544 results about "Spatial encoding" patented technology

The present disclosure provides methods and assay systems for use in spatially encoded biological assays, including assays to determine a spatial pattern of abundance, expression, and / or activity of one or more biological targets across multiple sites in a sample. In particular, the biological targets comprise proteins, and the methods and assay systems do not depend on imaging techniques for the spatial information of the targets. The present disclosure provides methods and assay systems capable of high levels of multiplexing where reagents are provided to a biological sample in order to address tag the sites to which reagents are delivered; instrumentation capable of controlled delivery of reagents; and a decoding scheme providing a readout that is digital in nature.

The invention belongs to the technical field of marine environment prediction, and discloses a ship navigation sea wave dynamic space-time prediction method and system based on deep learning. The method comprises the following steps: carrying out space-time alignment, missing value repair and standardization processing on acquired ship AIS data and an ERA5 reanalysis data set, and generating node feature vectors containing latitudes and longitudes, timestamps, wind speeds and significant wave heights; through node feature coding, centrality coding, space coding and time coding, ship trajectory node importance and time-space interaction relation are quantified. Constructing a SeaGraph model, and outputting an effective wave height prediction value of a target waypoint; and performing verification. According to the method, the space-time constraint of a traditional static modeling framework is broken through, the advantages of a self-attention mechanism and a graph network are integrated, the predictive modeling capability of dynamic evolution of a wave field in front of a ship navigation track is enhanced, and high-precision sea wave forecasting support is provided for intelligent ship navigation under complex sea conditions.

The invention relates to the technical field of people flow prediction, and discloses an urban people flow prediction method based on an urban point-of-interest spatio-temporal data set, which comprises the following steps: acquiring the urban point-of-interest spatio-temporal data set; performing multi-modal feature dynamic fusion by adopting a hierarchical perception attention fusion mechanism to obtain a point-of-interest feature vector; performing spherical spatial dependency modeling by adopting a regional adaptive spherical convolutional coding method to obtain a spatial coding vector; using a periodic mask generator to carry out business time constraint coding to obtain a time feature vector; and according to the interest point feature vector, the space coding vector and the time feature vector, space-time coupling features are extracted, regional perception time sequence modeling is performed on the space-time coupling features, and a people flow prediction value and crowding degree grade probability distribution are generated through multi-task sharing prediction. According to the urban people flow prediction method based on the urban interest point spatio-temporal data set, the urban people flow prediction performance is improved.

The invention discloses a time-space interaction vehicle trajectory prediction method based on speed perception, and the method comprises the steps: obtaining driving trajectory data of a target vehicle and surrounding vehicles in a perception range of the target vehicle, inputting the driving trajectory data into a pre-trained trajectory prediction model, and obtaining the trajectory data of the target vehicle in a prediction time period; the trajectory prediction model comprises an encoder module, a speed sensing interaction modeling module and a decoder module; in the encoder module, a spatial-temporal feature encoder is used for obtaining spatial-temporal feature codes based on the driving tracks of the target vehicle and other vehicles; the scene perception encoder is used for extracting a global spatial dependency relationship between vehicles to obtain a scene perception spatial code; the speed perception interaction modeling module is used for extracting space-time interaction features and scene interaction features of the target vehicle and surrounding vehicles by using a multi-head attention mechanism based on the space-time features and scene perception space codes, and further obtaining global interaction features; and the decoder module is used for obtaining a driving track of the target vehicle in the prediction time period based on the space-time interaction characteristics and the global interaction characteristics. According to the method, the vehicle-scene spatial dependency can be accurately captured, and the trajectory prediction accuracy is improved.

The invention discloses a city-level CIM scene streaming rendering method based on multi-modal fusion, and the method comprises the following steps: S1, collecting city data, and carrying out the preprocessing of the city data; s2, establishing a cross-modal entity alignment relationship, and constructing a city object structure body; s3, dividing a space by adopting a quadtree method, generating rendering sub-blocks and distributing a unique space code; s4, binding the rendering sub-blocks with the urban object structure body, constructing a multi-modal fusion network, and generating a multi-modal rendering unit set; s5, constructing an inter-frame rendering cost function, and scheduling and sequencing the multi-modal rendering unit set to form an inter-frame scheduling sequence; s6, loading the rendering units according to the inter-frame scheduling sequence to form a city-level three-dimensional view of continuous frames; and S7, updating the scheduling sequence, dynamically adjusting the loading sequence, and realizing stable and efficient streaming rendering of the city-level CIM scene. According to the method, the fusion efficiency and rendering performance of the urban large-scale three-dimensional data are improved, and the method has remarkable engineering applicability and popularization value.

The invention belongs to the technical field of power system load prediction, and relates to a long-range multivariable load prediction method and system based on time-frequency domain collaboration, and the system carries out the normalization and stabilization of a multivariate load time sequence through a data preprocessing module; the feature embedding module performs linear embedding on the block sequence to construct high-dimensional feature representation; the state space coding module extracts long-range dependency features and generates depth time sequence representation; the decoding prediction module maps the coding features into a preliminary prediction sequence; the time sequence alignment module identifies a leading-lagging relation among multiple variables and aligns a time sequence; the frequency domain optimization module realizes frequency domain component fusion based on adaptive filtering; and the model training optimization module is used for performing training and optimization through a signal attenuation loss function. The method can effectively improve the precision and robustness of long-range multivariable load prediction, and especially has obvious advantages in the aspects of processing complex dependency relationships and dynamic time delay.

The invention discloses a patterned wafer defect detection system and method based on double-optical-comb space-time coding, and the system comprises a double-optical-comb light source module, a space coding illumination module, a time programming control module, a signal collection and processing module, and a data analysis and processing module. And the data analysis processing module receives and analyzes the restored three-dimensional shape information of the surface of the wafer to be detected, extracts defect related characteristics, establishes a mapping relation between spectral characteristics and defects, and identifies the defects on the surface of the wafer to obtain corresponding defect types and position information. According to the invention, a wafer defect detection technology based on double-optical-comb space-time coding is adopted, rapid scanning and parallel detection are realized by using a double-optical-comb interference technology, the detection efficiency is greatly improved, and absolute distance information of the wafer surface can be obtained. The result is fed back to the precise air floating motion platform, so that an automatic focusing optical path system and a feedback control part can be omitted, the detection efficiency is improved, and the size, complexity and cost of the whole wafer detection system are reduced.

The invention relates to a method for constructing a space-time multi-task prediction large model based on data fusion. The method comprises the steps that firstly, a data fusion module is constructed, the data fusion module comprises a time sequence alignment sub-module, a region fusion sub-module and a feature splicing sub-module, and original data sets are fused based on the three dimensions of time, space and features; then, constructing an input embedding module, and extracting time features of the data set while embedding the fused data set into a high-dimensional space; constructing a spatial coding module, and extracting spatial features of the data set; then, a base large model prediction module is constructed, and reasoning prediction is carried out; and finally, designing a multi-task output module, and performing multi-task splitting on a prediction result to obtain a space-time multi-task prediction large model. According to the method, multiple tasks can be predicted at the same time, the multi-task prediction capability of the model is improved, the calculation cost and deployment cost are reduced, and the method has great advantages in an urban traffic comprehensive scene needing quick response.

The invention relates to the field of ocean data buoy data quality control, and discloses a buoy meteorological data restoration method based on a space-time double-attention neural network, and the method comprises the following steps: data collection and preprocessing; establishing a space-time double-attention neural network model, wherein the model comprises a time coding link, a space coding link and a feature fusion layer; model training: inputting the time features containing the target elements to be predicted into a time coding link, inputting the meteorological elements into a space coding link, and outputting the target elements at the moment to be predicted by the model; after model training, evaluating the model by using the test set; and predicting a vacancy value or an abnormal value in the multi-dimensional meteorological element data acquired in real time by using the model which is qualified in evaluation, so as to realize data restoration. According to the method disclosed by the invention, time and space constraints are effectively combined, and abnormal values and vacant values in the meteorological data are predicted and replaced, so that the meteorological data are repaired.

The invention discloses an open set hyperspectral image instance graph construction method based on a distillation diffusion model, and the method comprises the following steps: extracting the spatial and spectral features of a target domain HSI through a spatial encoder and a spectral encoder, executing forward diffusion in a diffusion classifier by taking a real label as a starting point, and carrying out the condition reverse denoising, and finally, closed set category prediction is obtained. A single sample prediction set is generated through Monte Carlo sampling, a prediction variance is calculated, uncertainty distribution is constructed, an unknown class probability is calculated by using a cumulative distribution function, and finally, a known class probability and an unknown class probability are fused to generate teacher open set prediction through a mapping module. A K neighbor graph structure of a sample is constructed in a feature space, teacher open set prediction and student open set prediction are respectively used as nodes to construct a teacher / student instance graph, a Wasserstein distance of prediction probability distribution of the teacher / student instance graph and the student open set prediction is minimized through a graph isomorphic knowledge distillation module, and knowledge distillation based on prediction manifold alignment is realized.

The invention provides an event sequence prediction method based on a space-time diffusion generation network. Comprising two parts: an event sequence spatio-temporal feature extraction and de-noising diffusion module; the spatio-temporal feature extraction of the event sequence comprises an event space correlation modeling module, an event space correlation embedding module and an event time correlation learning and embedding module; the denoising diffusion module receives sequence time coding information and space coding information generated by the space-time feature extraction module, predicts time and space information of event occurrence by adopting a denoising diffusion model, and jointly learns space-time coding features by using a denoising network through a reverse denoising step to respectively obtain time and space attention output; and a de-noising result is obtained for multiple times through an iterative calculation method, and a space-time prediction result is finally generated. According to the scheme, the structural relationship and the spatial representation information between the sequence events are adaptively generated according to the spatial features of the sequence, and a better learning effect is achieved for different spatial distribution data.

The invention relates to the technical field of hyperspectral imaging, in particular to a single-pixel hyperspectral imaging method and device based on time domain stretching. The method comprises the following steps: broadband signal light passes through a sample to be detected to obtain two-dimensional image information and one-dimensional spectral information, a mask pattern is generated by using a spatial light modulation module, and spatial coding modulation is performed on the signal light carrying the sample information; the broadband signal light subjected to space coding is effectively collected through a multimode optical fiber, different space frequency components are homogenized, then time stretching is conducted through a time stretching module to achieve separation of different wavelengths in the time domain, and time domain information of signal pulses is recorded through a single-pixel detector; time is mapped back to a frequency spectrum through the accurate corresponding relation between the signal light frequency and the time delay amount, and a hyperspectral image is obtained according to the known mask sequence and the signal intensity corresponding to different wavelengths in combination with an association algorithm. According to the technical scheme, high-spectral imaging with high speed, high spectral resolution and high sensitivity can be realized.

The invention discloses a traffic scene image data analysis and target segmentation method and device, and the method comprises the steps: enabling an input image of a traffic scene to obtain a target segmentation result through a traffic scene object segmentation model Tramba; the traffic scene object segmentation model Tramba comprises an S-stage visual state space encoder, S-1 double-frequency-domain visual state space modules, S-1-stage spiral visual state space decoders, an up-sampling module and a segmentation head. And the S-1 double-frequency-domain visual state space modules are used for performing high-frequency and low-frequency frequency domain enhancement on the RGB features of the first S-1 layers through a shift window scanning mechanism and an expansion scanning mechanism respectively and then connecting the RGB features to the input end of the spiral visual state space decoder of the corresponding level in a jumping manner. The invention aims to solve the problems that perception of a scanning mechanism in an existing Mama model on image details and a global structure needs to be enhanced and deep understanding on a traffic scene is lacked.

A pharmaceutical hyperspectral reconstruction method based on a coded aperture snapshot spectral imaging (CASSI) system includes: collecting and processing original pharmaceutical hyperspectral images to obtain augmented pharmaceutical hyperspectral images; performing simulated spatial encoding on the augmented pharmaceutical hyperspectral images to obtain encoded measurement images; performing spectral inverse shift on the encoded measurement images, then performing inverse encoding to obtain inversely encoded three-dimensional hyperspectral images, using the augmented pharmaceutical hyperspectral images as target images, and constructing a training set and a testing set according to the inversely encoded three-dimensional hyperspectral images and the target images; constructing a deep symmetric neural reconstruction network, and training and testing the deep symmetric neural reconstruction network; and deploying a tested deep symmetric neural reconstruction network onto the CASSI system, real-time collecting pharmaceutical measurement images using the snapshot coded imaging system, and performing computational reconstruction on the pharmaceutical measurement images to obtain reconstructed three-dimensional hyperspectral images.

A system and a method for single-shot compressed optical-streaking ultra-high-speed imaging, the system comprising a spatial encoding module spatially encoding the transient event with a binary pseudo-random pattern into spatially encoded frames; a galvanometer scanner temporally shearing the spatially encoded frames; and a CMOS camera receiving the temporally sheared spatially encoded frames, during one exposure time of the camera, for reconstructing the transient event. The method comprises spatial encoding a transient event; temporal shearing resulting spatially encoded frames of the event, spatio-temporal integration, and reconstruction.

The invention provides an image coding method and device based on deep learning, and belongs to the field of image coding, and the image coding method comprises the steps: a coding end uses nonlinear transformation to project an original image to a hidden space; the coding end performs probability distribution estimation by using a causal context; the encoding end encodes the hidden variables by using an entropy encoding technology; the decoding end performs probability distribution estimation by using a causal context; the decoding end decodes the hidden variables by using an entropy coding technology; the coding end reconstructs the hidden variables into an original image by using nonlinear transformation; and performing end-to-end reverse gradient propagation optimization on an image coding and decoding process by using a distortion loss function and a causal context adjustment loss function. By using a better causal context to adjust a loss function and a more efficient network structure, better image compression capability and higher rate distortion performance are obtained.

The invention discloses a peasant household credit assessment method and system based on multi-modal data federated learning, and relates to the technical field of computer intelligence, and the method comprises the steps: constructing a dual-channel representation mode of land assets based on GIS space coding and property and age limit information, and generating multi-dimensional feature input of peasant household production capacity and asset conditions in combination with multi-source data; a three-level federated learning structure is constructed, data is safely exchanged through TLS communication encryption and equipment identity authentication, and differential privacy noise is injected; a Top-K gradient selection algorithm is adopted to carry out parameter sparse transmission, a sparse rate is dynamically adjusted in combination with a round increasing strategy, a grouping sparse convolution and parameter freezing mechanism is introduced, and a federal deep learning model is compressed and an adaptive edge is deployed through 8-bit distillation quantification; according to the method, multi-modal feature construction, layered federated training and a privacy enhancement mechanism are fused, and fine modeling, lightweight deployment and data security collaboration of peasant household credit assessment are realized.

The invention belongs to the technical field of computer vision, and relates to a remote sensing target detection method and system based on multi-scale feature fusion and a medium, and the method comprises the steps: obtaining a remote sensing image, inputting the remote sensing image into a bottom-to-top feature extraction backbone network, and obtaining a multi-level feature map Ck; inputting the Ck into a multidirectional feature strengthening network to obtain a multi-scale feature map Yk; inputting the Yk into a feature extraction backbone network from top to bottom to obtain a multi-scale fusion feature map Pk; inputting the Pk into a target detection network for target detection to obtain a detection result; the multidirectional feature enhancement network performs all-directional frequency domain information enhancement on each level of feature map, performs feature enhancement of different scales on frequency domain enhancement features, and performs bidirectional space coding and feature weight optimization on H and W dimensions by using a coordinate attention mechanism. Multiple information of frequency domains and space domains of low dimensions and high latitudes of the feature layer is comprehensively concerned, the detection capability of small targets is effectively improved, and the capability of coping with complex backgrounds is enhanced.

One embodiment provides a compressive hyperspectral imaging system. The compressive hyperspectral imaging system can include a coded aperture configured to spatially encode an optical signal associated with a scene, an integrated photonic device configured to disperse the spatially encoded optical signal, and an array of photo detectors configured to detect the dispersed and spatially encoded optical signal. The output of the array of photo detectors is used for reconstruction of a hyperspectral image corresponding to the scene.

The invention discloses a terminal node security management method using a block chain technology, and particularly relates to the application field of a block chain in mobile terminal dynamic access and behavior management, and the method comprises the steps: collecting the real-time positioning information of a mobile terminal node, inputting the positioning information into a space coding function, and generating a unique space identifier; querying a chain domain mapping table by using the space identifier, wherein the chain domain mapping table records a binding relationship between the space identifier and the chain domain identifier; the returned chain domain identifier is used as an on-chain home domain identifier of the current terminal node in the block chain system; and comparing the current chain domain identifier with the chain domain identifier recorded last time. By constructing an on-chain affiliation binding mechanism based on spatial coding and chain domain mapping, dynamic chain domain redirection when the physical position of a terminal node jumps is realized, and the problems of fuzzy terminal affiliation and identity breakage under a traditional block chain architecture are effectively solved, so that on-chain identity continuity under spatial drift is guaranteed.

The invention discloses a traffic flow prediction method based on a time-space synchronization embedded graph Transform model. The method comprises the following steps: acquiring historical traffic flow sequence data of a target area and preprocessing the historical traffic flow sequence data; defining a traffic network of a target area as an undirected graph, calculating a static adjacent matrix based on a graph structure, and obtaining spatial coding features through a full connection layer; extracting original traffic features through a full connection layer based on the preprocessed data; embedding the characteristic representations of the week cycle, the day cycle and the timestamp through a full connection layer to obtain week cycle, day cycle and timestamp characteristics; based on a space-time adaptive embedding mechanism, obtaining adaptive space-time features; hidden space-time representation is obtained through feature splicing; extracting space-time dependence characteristics of traffic flow along a time dimension and a space dimension through a double-channel attention mechanism to obtain enhanced space-time characteristic representation; and generating a traffic flow predicted value of each node in a future time period through a full-connection output layer. According to the invention, the traffic flow prediction precision is improved.

The invention discloses a spatio-temporal data compression method and system based on lightweight processing, and relates to the technical field of image data processing, and the method comprises the steps: based on spatio-temporal data to be compressed, establishing standardized data entries, forming a window set through time axis segmentation, carrying out the periodic discrimination of windows according to the frequency domain energy distribution, and constructing spatio-temporal blocks, establishing an error control parameter table based on the global error budget; generating a time coding stream, performing integerization on the three-dimensional coordinates and the attribute values, generating a space coding stream through double difference and run length coding, performing edge folding lightweight and texture compression on the grid data, and generating a joint coding result; and based on a joint coding result, dividing the compressed data into a base layer and a plurality of enhancement layers, and establishing a relevance hierarchical storage structure and a block-level index table to generate the compressed data capable of being transmitted in a streaming manner. According to the method, collaborative compression can be carried out by utilizing spatial-temporal data internal relevance.

The invention relates to the technical field of automatic driving, in particular to a multi-view-angle perception-driven automatic driving test scene twin reconstruction method, which comprises the following steps of: S1, acquiring and preprocessing multi-view-angle image perception data to form a multi-view-angle image sequence under a unified coordinate system; s2, spatial coding and modeling based on an orthogonal three-plane structure; s3, on the basis of a view perception-space interaction encoder of a Transform architecture, completing space coding processing represented by an orthogonal three-plane structure; s4, structure and semantics combined twinborn reconstruction decoding is carried out; and S5, generating a simulation compatible format and deploying a platform. According to the method, the terrain, facilities, traffic elements and other contents of a real road scene can be automatically restored, the storage and calculation cost is remarkably reduced while the spatial information integrity is ensured, various simulation platform support formats can be flexibly output, the method serves for closed-loop testing of automatic driving, and the reconstruction efficiency and the system suitability are improved.

The invention discloses a planning and navigation method and system for a puncture robot. The method comprises the steps that a preoperative high-resolution image and ultrasonic simulation data are registered through a two-channel convolutional neural network, high-precision space alignment is carried out, and multi-modal fusion body image data Ifuse are generated; carrying out topological structure analysis on the multi-modal fusion body image data Ifuse, and carrying out semantic enhancement three-dimensional model construction; based on the semantic enhancement three-dimensional model, performing global path planning through obstacle space coding and reinforcement learning pre-planning; according to the global path planning result, navigation parameters and a lightweight model capable of being imported into a navigation system are generated, and preoperative navigation data are output. The system comprises a two-channel convolutional neural network registration module, a semantic enhancement three-dimensional model construction module, a global path planning module and a navigation data encapsulation module. According to the invention, the precision and stability of registration are improved, the problem of multi-modal image data fusion can be effectively solved, and accurate preoperative navigation data output is realized.

The invention discloses a continuous variable quantum key distribution method, device and system, and belongs to the field of wireless communication. Firstly, a sending end generates continuous light pulses for discrete coding, a one-dimensional discrete modulation signal is generated, quantum signal light is generated through an attenuator, and the quantum signal light is coupled to a quantum channel for transmission. Then, a receiving end adopts a homodyne detector to measure the regular component of the quantum signal in a self-calibration manner; and after a local measurement base direction and a phase drift angle introduced by a dynamic compensation quantum channel are adjusted in real time, a covariance matrix is calculated and input into a post-processing module, the code rate of the system is further calculated, and a security key is generated by using a protocol between a receiving end and a transmitting end. According to the device, the intensity modulator is connected between the laser and the attenuator, so that one-dimensional discrete modulation is realized, and dependence on a phase reference signal is avoided. According to the system, a traditional phase locking module is removed, an asymmetric phase space coding mechanism is adopted, dependence on a phase reference signal is eliminated, and the safety and stability of the system are improved.

The invention discloses an underwater image enhancement method based on dual-path feature decoupling and gating fusion. The method comprises the following steps: firstly, constructing a training data set; then, an encoder-bottleneck layer-decoder is used as a trunk, and a dual-path encoding and decoding mechanism is adopted to construct an underwater image enhancement model; the encoder is used for extracting direction encoding features and space encoding features; the bottleneck layer is used for extracting global and local features and fusing and outputting bottleneck features; the decoder is used for decoding and multi-scale refinement so as to reconstruct and obtain an underwater enhanced image; then training is carried out to obtain a trained underwater image enhancement model; and finally, the device is deployed to acquire the underwater image in real time for underwater image enhancement. According to the method, independent modeling is carried out for anisotropic scattering / edge attenuation and spatial non-uniform atomization / local brightness imbalance, and mutual interference of different degradation causes in the same feature space can be reduced.

The present disclosure is directed to a traffic signal apparatus communication system and methods of communicating traffic information to vehicles using same. The traffic signal apparatus communication system includes a traffic signal apparatus for providing a message to a vehicle. The apparatus includes at least one spatially encoded marker, and the vehicle is configured to receive returns of a radar signal from the spatially-encoded marker. At least one controller of the vehicle is configured to determine the message encoded by the spatially-encoded marker based on the returns and to control the vehicle based on the message. The message may include a value indicating a time to a transition of a new state of the traffic signal apparatus, where the new state includes emission of light from one of a first light source, a second light source, or a third light source of the traffic signal apparatus.

The invention relates to a hyperspectral de-mixing method based on a double-branch cross-weighted network, and the method comprises the following steps: obtaining a hyperspectral multi-mode data set which comprises hyperspectral image data and laser radar data, and inputting the hyperspectral image data and the laser radar data into the double-branch cross-weighted network; processing the hyperspectral image data through a spectral encoder to obtain primary spectral features; a spatial encoder processes laser radar image data to obtain primary spatial features, the primary spectral features and the primary spatial features are input into an adaptive feature selection module to be processed to obtain precise spectral features and precise spatial features, and the obtained precise spectral features and precise spatial features are added to be fused; and obtaining an abundance coefficient graph and an end member from a fused result through an activation function, and obtaining a reconstructed hyperspectral image by using a decoder. According to the invention, the unmixing effect of different substances of similar materials in a multi-modal scene can be improved.

The invention discloses a ship path search and arrival time prediction method based on AIS historical data. The method comprises the following steps: S1, setting grid feature codes of AIS data; s2, constructing an AIS track network and weights; and S3, searching and simplifying a ship path. The method has the advantages that the abnormal data is removed based on the geographic space features and the track features, and the quality of ship data and the path searching effect in the global range are improved. An AIS data gridding method based on a Google S2 spatial coding principle is provided, and the grid size is intelligently planned according to the density of AIS data. The dynamic weight strategy is introduced into the edge weight endowing of the AIS trajectory network, the weight is dynamically adjusted according to the daily density condition of the ship, the method adapts to the change of ship distribution in different time periods, and the path search accuracy is improved. A directed path search optimization method based on a Bellman-Ford algorithm and a path trajectory simplification method based on Kalman filtering are provided, the efficiency of the path search algorithm is improved, and efficient expression of the path trajectory is achieved.

The invention discloses an indoor high-precision positioning system and method based on a UWB technology, and particularly relates to the technical field of positioning, and the system comprises a region division module which divides an indoor space into N dynamic region units based on the structural layout of a building and the coverage range of UWB anchor nodes; establishing a bidirectional mapping relationship between the region and the UWB tag by taking the key building nodes as boundaries; each area unit is associated with moving track data of the label, a space encoder is introduced to generate an area feature vector, and an original three-dimensional coordinate is obtained to represent the positioning characteristic of the area; according to the method, multi-source data of the UWB signal layer data, the motion sensing layer data and the environment sensing layer data are analyzed and processed, the superposition error of environment reflection and hardware deviation is effectively reduced, and the geometric convergence precision of position calculation can be remarkably improved in a complex space.