187 results about "Spatial localization" patented technology

The invention relates to the field of remote sensing image processing, in particular to a high-resolution remote sensing image semantic segmentation method based on multi-scale feature fusion, which comprises the following steps: acquiring a public remote sensing image data set, preprocessing the image, and constructing a training and testing set of semantic segmentation; a CTMFNet is designed, an encoder is composed of a lightweight residual module and an MS-Transform, and local space details and global context information are extracted; rID is adopted to reduce spatial information loss, LSFE is introduced to improve spatial positioning capability, and feature calibration is carried out in space and channel dimensions through DecoderAttn to realize boundary fine segmentation; inputting the training sample into the network for training to obtain a converged optimal semantic segmentation model; and inputting the test set into the model to obtain a semantic prediction map, and outputting a fine segmentation result of the remote sensing image through multi-scale fusion and boundary restoration. According to the method, the precision and robustness of ground feature extraction are effectively improved, the calculation cost is remarkably reduced while high segmentation precision is kept, and the method has good practical value and popularization prospects.

The invention belongs to the technical field of fault detection, and relates to a high-voltage transmission line fault detection and identification method. The method comprises the following steps of: dividing candidate fault sections according to a topological structure by synchronously acquiring steady-state and transient-state traveling wave signals of key nodes of a power grid; identifying a fault by using a multi-feature fusion criterion and extracting traveling wave features; executing mode recognition-based steady-state section judgment and multi-terminal ranging and polarity verification-based transient positioning in parallel to generate two types of positioning result sets; and finally, carrying out intelligent fusion judgment on the two types of results under topological constraints. According to the method, the problems that a traditional single-point signal analysis method cannot determine a fault section, lacks space positioning capability and is insufficient in adaptability are solved, section-level accurate positioning of the fault is realized, and the positioning accuracy, robustness and fault processing efficiency are remarkably improved.

A method for surveying an environment by a movable surveying instrument with a progressional capturing of 2D-images by at least one camera and applying a visual simultaneous location and mapping algorithm (VSLAM) or a visual inertial simultaneous location and mapping algorithm (VISLAM) with a progressional deriving of a sparse evolving point cloud of at least part of the environment, and a progressional deriving of a trajectory of movement. The method comprises a progressional matching of the sparse evolving point cloud with a previously derived 3D-geometry, with a minimizing of a function configured to model a distance between the sparse point cloud and the previously derived 3D-geometry and deriving a spatial localization and orientation of the surveying instrument. At least one surveying measurement value of the environment by a spatial measurement unit is combined with the sparse point cloud or the previously derived 3D-geometry.

The invention relates to the technical field of three-dimensional models, and discloses a GIS data-based low-altitude visual three-dimensional model automatic generation method, which comprises the following steps of: acquiring multi-source GIS vector data from an open geographic data source, performing standardized preprocessing through a data cleaning rule set, constructing a Cesium earth scene basic framework in a UE5 engine environment, and dynamically generating a city L2-level three-dimensional visualization model based on a preprocessed GIS data stream; when a low-altitude visual three-dimensional model is generated, a lightweight data format is adopted, data transmission efficiency and dynamic updating capability are improved, complex light environment changes can be adapted in real time, the problem of color distortion on the surface of the model is reduced, meanwhile, a visual angle deviation detection and correction mechanism is established, the geometric body edge distortion phenomenon is relieved, and the generation efficiency of the low-altitude visual three-dimensional model is improved. And the color rendition precision and the spatial positioning reliability of the low-altitude scene are improved.

The invention relates to the technical field of pile foundation deformation measurement, in particular to a pile foundation deformation distributed test method and system, and the method comprises the steps: obtaining a time sequence strain value sequence of sampling points of a tower pile foundation, calculating a dynamic disturbance index, extracting a quasi-static signal, and determining a long-term strain value increment through time domain trend analysis; through space-time decoupling operation, correlation between time evolution behaviors of a sampling point and a physically adjacent sampling point is compared, and a space localization index is determined; taking the spatial localization index as a confidence coefficient weight, and performing weighted distribution on the long-term strain value increment to respectively determine an adaptive global trend component and a local abnormal component; and finally, performing spatial integral operation on the global trend component and the local abnormal component to obtain two decoupled deformation curves so as to determine the deformation state. The method improves the accuracy of the deformation test.

The invention relates to the technical field of data processing, and discloses a drainage pipe network rain and sewage mixed connection tracing and spatial positioning method for a micro monitoring station network, which comprises the following steps: defining an upstream convergence pipe network range of each micro monitoring station according to a flow direction connection relationship of a drainage pipe network and layout nodes of the micro monitoring stations, establishing a unique mapping relation library between each pipe section and the corresponding multi-stage monitoring topological sequence; obtaining water quality and water level data of a monitoring station, intercepting an analysis time period, and identifying a target monitoring topological sequence which generates a response currently in the mapping relation library based on a time-space propagation association attribute of data between stations; locking an isomorphic mapping candidate pipe section set corresponding to the sequence, and constructing a pollutant source emission inversion model of each candidate pipe section according to a pipe network fluid transport diffusion mechanism; and calculating the fitting deviation degree of the source hypothesis of each candidate pipe section to the monitoring data by using the model, and selecting the pipe section with the minimum deviation degree as a positioning result.

The invention discloses a method and system for screening drug targets with space-time specificity and computer equipment, and relates to the technical field of bioinformatics and computational biology. The screening method is based on single cell transcriptome sequencing data, and comprises the following steps: (1) quantitatively reconstructing spatial positioning and functional modes of cells in tissues, namely 1.1) carrying out data preprocessing on the single cell transcriptome sequencing data; 1.2) reconstructing the spatial positioning of the single cell; 1.3) reconstructing a single cell biological function mode; (2) screening a drug target with space-time specificity, wherein the screening comprises the following steps: 2.1) cell-cell communication analysis; the invention discloses a single-cell data analysis method based on a GRN (Gene Regulatory Network), which is characterized by comprising the following steps of (1) establishing a single-cell data analysis method, (2) establishing a GRN (Gene Regulatory Network) taking a specific tissue microenvironment state as a core, and (3) discovering a target spot. The single-cell data analysis method is innovative, provides a new thought and a technical path for research and development of drugs for metabolic diseases and other systemic diseases, and has a popularization and application basis.

The invention relates to the technical field of AR, and discloses an AR war chess projection and identification method and system based on spatial positioning and region segmentation. The method comprises the following steps: acquiring a map local image, identifying angular point pixel coordinates and angular point world coordinates of a reference mark in the map local image, and calculating a working range polygon of a map surface currently covered by a projection device based on the angular point pixel coordinates and the angular point world coordinates; the coordinate information of the working range polygon is sent to a server, and the server screens local situation data located in the working range polygon from the global situation data; and receiving the local situation data pushed by the server, rendering a projection image limited in the working range polygon, driving the projection device to project the projection image to the surface of the map, and displaying situation enhancement information in a region corresponding to the working range polygon in an overlapping manner. According to the invention, the efficient multi-device cooperative work capability is realized, and the technical problem of large-size map AR enhancement is solved.

The invention relates to the technical field of product design, in particular to a household appliance appearance defect optimization product design method based on image discrimination, which comprises the following steps: acquiring a household appliance appearance image, calculating a gradient direction, constructing a distribution matrix, analyzing consistency to generate a defect edge image, extracting high-frequency feature scores to identify defect areas, and clustering point cloud to output defect clusters. According to the method, by calculating the gradient direction numerical value of each pixel point and constructing the gradient direction distribution matrix, the processing capacity of the directivity difference of the household appliance surface texture is effectively improved, the defect point cloud is accurately divided through the density-based noise application spatial clustering algorithm, the defect point cloud is accurately divided, and the defect point cloud is accurately divided. According to the method, the space positioning accuracy of the defect area is improved, the precision of optimization design is ensured, curved surface reconstruction is performed in combination with the least square method, the appearance design of the household appliance is accurately corrected, and the production efficiency and the design quality are optimized.

The invention provides a lightweight intelligent control method for a low-altitude operation scene, and the method comprises the steps: focusing a low-altitude key target through an attention mechanism based on a lightweight deep learning model; data enhancement is carried out through a dynamic data enhancement module so as to expand the diversity of training samples; calling a space-time calibration model to process the multi-source data, and controlling a time synchronization error and a space positioning deviation within a preset range; based on a dynamic obstacle trajectory prediction module, historical motion data and environmental constraints are fused, and a lightweight LSTM network is adopted to predict a trajectory of a dynamic target in a future time period; and calling a task priority-computing power load dynamic matching mechanism to realize task allocation, and adjusting computing power occupation of non-critical tasks according to a designed energy consumption perception scheduling strategy. According to the method, the autonomous perception, decision making and resource management capabilities of the low-altitude operation unmanned aerial vehicle in a complex environment are comprehensively improved, so that the low-altitude operation unmanned aerial vehicle better meets the requirements for safety, efficiency and continuity in actual operation.

The invention discloses a quadruped robot space precise positioning method and system based on deep learning. The quadruped robot space precise positioning method specifically comprises the steps that S1, a laser radar data stream and an inertial measurement unit data stream are aligned to generate a synchronous sensing data sequence in a unified mode; s2, determining a mapping pose sequence through consistency evaluation of poses output by the offline point cloud registration algorithm pool; s3, performing parallel loopback detection to generate a unified loopback constraint set; s4, the mapping pose sequence, the loopback constraint set and the pre-integration constraint are written into the factor graph model to generate a three-dimensional point cloud reference map; s5, the local point cloud frame and the three-dimensional point cloud reference map are registered, and a map matching pose is output; s6, the map matching pose and the pre-integration result are written into a factor graph model to continuously solve and output a continuous-time six-degree-of-freedom pose result; and S7, outputting a six-degree-of-freedom pose result in continuous time. According to the invention, direction constraint consistency point cloud registration and dual-channel loopback optimization are introduced, and stable and high-precision positioning of the closed space is realized.

The invention relates to the technical field of Internet of Things coding, in particular to a communication infrastructure asset digital management and operation and maintenance system based on the Internet of Things, which comprises the steps of acquiring equipment output content, arranging frequency bands and interface identifiers, combining positioning information for verification, generating a unified record structure, expanding coding comparison to form an evolution number, and carrying out management and maintenance. And inserting a character to construct an intergenerational identifier, matching boundary refined region mapping, and aligning and splicing to generate an operation and maintenance identifier. According to the method, a uniform data field structure is formed through sequential combination of frequency band coverage and interface identification and fusion of spatial positioning information, a continuous equipment intergenerational relationship is constructed through splicing of evolution sequence numbers, spatial distribution levels are processed through the number of region registration and boundary rules, and the spatial positioning information is obtained. Uniform identification content is generated through splicing of the intergenerational numbers and the region identifiers, collaborative organization of communication facilities in development states, space attribution and management relations is supported, and the scheduling and management efficiency of asset data is improved.

The invention provides a spatial diagnosis system for mountainous city facility layout. Comprising the following steps: a data acquisition and preprocessing module preprocesses initial multi-source spatial data of a target area to obtain target multi-source spatial data; the space unit division module performs space unit division on the target area to obtain a plurality of space units; a public service facility reachability calculation module determines public service facility reachability of a target demand point in the target area according to the target multi-source spatial data; the spatial difference diagnosis module identifies spatial heterogeneity of each spatial unit on different scales according to the target multi-source spatial data; the problem space positioning module performs problem space positioning according to the public service facility accessibility of the target demand point and the topographic features of the target area; and the optimization scheme generation module generates an optimization scheme for each problem space. Therefore, according to the regional characteristics of the mountain city, optimal configuration schemes of different facilities are provided in a targeted manner, so that the scientificity and rationality of walking facility configuration are improved.

The invention discloses a large-burial-depth pipeline nondestructive detection method and system, and relates to the technical field of pipeline positioning. The system combines a region feasibility identification module, an initial positioning module, a multi-type nondestructive detection unit, a multi-path anomaly identification module, an underground structure matching module, an intelligent obstacle avoidance compensation module and a signal fusion module, and adopts a multi-source information fusion and intelligent path planning technology. And high-precision and non-intrusive nondestructive detection of the metal pipeline with the burial depth exceeding 5 meters is realized. Through comprehensive evaluation of ground obstacles and underground interference sources, the detection path layout is optimized, and the problems of signal interference, multi-path reflection and detection blind areas in the urban complex underground environment are effectively solved. The system can accurately reject non-target structure reflection signals, dynamically generate an obstacle avoidance compensation path, and improve the detection coverage rate and the signal integrity. And finally, three-dimensional space positioning and digital map coordinate output are realized, and the accuracy of pipeline positioning and the reliability of depth estimation are remarkably improved.

The invention discloses a cutting pick wear detection method and system for a heading machine, and relates to the technical field of mining machinery state monitoring, and the method comprises the steps: obtaining a cutting pick tool seat and a tool bit, fixing the tool bit on the tool seat through a plurality of rotating shafts, and arranging a plurality of high-bandwidth acoustic emission sensors on the rotating shafts; identifying a rotation detection signal by using a high-bandwidth acoustic emission sensor, and extracting characteristics such as acoustic emission pulse starting time and peak amplitude; constructing a sound source space positioning model to obtain a sound source positioning result; and mapping a sound source positioning result to the rotating shaft three-dimensional model to identify hidden wear, and outputting a risk level. The technical problems that the traditional detection means is difficult to accurately identify the hidden wear of the heading machine cutting tooth rotating shaft and cannot comprehensively and accurately acquire wear information to meet the accurate evaluation requirement are solved, and the accurate identification and risk grade evaluation of the hidden wear of the heading machine cutting tooth rotating shaft are achieved; and the accuracy and comprehensiveness of cutting pick wear detection are improved.

The invention relates to the technical field of computer vision detection, and discloses a PCB defect detection method based on improved YOLOv8. According to the invention, by introducing the C2f module fused with the coordinate attention mechanism, the adaptive weighted bidirectional feature pyramid structure and the lightweight decoupling detection head, the feature extraction capability and the spatial positioning precision of the model for PCB micro defects are improved, and the problem of small target missing detection is solved; meanwhile, in combination with defect area specific enhancement training and a dynamic receptive field optimization strategy, the anti-interference capability of the model to a complex background is enhanced, and the false detection rate is greatly reduced; besides, by adopting a structure re-parameterization technology, the model reasoning speed is improved while the detection precision is ensured, so that the method can be efficiently deployed on industrial detection equipment with limited resources, the balance of high precision and high efficiency is realized, and the comprehensive performance of PCB defect detection is comprehensively improved.

The invention discloses a plot soil pollution condition survey information acquisition and analysis system, relates to the technical field of plot soil information acquisition and analysis, and aims to solve the problem of unclear soil pollution condition analysis. According to the method, abstract sample detection data is converted into a law conclusion with spatial significance through accurate association, standard processing, visual display and deep analysis, the accuracy and systematicness of spatial analysis are ensured, the practicability of a result is enhanced through visualization, and the accuracy and accuracy of the result are improved by combining the spatial distribution law of sample analysis. A pollution core area is delimited for an aggregation area, key parameters are counted, classified implementation of target area, physical and chemical property and pollutant detection is provided for traceability and key treatment, various sample sensors are accurately matched with detection items, a full-dimension detection system of basic attributes and pollution characteristics is formed, and the detection accuracy is improved. The grid point distribution method or the snakelike point distribution method is combined with the marked GPS coordinates, the sampling depth and the serial number, so that the point location distribution is uniform, and the space positioning traceability of the sample is realized.

The invention discloses a reverse knowledge distillation-based cross-stage feature fusion method for industrial anomaly detection and positioning, and relates to the technical field of knowledge distillation and transfer learning. The method comprises the following steps: step 1, constructing a teacher network encoder, a student network encoder and a decoder; 2, extracting input image features through a student network encoder, and reconstructing the features through a student decoder; step 3, student network cross-stage feature fusion design; 4, locally sensing a dynamic attention module LDA, and adding a sliding window and convolution to replace original global pooling; and step 5, a self-adaptive multi-scale feature fusion module CAAMS-FF establishes a dependency relationship of a context so as to realize a fine-grained high-quality feature reconstruction effect. And step 6, inputting an anomaly graph obtained by the teacher-student network into the segmentation sub-network to obtain a final anomaly detection and positioning result. While the calculation efficiency is maintained, the spatial positioning precision and the multi-scale adaptability are significantly improved.

The invention belongs to the technical field of water supply network leakage monitoring, and particularly relates to a water supply network leakage node and time window combined positioning method based on a hybrid neural network, which realizes spatial positioning of a water supply network leakage node and time positioning of a leakage time window. Comprising the following steps of 1, collecting node pressure and flow time sequence data of the water supply network in different scenes, and recording leakage nodes and leakage starting and ending time labels; 2, carrying out normalization preprocessing on the data, and constructing a standardized time sequence data set containing a multi-leakage scene mark; step 3, a dual-branch hybrid neural network model TCN-Transform-KAN is built, and a dual-branch hybrid neural network model TCN-Transform-KAN is built; 4, constructing a joint loss function of node classification loss and time regression loss; and step 5, training through a joint loss function constraint model, and outputting a leakage node and a center position and a continuous width of a leakage time window. According to the method, joint precise identification of the leakage nodes and the time range is realized, and technical support of time-space two-dimensional information is provided for intelligent operation and maintenance of a water supply pipe network.

The invention provides a sound source localization method using time delay and dead reckoning. The method comprises the following steps: acquiring an original sound signal and carrying out noise reduction preprocessing; virtual sound rotation processing is carried out on the two sound source directions, interpolation simulation rotation is carried out on effective sound signals after noise reduction, and angle differences before and after rotation are compared to eliminate false angles; and finally calculating the position of the sound source through a geometric positioning algorithm by combining the unique sound source direction, the movement distance and the movement direction of the equipment and the time difference between the sound source and the two microphones. According to the method, acoustic orientation and a smart phone inertial navigation technology are deeply fused, a displacement vector is obtained by utilizing dead reckoning, and multi-position TDOA measurement information is combined, so that sound source three-dimensional space positioning only depending on single mobile equipment is realized, and the inherent defect that a traditional pure acoustic positioning method cannot measure distance is overcome.

The invention discloses a surgical instrument abnormal state recognition method based on image recognition, particularly relates to the field of image recognition, and is used for solving the problem that an existing surgical instrument structure is abnormal and detained. Instrument identification is executed by collecting image stream data covering an operation field space; dividing a shielding area in the surgical field according to an image shielding form, and counting a high-risk part of an instrument structure in combination with medical surgical accident data; identifying a start-stop image frame when the instrument enters and exits from the sheltered area, constructing a polar coordinate system based on the main axis direction and the center of the instrument, performing angle sorting and sliding comparison on the edge of the instrument under the coordinate system, and focusing and identifying the structure defect of the high-risk part; and carrying out overlapping analysis on the identified structure loss abnormity and the surgical field shielding area, and outputting a closed area set in which the component may be retained. According to the scheme, abnormal focusing of the instrument fracture event in the operation and part retention risk space positioning are achieved, and the accuracy and efficiency of risk management and control in the operation are improved.

The invention discloses an AR (Augmented Reality) space positioning and enhancing method and equipment based on an event camera. The method comprises the following steps: acquiring event stream data collected by an event camera, extracting high-frequency dynamic event feature points, and performing time domain resampling on event streams based on a sliding time window to generate a multi-segment continuous event fragment set; determining the real-time position and attitude information of the target object in the three-dimensional space; and a Gaussian voxel model is constructed, and spatial coordinate binding and attitude adaptive rendering of AR content are realized through Monte Carlo nerve sputtering rendering. In some embodiments, significant event points are screened in combination with an event neighborhood graph structure and a dynamic response mechanism; in other embodiments, anisotropic covariance modeling is performed on event point clouds based on principal component analysis. And finally, fusing the rendering result and the real image to generate an augmented reality image. The method has the capabilities of high-precision positioning, low-delay rendering and virtual-real fusion display, and is suitable for scenes of AR navigation, immersion interaction and the like.

A method and system for parasitic extraction in semiconductor layouts using spatial localization and point reduction, thereby improving efficiency of predictive models for identifying parasitic capacitance in electronic circuit design. Candidate physical layouts are generated from electrical design. Transistors and wiring are spatially modeled as three-dimensional structures. Geometrical points are identified from the three-dimensional structures being modeled and used as nodes in a neural network. A subset of the nodes is selected, and the subset is further limited to a limited region surrounding the given node to be evaluated. Nodes can have weighting based on relative geometrical position. Parasitic extraction is performed on the limited number of nodes for efficient evaluation of candidate layouts.

The invention concerns methods for predicting changes in a tissue, comprising obtaining snap shot information on individual cells of at least one cell type in a section of a tissue biopsy, wherein the information comprises: (i) spatial localization, (ii) cell type and (iii) cell division state at a specific point in time; calculating cell division probabilities for each of said individual cells; determining cell population dynamics based on the cell division probabilities and predicting future changes in the tissue based thereon. Particularly, the method concerns predicting tissue dynamics in cancer biopsies relevant for prognosis, and assessment of response to therapy.

The invention provides a Bluetooth positioning method, and relates to the technical field of positioning. In view of the problems of slow positioning response and low accuracy in the prior art, the Bluetooth positioning method provided by the invention comprises the following steps: acquiring original signal data corresponding to Bluetooth equipment acquired based on a plurality of Bluetooth base stations; and inputting the original signal data, the pose data of the Bluetooth base station and the Bluetooth channel index into the trained spatial positioning model, and outputting a positioning result of the Bluetooth device through processing of the spatial positioning model. Wherein the spatial positioning model is obtained through training based on sample data of the Bluetooth device and a reference trajectory, and the reference trajectory is obtained through data fusion based on point cloud data measured by the laser radar assembly and motion compensation data measured by the inertial measurement assembly; the sample data comprises original signal data, pose data of the Bluetooth base station and a Bluetooth channel index. According to the embodiment of the invention, the positioning efficiency and the positioning accuracy can be improved.

A spatially positioned imaging system for a machine, comprising: an imaging device mounted to a frame of the machine; a target configured to interact with the imaging device; and a controller operatively connected to an implement of the machine, the controller configured to receive the target data from the imaging device, interpret the target data to compute a map of a field on which the machine is operating, provide a real-time estimate of a position of the machine within the field, and provide automated input for the implement.

The application provides a large scene graph switching method, system and device and a storage medium, and the method comprises the following steps: S1, constructing a point cloud map and a graph database of a large scene; S2, robot positioning according to the graph map and real-time switching of the graph. The application firstly cuts the high-precision point cloud map of the complete large scene according to the graph, which is used for constructing the graph database of the complete large scene; then dynamically loads the graph around the current position of the robot according to the graph database, which is used for composing the graph map of the local large scene, and is used for real-time calculation of the position of the current robot in the large scene; and according to the current robot position and the predicted action path, it is decided which graphs in the graph database are switched to at present, and the graph map of the local large scene is updated in real time; the robot can realize fast and real-time graph switching in the super large scene without feeling, and accurate 3D space positioning can be realized on the hardware device with limited computing power.

This invention discloses a small target tracking method based on image enhancement and attention mechanisms, comprising: performing adaptive histogram equalization on shallow feature maps to enhance local contrast, and fusing with deep semantic features to construct a multi-scale feature pyramid; generating attention weight maps in the horizontal and vertical directions through a coordinate-decoupled attention module to highlight the salient regions of small targets; introducing a dynamic anchor box generation algorithm based on Gaussian distribution, dynamically sampling and generating anchor boxes based on a Gaussian distribution model constructed from the target position in the previous frame; and using the deep cross-correlation layer of a Siamese network to calculate the similarity response map between the template and the search region to determine the center coordinates and bounding box of the small target. This invention effectively solves the problems of insufficient feature extraction, low spatial positioning accuracy, and inflexible anchor box generation in existing methods, significantly improving the accuracy and robustness of small target tracking.

The application provides a kind of virtual and real interactive intelligent decision-making method and system, related to engineering safety supervision technical field.The application realizes the deep integration of video intelligent analysis and GIS geographic information system by constructing the whole process link of real-time video stream acquisition, abnormal event intelligent detection, space coordinate mapping conversion, geographic element automatic association, GIS spatial analysis, intelligent decision-making output, and links video sensing data and geographic space data.The application replaces the cumbersome process of traditional manual positioning to execute spatial analysis, compresses the pre-cycle of emergency response, and improves the efficiency of emergency decision-making;Through the accurate spatial positioning of abnormal events and the automatic matching analysis of surrounding road network, water conservancy facilities, risk avoidance points and other geographic elements, the problem of incomplete understanding of key elements and high error rate in manual analysis is solved, providing accurate virtual and real fusion data for emergency decision-making, improving the accuracy and rationality of emergency decision-making plan, and realizing rapid response and accurate disposal in high-risk emergency scenarios.

The invention discloses a spatial positioning method based on signal strength indication, which relates to the technical field of wireless positioning and comprises the following steps of: 1, constructing a positioning network comprising positioning nodes and positioning base stations; 2, the central positioning base station communicates with other common positioning base stations, and meanwhile, the central positioning base station sets space coordinates and communication channels of all the positioning base stations; step 3, the positioning base station immediately feeds back the data to be sent to the positioning node when receiving the position requirement data of the positioning node; and step 4, the positioning node exchanges data with the nearest positioning base station through the channel n. According to the method, the antenna design is optimized, the RSSI positioning precision is further improved, a foundation is laid for data exchange between the positioning node and the positioning base station through the optimized design of the RSSI algorithm, and under the condition of integrating factors such as cost and power consumption, certain positioning precision is guaranteed, and real-time data uploading is achieved.