitoring, and is used for solving the problem of poor early warning of the deformation risk of the supporting structure. According to the method, high-density acquisition of inclination angle, deformation and stress data of a key area of a deep foundation pit supporting structure is realized by constructing a sensitivity-driven measuring point layout mechanism; the method comprises the following steps: constructing an inversion model based on stress and strain rates, obtaining a structural equivalent modulus, extracting a change rate of the structural equivalent modulus, judging a rigidity evolution trend, further constructing a modulus change rate space field, setting a region threshold by combining a disturbance sensitivity factor, realizing abnormal region identification and spatial positioning, and extracting structural response behavior information on the basis. And driving a system to trigger a grading response strategy according to an analysis result of the structure response behavior information, thereby realizing intelligent early warning of local deformation and stress instability of the deep foundation pit supporting structure.

The invention discloses a substation autonomous inspection and foreign matter cleaning cooperative control system and method, and belongs to the technical field of intelligent inspection and foreign matter cleaning. The system comprises a multi-modal detection module used for acquiring images, point clouds and multi-source sensing data and identifying foreign matter coordinates and visual features; the space positioning module is used for executing space registration to obtain a three-dimensional coordinate of the foreign body; the risk decision module fuses the multi-dimensional information and evaluates the foreign matter risk level; the cleaning execution module generates a cleaning path and execution parameters and issues the cleaning path and the execution parameters; and the inspection execution module plans inspection paths and actions based on the state and constraint conditions, and completes subsequent equipment inspection tasks. According to the scheme, the system realizes clear division of labor and accurate control in multiple stages of foreign matter identification, space positioning, risk assessment, cleaning instruction generation, polling after cleaning and the like, and the expansibility and the overall response efficiency of the system are improved, so that efficient, safe and stable execution of autonomous polling and foreign matter cleaning tasks of the transformer substation is guaranteed.

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 relates to a fire prevention and control smart network method and system based on Beidou satellite navigation. The method comprises the following steps: firstly, acquiring real-time operation data of fire-fighting equipment in a building, and scanning a building structure through a three-dimensional imaging device to generate a three-dimensional digital model; secondly, fusing the real-time operation data, the three-dimensional digital model and preset environment parameters to form a fire prevention and control reference data set; thirdly, carrying out multi-dimensional data comparison on the reference data set based on a preset fire-fighting standard program, and generating an alarm signal with spatial positioning information; then, determining a fire-fighting facility failure area according to the alarm signal, and calculating a fire risk diffusion parameter; and finally, generating a grading early warning instruction according to the risk diffusion parameters, and constructing an electronic combat map containing the optimal rescue path. The method provides global and accurate basic data support for fire prevention and control, improves the timeliness and accuracy of early warning, optimizes the rescue path, and improves the fire disposal efficiency.

The invention relates to the technical field of image defect detection, in particular to a building engineering quality detection and evaluation system based on machine vision, which comprises an image acquisition module, a crack main shaft identification module, a space packaging module, a structure overlapping detection module and a building quality evaluation module. According to the method, a crack boundary layer with a spatial positioning function is constructed through image edge scanning and pixel position marking, pixel segments with high continuity are screened to generate a path line, the continuity and representativeness of crack trend are improved, and a rectangular block is packaged to enhance organization and complete expression of cracks in a spatial level; performing coordinate comparison with a structure stress area to form corresponding association between defect influence and a structure function, judging the influence of the crack on a key area through a space coverage relation between a total block amount and an overlapping amount, and establishing multi-dimensional building quality grade division by combining crack form, position distribution and structure association information; and comprehensive evaluation of building structure defects from geometry to structure level is realized.

The invention relates to the technical field of synchronous positioning and map construction, in particular to a geometric-semantic collaborative fusion mobile robot three-dimensional semantic map construction method, which is used for performing deep fusion on a geometric map and multi-view semantic information so as to solve the problem of difference of the two types of information in resolution, scale and dimension. The geometric map mainly provides structured information of the environment, and the semantic information describes object categories and features thereof in the environment. Direct fusion of the two types of information often causes spatial positioning errors and semantic information loss or redundancy problems. According to the method, a geometric map containing environmental structured information is constructed through an SLAM technology, and meanwhile, object classification, attribute labeling and semantic relationship analysis are performed on multi-view image data by utilizing the semantic information extraction capability of a large model. And finally, precise fusion of the geometric map and multi-view semantic information is realized, and meanwhile, the object recognition and semantic understanding capabilities of the robot in a complex scene are improved.

The invention discloses an actuator sealing performance test method and system, particularly relates to the technical field of industrial equipment sealing performance detection, and is used for solving the problem of positioning failure caused by signal aliasing in a multi-leakage scene in an existing method. Pressure attenuation frequency spectrum characteristics and leakage impact frequency spectrum peaks are extracted respectively by pressurizing a sealed cavity and synchronously collecting dynamic pressure and vibration signals; calculating a path loss value based on the amplitude gradient and the propagation time difference, and screening candidate leakage nodes; constructing a space grid model to solve three-dimensional coordinates of the leakage source; after the coordinates are projected to the surface of the structure model, pressure modal coherence and vibration impact energy are coupled and analyzed to generate a detection map; and finally, outputting a multi-leakage-point identification graph through spatial clustering and intensity grading. Accurate separation and spatial positioning of multiple leakage signals are achieved, and the reliability of complex sealing structure detection is remarkably improved.

The invention relates to the technical field of visual navigation, in particular to a robot navigation method based on vision, which comprises the following steps: acquiring a grayscale image to extract dynamic point locations, screening boundary features to reconstruct a feature set, calculating gradient change to generate candidate guide points, converting coordinates to construct a path track set, and outputting a navigation planning path. According to the method, rapid elimination of static backgrounds is realized through difference threshold selection, dynamic region extraction accuracy is effectively improved, combined screening of boundary line feature point frequency and average feature density is combined, key feature stability is enhanced, and the identification capability of image local structure mutation is enhanced by using a variance change rate of a gradient amplitude sequence. Robustness of path guide point extraction is improved through judgment of an abnormal section, dynamic construction of a path track is realized by means of continuous time integration of image coordinates, path identification precision and spatial positioning continuity are optimized, and continuity, precision and real-time performance of navigation path planning are improved.

The invention relates to the technical field of physical education, in particular to a physical education method and system based on multimedia and holographic AR fusion, and the method comprises the following steps: collecting the motion data of a trainee based on a multi-source sensor array, constructing a motion parameterized model, and generating a holographic projection generation module through the cooperation with a standard motion database; a holographic AR teaching image is generated in real time, an environment perception module is combined to carry out adaptive projection on illumination and spatial layout, a spatial positioning module is used to establish coordinate mapping between a trainee and a virtual image, and a multi-channel feedback device is started to output visual, tactile and auditory signals. And finally, comparing the action parameters with the standard action data to automatically generate a training report containing wrong action labels and correction suggestions. According to the method, the motion capture precision and the interactive immersion can be effectively improved, and efficient and extensible technical support is provided for modern physical education and training.

The embodiment of the invention provides a method and device for identifying fire protection hidden dangers through an AI visual analysis technology, and the method and device achieve the integration of image video streaming, three-dimensional space, thermal imaging, environment perception and other multi-modal data through the innovative construction of a multi-source data collection and fusion mechanism. And designing a feature extraction model based on transfer learning, and realizing high-precision hidden danger feature recognition through integrated learning in combination with a hierarchical classification structure and an attention mechanism. Time sequence analysis and space positioning technologies are introduced, a hidden danger feature association network and an evolution model are constructed, and hidden danger development trend prediction and common hidden danger discovery are achieved. According to the method, the defects of the traditional technology in the aspects of multi-modal data processing, feature recognition, trend prediction and the like are effectively overcome, and the intelligent level and the early warning capability of fire protection hidden danger recognition are remarkably improved.

The invention discloses an anti-collision monitoring system based on a depth estimation and instance segmentation fused three-dimensional model, particularly relates to the field of intelligent driving security and protection, is used for solving the problem of target recognition and anti-collision in an environment, realizes efficient feature extraction of global semantics and local textures through a multi-branch network structure, and ensures the accuracy and comprehensiveness of target segmentation. In combination with a cross-frame identity association and conflict detection mechanism, the consistency of target identities is effectively maintained, and the mismatching problem caused by shielding or similar appearances is reduced; multi-scale space-time shielding mode analysis and boundary motion consistency verification are adopted, a fusion algorithm is utilized to comprehensively evaluate shielding complexity and segmentation robustness, a high-risk shielding area is identified, secondary difference correction is executed, and space positioning information of a target is remarkably optimized; and finally, integrating the optimized segmentation and depth data into a global coordinate system, constructing a high-precision and coherent three-dimensional semantic scene, and providing stable and high-quality input data for collision detection and trajectory prediction.

The invention relates to the technical field of unmanned aerial vehicle cooperative hoisting, and provides a multi-unmanned aerial vehicle cooperative hoisting system and an unmanned aerial vehicle module, the unmanned aerial vehicle module is composed of two or more unmanned aerial vehicles, and each unmanned aerial vehicle is equipped with an airborne computer, a flight control assembly, a power assembly, a space positioning sensor, a tension sensor and a communication unit; the spatial positioning sensor is used for acquiring spatial position data of the unmanned aerial vehicle; the tension sensor is used for acquiring tension data of goods hoisted by the unmanned aerial vehicles, and the airborne computer is in data connection with a communication unit of the unmanned aerial vehicles and is used for calculating real-time lift force and attitude adjustment amount of the unmanned aerial vehicles based on the tension data and spatial data acquired by the unmanned aerial vehicles and controlling flight parameters of the multiple unmanned aerial vehicles according to the real-time lift force and attitude adjustment amount; according to the invention, through a multi-source fusion positioning and sliding mode adaptive hybrid control algorithm, cooperative positioning and dynamic load balancing of the unmanned aerial vehicle group are realized, and the overall stability and anti-interference capability of the hoisting system under complex disturbance are ensured.

The invention relates to an intelligent building damage detection method based on machine vision. The method comprises the following steps: multi-modal data acquisition; preprocessing and feature enhancement: processing the visual data and the acoustic data; damage detection: designing a material constitutive driven GAN architecture, calculating and generating stress distribution of a crack by a generator through a differentiable finite element analysis layer, performing L2 loss constraint on the stress distribution and an ABAQUS simulation result, evaluating image authenticity and physical consistency by a discriminator by adopting a double-branch structure, and outputting damage parameters in cooperation with a lightweight YOLOv8-Nano detection head; acoustics-vision depth fusion detection: aligning a potential space through a double-flow cross-modal encoder in combination with cross-modal contrast learning, and preferentially processing hidden damage by using a gating fusion unit for dynamically calculating a fusion weight through material acoustic impedance; when the visual detection confidence coefficient is smaller than a set threshold value, triggering frequency domain mutation detection and spatial positioning of the acoustic mode; and edge-cloud cooperative processing.

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 relates to the technical field of electrical equipment defect monitoring, in particular to a high-voltage equipment defect positioning system and method based on multi-modal image fusion, and the system comprises an ultraviolet triggering module, a modal synchronization module, a space positioning module, a structure recognition module and a fusion diagnosis module. According to the method, automatic calibration of a suspected defect area can be realized by performing quantitative threshold judgment on a corona discharge signal in an ultraviolet image, the consistency of three-mode data in space and time dimensions is ensured, and the accuracy of the defect area is improved by comparing three types of coordinates of a lead hot spot, boundary geometry and a spot center in the image and performing registration offset correction. The image feature alignment precision is improved, the insulator chain light spot distribution path is extracted, the continuous distribution length is calculated and corresponds to the pollution level standard, automatic identification of the pollution level is achieved, a multi-modal image fusion feature block is constructed, and a risk sorting label is given. And the defect positioning accuracy and the comprehensive evaluation capability on the defect property and the influence degree are effectively improved.

The invention relates to the technical field of partial discharge detection, in particular to a leak detection and partial discharge detection method and system based on artificial intelligence, and the method comprises the following steps: obtaining a partial discharge pulse signal, extracting a phase mutation coordinate, analyzing an incremental feature, reconstructing a propagation waveform, constructing an amplitude gradient matrix, and screening a direction vector; and a backtracking path calculates a change rate to generate a time track, and track mapping and space positioning are completed in combination with image data. According to the method, the accuracy and the response speed of partial discharge detection are improved through multi-stage signal processing, extreme points and phase abrupt change are analyzed, more accurate discharge feature capture and equipment defect positioning are achieved, phase and amplitude data are combined, the discharge mode is recognized, a signal propagation path is reconstructed, the fault source positioning accuracy is improved, and the fault source positioning accuracy is improved. Through dynamic rendering, the intuition of a detection result is enhanced, real-time decision is supported, the method is more intelligent and efficient, the equipment fault risk is greatly reduced, and the safe operation level of power equipment is improved.

The invention discloses an improved YOLOv11-based traffic sign identification and detection method, which comprises the following steps: based on a backbone network of YOLOv11, providing an IPC3k2 layer, replacing standard convolution by Ghboost to realize lightweight feature extraction, embedding a Coard coordinate attention mechanism in Bottleneck to strengthen space positioning capability, and combining a double-branch Ghboost feature fusion strategy; a SomSPPF module is provided for the trunk part, and a multi-scale deformable pyramid pooling module, a two-dimensional attention mechanism module and a SamReBlock re-parameterization convolution module are fused; in a neck network, an MSCATR attention module is added, dynamic channel fusion is adopted to realize dynamic fusion of channel statistical guidance, cross-space fusion is utilized to establish a space cooperation mechanism, an adaptive feature fusion module is introduced, and finally residual multi-level feature optimization is realized through learnable weight parameters. Compared with the prior art, the method has the advantages that the accuracy of traffic sign detection of the YOLOv11 model can be effectively improved, and the advantages of real-time performance and robustness of the model are considered.

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 provides a spatial heterogeneous vision positioning system and positioning method of a pineapple picking robot, relates to the technical field of picking robot positioning, and realizes high-precision spatial positioning and attitude estimation of the robot in a complex terrain environment by comprehensively utilizing multi-source information such as vision, inertial measurement, multispectrum and structured light. The positioning stability and the real-time performance are obviously improved; by embedding a multi-modal deep learning model, efficient non-destructive detection and intelligent classification of internal defects of pineapple fruits are realized, a mechanical arm is supported to dynamically adjust a picking strategy, fruits with better quality are preferentially selected for picking, and the picking quality and efficiency are improved; and meanwhile, the preset three-dimensional terrain model and the heuristic search algorithm are combined, the motion path of the robot is effectively planned, it is guaranteed that the robot safely and stably moves in the complex environment, and the mechanical damage risk is reduced.

The invention relates to the technical field of high-speed railway operation and maintenance, and provides a 4C detection-based high-speed railway overhead line system completion acceptance method and system, and the method comprises the steps: collecting overhead line system image data, obtaining pose data and spatial positioning data of an inspection vehicle, carrying out the synchronous calibration processing, building a mapping relation, and obtaining a calibrated overhead line system image; inputting the calibrated overhead line system image into a multi-scale target detection model, outputting a detection result, performing pose correction on the detection result, and generating a three-dimensional positioning coordinate in combination with the mapping relation; constructing a time sequence feature vector, inputting the time sequence feature vector into the prediction model, outputting a defect evolution trend and a risk probability of the part, and grading the risk probability; and according to the risk level and the spatial positioning data, combining a knowledge graph driven rule engine, generating an intelligent work order, mapping the intelligent work order to field equipment, and collecting and feeding back a maintenance result. According to the method, the recognition and spatial positioning precision of the parts of the overhead line system is improved, and the safety and reliability of completion acceptance of the overhead line system of the high-speed rail are improved.

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 relates to a transplanting position quality evaluation method based on image analysis and geometric modeling, in particular to a transplanting position quality evaluation method based on image analysis and geometric modeling. The invention aims to solve the problems of low precision, single evaluation index and lack of space positioning capability of the existing unmanned aerial vehicle remote sensing image rice transplanting position estimation. The method comprises the steps that S1, an unmanned aerial vehicle collects an RGB image data set of unmarked farmland seedling positions; s2, obtaining an RGB image data set marked with farmland seedling positions; s3, obtaining a pre-trained encoder; s4, obtaining a trained farmland rice seedling target detection model; s5, inputting to-be-detected RGB image data into the trained farmland rice seedling target detection model, and outputting farmland seedling positions in the to-be-detected RGB image data; s6, obtaining the transplanting spacing, judging whether seedlings are missing or not, and if not, ending; if not, estimating a seedling missing position; s7, the seedling missing position in the step S6 is converted into a geographic coordinate system. The method is used in the field of transplanting position estimation.

The invention relates to the technical field of civil engineering construction, in particular to a precise positioning civil engineering pile foundation construction system which comprises a multi-source space sensing and positioning module, a space-time multi-dimensional data fusion module, a three-dimensional guiding and correcting module, a hydraulic control module and a closed-loop feedback optimization module. The multi-source space sensing and positioning module obtains pile machine position and attitude information through the Beidou / GNSS high-precision positioning unit, the inertia high-precision navigation unit and the laser horizontal measurement unit, and the space-time multi-dimensional data fusion module processes and fuses the information to generate unified space position data. The three-dimensional guiding and correcting module compares the data with preset pile position coordinates, if the deviation exceeds a threshold value, a correcting instruction is generated, the hydraulic control module executes the instruction and adjusts the position and the posture of a pile machine, the closed-loop feedback optimization module collects the data in the correcting process and optimizes a correcting strategy, all-weather centimeter-level precision space positioning is achieved, and the positioning precision is improved. And the accuracy and efficiency of pile foundation construction are improved.

The invention provides an intelligent machine room multi-dimensional dynamic monitoring management method and system based on digital twinning, and relates to the technical field of machine room monitoring, and the method comprises the steps: collecting a three-dimensional point cloud set and an RGB image set of a machine room space, and building a space mapping table; mapping the access control event data to a corresponding position in a digital twin space, generating an access control event space positioning set, identifying access control abnormal events, and constructing an access control abnormal event space positioning set; performing abnormal behavior recognition, and constructing an abnormal event graph structure; reconstructing and visualizing an abnormal track based on the abnormal event graph structure; in combination with the access control abnormal event space positioning set, the abnormal behavior category prediction probability distribution and the track risk level, executing a grading early warning mechanism, and generating an access control abnormal event log; according to the invention, intelligent positioning, track visualization and risk grading early warning of access control abnormal events in the intelligent machine room are realized, and the method is suitable for digital operation and maintenance management systems such as the intelligent machine room.

The invention discloses a self-adaptive multi-sensor fusion SLAM (simultaneous localization and mapping) system implementation method and system, a hierarchical fusion asynchronous architecture with a front-end sensing layer, a middle fusion layer and a rear-end optimization layer is designed, and the front-end sensing layer comprises a feature extraction and matching and timestamp asynchronous processing mechanism. The middle fusion layer comprises sliding of a time window and alignment of data to a time axis, and the rear-end optimization layer comprises a multi-source factor graph model, state estimation, map updating and calculation optimization; a self-adaptive weight adjustment module is arranged between the front-end sensing layer and the middle fusion layer; multi-dimensional evaluation, self-adaptive adjustment and engineering practicability of SLAM spatial positioning mapping are realized; the front-end sensing layer performs data acquisition and preprocessing on each sensor for spatial positioning mapping, and then independently performs feature extraction and matching and timestamp asynchronous processing mechanism implementation. The problem of fusion time sequence synchronization is effectively solved, the environmental adaptability is improved, and the calculation complexity is reduced.