198 results about "Spatiotemporal encoding" patented technology

The invention relates to the technical field of data processing, and discloses a comprehensive management method and system for a smart park, and a storage medium. The method comprises the steps that environment, energy consumption and people flow data are collected through the Internet of Things, and a basic data set is obtained through edge node processing and semantic alignment; constructing a digital twin system by using a spatio-temporal data cube and a graphical mode; constructing a land digital identity system by adopting space-time coding and block chain technologies; performing anomaly identification and risk analysis on the activity data to obtain supervision early warning data; performing multi-dimensional analysis on the enterprise operation data to form an enterprise portrait; and presenting resource distribution through mixed reality, and analyzing cross-department data to obtain operation efficiency data. According to the invention, holographic perception, intelligent supervision and cooperative service of park elements are realized.

The invention discloses a cross-source data three-dimensional reconstruction method and system based on improved Gaussian sputtering, and the method comprises the steps: collecting an unmanned plane inclined image and a ground panoramic image of a target region, and constructing a time-space correlation data set; based on multi-view geometric constraints, space-time coding matching point pairs are established through an adaptive feature pyramid, intelligent incremental cross-source data sparse reconstruction is carried out, and point cloud and camera parameters are output; adopting improved Gaussian sputtering, compressing a three-dimensional Gaussian kernel into a two-dimensional Gaussian primitive through double tangent vector constraint, and fitting surface geometry to realize multi-scale reconstruction; and optimizing primitive parameters by using a differentiatable renderer, completing multi-scale fine reconstruction through gradient back propagation, and generating a high-precision three-dimensional model. According to the method, multi-scale accurate geometric prior input and accurate camera poses are provided for three-dimensional reconstruction, the dependence on professional manual operation in a traditional three-dimensional reconstruction method is greatly reduced, and meanwhile, the geometric accuracy and visual fidelity of a reconstruction result are remarkably improved.

The invention provides a cellular network flow space-time prediction method and system based on a hybrid neural network, and belongs to the technical field of intelligent communication. The method adopts a layered deep neural network architecture, and comprises a data embedding layer, a space-time coding layer, a feature fusion layer and an output layer. The data embedding layer maps a historical traffic sequence, cross-domain external data and metadata into high-dimensional features; the space-time coding layer is used for respectively fusing one-dimensional causal convolution and a Mama neural network to extract multi-scale time features and densely connecting convolution and a multi-head attention mechanism to capture multi-scale space features through time and space modeling branches; the feature fusion layer realizes adaptive weighted fusion of spatial-temporal features, cross-domain features and metadata features by using a gating fusion mechanism; and the output layer performs linear transformation on the fusion features to generate a final prediction result. According to the method, the spatial-temporal dynamic capture of the service traffic is accurate, the prediction curve is highly fit with the true value, and the accurate prediction of the multi-service traffic of the cellular network is realized.

The invention relates to the technical field of artificial intelligence processing, in particular to a manufacturing and processing system based on artificial intelligence simulation control parameters. According to the manufacturing and processing system, three-dimensional space-time coding is carried out on vibration, temperature and deformation time-varying signals through a multi-mode sensing module; generating a composite feature body including an equipment rigidity distribution matrix, a thermal deformation gradient vector and a material residual stress tensor, and solving the problems of spatial-temporal asynchronization and physical field feature splitting of multi-source data; the parameter resolving module constructs a parameter incidence matrix based on a fractal neural network, combines dynamic constraint conditions of thermal deformation gradient vectors, performs multi-objective optimization on a rigidity distribution matrix and residual stress tensor, generates an initial parameter solution set for eliminating time-lag deviation, and breaks through the nonlinear coupling optimization bottleneck of a traditional static model; and the state dissociation module calls a historical wear feature library to construct a performance attenuation reference surface, and tool accumulated wear and instantaneous thermal deformation disturbance are synchronously inhibited through convolution operation and projection correction of a dynamic compensation factor.

The invention relates to a traffic flow prediction method based on multi-channel space-time interaction and fine-grained graph convolution, and the method comprises the steps: carrying out the processing of traffic flow data, a time label and a self-adaptive space-time feature through a space-time feature embedding module, and carrying out the extraction of a time sequence feature and a space feature from a high-dimensional embedded feature through a coding module; the encoding module comprises a multi-channel space-time encoder; performing spatio-temporal feature fusion on the time sequence features and the spatial features by adopting a bidirectional spatio-temporal interaction fusion module; and performing feature extraction on the space-time fusion feature, the traffic flow data, the time label and the adjacent matrix by adopting a fine-grained space-time diagram convolution module, and then performing prediction by adopting a prediction module to obtain a traffic flow prediction result. According to the method, multi-scale time and space features are extracted by using a multi-channel space-time encoder, global space-time dependence is constructed by fusing attention through bidirectional space-time interaction, a fine-grained dynamic space-time relationship is captured by using a dynamic and static graph combined graph convolutional network, and the accuracy of traffic flow prediction is improved.

The invention discloses a multi-modal content intelligent auditing and violation detection method and system, and relates to the technical field of information processing. The method comprises the following steps: carrying out audio-picture separation on a video stream, and carrying out parallel processing on audio-to-text and visual key frame extraction; a space-time encoder is constructed to record the corresponding relation of the time stamps of all the modes; constructing a multi-modal resource target dictionary, and forming an inter-entity knowledge graph by using relation categories; calculating a confidence coefficient difference index between modals by comparing, learning and training the shared semantic space; and carrying out violation judgment, triggering a sensitive characteristic threshold value for any mode, and starting multi-mode evidence cross validation. According to the method, audio and picture separation is carried out on the video stream, parallel processing is carried out on audio-to-text and visual key frame extraction, a multi-modal resource target dictionary is constructed, violation judgment is carried out according to confidence coefficient difference indexes among modals, and false information auditing efficiency and detection efficiency are improved.

The invention relates to the technical field of data analysis, in particular to a city physical examination accurate analysis method and system based on AI multi-modal data collaboration, and the method comprises the following steps: constructing a self-adaptive fractal space-time grid, and mapping a city multi-modal data source into a space-time coding vector with a fractal dimension; establishing a physical field driven cooperative resonance network, and extracting an abnormal cooperative mode exceeding a normal resonance threshold; calculating a modal entropy chain value according to the real-time metabolic rate of the urban system, and generating an optimal weight matrix through a non-equilibrium thermodynamic model; and fusing the abnormal cooperation mode and the dynamic weight matrix, and generating a space-time causal map for displaying a fault source and a propagation path by using a causal discovery algorithm. According to the method, the multi-modal data collaboration and causal analysis technology is utilized, the emergency response capability of an urban system in the case of sudden failures is improved, the stability of urban operation is guaranteed, and the space-time causal atlas provides comprehensive visual information for the failure propagation process.

The invention belongs to the technical field of power system application, and particularly relates to an intelligent power flow data filling method based on space-time coupling. The invention provides an intelligent power flow data filling method based on space-time coupling, which comprises the following steps: firstly, carrying out data preprocessing on original data through Hampel filtering, phase alignment and normalization, constructing a space-time encoder through joint coding of adaptive graph convolution (Adaptive GCN) and attention LSTM, capturing space-time coupling characteristics, and carrying out data processing on the space-time coupling characteristics; a multi-physical constraint generative adversarial network (Pry-GAN) is designed through generator physical constraints and a multi-scale discriminator to realize data filling, and it is ensured that a filling result has both numerical reasonability and physical feasibility.

The invention belongs to the technical field of three-dimensional oil reservoir seepage, and particularly relates to a coupled neural operator fusing Fourier space-time coding and multi-field attention. The neural operator realizes joint prediction and efficient simulation of key physical quantities such as pressure, oil phase saturation and water phase saturation. The problems of calculation bottleneck, weak generalization ability and the like of a traditional method in the aspects of processing high-dimensional space input, time sequence evolution and physical coupling field modeling are solved. According to the coupling neural operator, a coupling correlation mechanism between a physical field and time and space is introduced through a multi-representation structure and attention enhancement, so that the cooperation efficiency between operator tasks, the space-time information utilization rate and the expression ability of a model to complex flow field interaction behaviors are effectively improved; the method has the characteristics of fast overall reasoning, low persistence cost, fast and stable optimization process convergence and the like. Fourier high-dimensional spatial-temporal feature mapping is fused, so that the model has higher spatial-temporal change sensing ability, and the modeling stability of the time evolution process is improved.

The invention provides a calcium imaging neural signal extraction method and product based on self-supervised pre-training, and relates to the technical field of artificial intelligence. Based on a pre-training data set containing a large amount of unlabeled two-photon calcium fluorescence imaging data, a pre-training decoding network and a corresponding pre-training task are self-supervised, and a pre-trained space-time coding network is trained by taking minimization of a pre-training loss function as a target; the method comprises the following steps of: firstly, establishing an encoder and decoder network, establishing fine-tuning data with accurate space-time marked neuron calcium signal positions through simulation, simultaneously performing fine-tuning on the encoder and decoder network by taking minimization of a fine-tuning loss function as a target, and checking the algorithm extraction precision based on various actual imaging data to obtain a calcium imaging signal extraction algorithm network with strong generalization performance. Therefore, a calcium signal extraction model which can be generalized in various imaging conditions and model biological brain regions can be obtained through training without manual labeling, a high-accuracy neural signal extraction result is obtained, the application difficulty that a deep learning model lacks data generalization ability is overcome, and the accuracy of a neural signal extraction result is improved. Calcium imaging neural activity analysis based on self-supervised pre-training and wide in application is achieved.

The invention relates to the technical field of information, and discloses a digital right near-field real-time cancel-after-verification system based on edge computing, which comprises a space-time coding module used for generating a unique cancel-after-verification identifier according to the geographic position and time of a user, and transmitting the identifier to a dynamic weight module; a dynamic weight module which is connected with the space-time coding module, generates a right and interest distribution strategy based on a federated learning model and an optimization algorithm, and outputs the optimized strategy to a block chain fragmentation module; the block chain fragment module is connected with the dynamic weight module, dynamically divides a consensus network according to the real-time cancel-after-verification density, executes distributed verification in fragments and generates a cancel-after-verification authorization instruction; and the cancel-after-verification execution module is used for distributing cancel-after-verification tasks according to the authorization instruction and synchronously updating the multi-node inventory state. By adopting the technical scheme of gridding coordinate mapping, time window division and nonlinear hash of the space-time coding module, the technical effect that the verification identifier is unpredictable and uniquely bound is achieved.

The invention discloses a cross-modal large model construction method and system based on track spatio-temporal characteristics, and belongs to the crossing field of artificial intelligence and dynamic spatio-temporal data processing, and the method comprises the steps: carrying out the sliding sampling and spatial distribution difference judgment through the semantic dynamic segmentation of multi-scale track spatio-temporal data, and generating spatio-temporal data blocks with consistent semantics; designing a space-time encoder of a hybrid architecture, extracting track time sequence association and spatial features, and unifying dimensions; constructing a text space-time fusion mechanism, and dynamically adapting cross-modal features by means of an anchor interface and gating fusion; a staged instruction fine tuning strategy is adopted, semantic alignment of space-time and text features is optimized firstly, then model top-layer parameters are trained cooperatively, complex scene adaptation is enhanced in combination with instruction difficulty progression and hard sample mining, and space-time constraint regular terms are introduced to guarantee output rationality. According to the method, high-precision cross-modal reasoning capability is provided for scenes such as track analysis and track prediction.

The invention relates to the technical field of food safety supervision, in particular to a food processing visual monitoring system and method based on order tracing, and the system comprises an intelligent collection terminal, a space-time coding server, a block chain evidence storage platform, an exception handling module and a user access terminal. The intelligent acquisition terminal comprises a multi-view camera array, a temperature sensor and an edge calculation module; the space-time coding server encrypts and binds order information with a three-dimensional space, and the block chain evidence storage platform stores video slices and associated metadata to generate a time sequence block chain record; the exception handling module automatically triggers order blocking and illegal evidence storage based on AI identification; and the user access terminal checks the video stream through the traceability code to implement dynamic privacy protection. Accurate tracing is realized through space-time encryption binding triggered by an order, edge calculation key frame extraction optimization storage and block chain evidence storage are combined, cross-system cooperative supervision is supported through dynamic privacy protection, and efficient, safe and low-cost food processing monitoring is realized.

The embodiment of the invention provides an image processing method and device based on computer vision and artificial intelligence, and the method and device achieve the privacy protection of data through innovatively designing a multi-dimensional data safety collection mechanism and through feature branch isolation and space-time coding. A hierarchical feature fusion network is constructed, and a safe and reliable target tracking system is established in combination with environmental constraints and physical laws. A dynamic weight adaptive mechanism is introduced, and the accuracy of a processing result and user privacy are ensured through multi-scale feature extraction and prediction correction. According to the method, the data security is protected, and meanwhile, the defects of the traditional technology in the aspects of data fusion, feature extraction, prediction constraint and the like are effectively overcome.

The invention discloses a multi-target radio frequency identification system based on a space-time coding metasurface. The multi-target radio frequency identification system comprises a space-time coding metasurface module, a baseband signal transmitting module, a received signal demodulation module, a signal preprocessing module and a target classification module, the space-time coding metasurface module adjusts the coding module in real time, dynamically regulates and controls electromagnetic waves, generates a plurality of detection channels, and realizes recognition scenes of various different targets; the baseband signal transmitting module generates and transmits a baseband signal, and after the baseband signal is modulated by the space-time coding metasurface module, a multi-order radio frequency signal containing the baseband signal is formed; the receiving signal demodulation module receives a radio frequency signal reflected from a target to be identified, extracts a specified harmonic wave and demodulates the radio frequency signal into a baseband signal; the signal preprocessing module transmits processed data of the demodulated signal to the target classification module; the target classification module uses a machine learning algorithm to classify and identify targets. According to the invention, simultaneous detection of multiple targets is realized, and the detection efficiency and speed are improved.

The invention discloses a multi-level electroencephalogram signal analysis method, device and equipment based on space-time coding. The method comprises the steps that multiple pieces of electroencephalogram channel data to be analyzed are acquired; dividing the electroencephalogram channel data into a parallel group or a series group according to channel information corresponding to the electroencephalogram channel data; extracting multi-scale time domain and frequency domain features from each electroencephalogram channel data of the parallel group, and constructing a first feature set according to the multi-scale time domain and frequency domain features; extracting a time sequence dependence feature from each electroencephalogram channel data of the series group, and constructing a second feature set according to the time sequence dependence feature; determining a primary feature set and a secondary feature set according to the first feature set and the second feature set; dividing the main feature set and the secondary feature set into row and column coding branches, and obtaining electroencephalogram signal feature representation according to the row and column coding branches so as to complete cognitive analysis according to the electroencephalogram signal feature representation.

The invention discloses a track user association method based on semantic perception and space-time coding, relates to the technical field of location service and user behavior analysis, and aims to solve the problems of excessive dependence of POI identifiers, limited space-time representation capability, insufficient cross-city generalization capability and the like of the existing TUL method in practical application. By introducing a pre-trained large language model to carry out POI category semantic coding, multi-frequency sine space-time coding and a double-flow transfer learning mechanism, the method can effectively improve the prediction precision and the model generalization ability.

The embodiment of the invention provides a terminal track generation method and device, a medium and a product, and relates to the technical field of mobile communication. The method comprises the following steps: determining mapping data between signaling and a position of a terminal; acquiring a time feature and a position feature in the mapping data by using a space-time encoder; and inputting the time feature and the position feature into a preset attention mechanism model, and determining the actual position of the terminal. According to the scheme provided by the invention, the dependence on a single base station signal is reduced through deep association of the terminal signaling and the position mapping data, and the dependence on the positioning precision of the base station is broken through; a space-time encoder and an attention mechanism model are utilized to realize multi-modal mobile modeling; based on signaling and an attention mechanism, semantic and base station interaction relation modeling is enhanced, and the precision of track production or track recovery is improved.

The invention provides a shale oil reservoir dessert intelligent identification method and system, relates to the field of shale oil reservoirs, and aims to obtain original logging data and extract curve deep space-time coding features by fusing a hybrid model of a convolutional neural network and a recurrent neural network. Then, multi-source logging features are dynamically fused through a cross-curve feature fusion algorithm, and spatial compression is carried out through feature principal component analysis; thirdly, implicit features strongly related to the geological signs of the sweet spots are screened through semantic level selection based on feature principal components, sweet spot probability prediction is achieved based on a lightweight classification model, an intelligent recognition closed loop without manual intervention is formed, and the dependence of a traditional method on expert experience is broken through; in this way, non-linear correlation in the logging curve is automatically mined through deep learning, and the interpretability and generalization ability of complex reservoir dessert recognition are improved.

The invention discloses a heterogeneous skeleton-based open vocabulary action recognition method and system. The method comprises the following steps of: constructing a heterogeneous open vocabulary skeleton data set; unifying the skeleton representation in the heterogeneous open vocabulary skeleton data set, and defining a unified space structure by establishing the maximum joint number and member number; a skeleton motion encoder model based on a Transform architecture is constructed, the skeleton motion encoder model comprises feature embedding, space-time encoding and a projection layer used for cross-modal alignment, and global time features, global space features and global visual features output by space-time encoding are subjected to cross-modal alignment through three parallel projection networks of the projection layer. Mapping to a semantic space aligned with text embedding from the pre-trained language model; training loss is constructed based on a multi-granularity motion-text alignment strategy including global instance alignment, stream specific alignment and fine granularity alignment, and a motion encoder model is trained. A wide range of experiments on a popular benchmark with heterogeneous skeleton data prove the effectiveness and generalization ability of the proposed method.

A technique is provided for tracking an object in a real-time time series of medical images. A method, performed by a downstream neural network, NN, includes receiving a real-time time series of medical images of a patient's anatomical region at an input layer of the NN. Using a spatio-temporal encoder, the real-time time series is encoded, and an encoded representation per frame is obtained. A frame corresponds to a medical image at a time instance within the real-time time series of medical images. Using a multi-head cross-attention, MCA, decoder, the encoded representation of a most recent frame is decoded. The MCA decoder correlates the most recent frame with a predefined number of preceding frames. An object is tracked. The tracking comprises determining coordinates of the object based on the decoded most recent frame.

The invention relates to a tropical cyclone scale monitoring method and device based on deep learning and space-time coding. The method comprises the steps of obtaining multi-source satellite image data and an international optimal path data set of the tropical cyclone, and performing preprocessing; performing space-time independent field coding based on the international optimal path data set to generate three-channel time scale field data reflecting annual change characteristics, seasonal change rules and evolution time recording characteristics of the tropical cyclones and two-channel space scale field data representing space position information of the tropical cyclones; and splicing with the three-channel satellite image data obtained by preprocessing to obtain a tropical cyclone multi-scale comprehensive feature data set, inputting the tropical cyclone multi-scale comprehensive feature data set into the trained TC-Resnet model to carry out tropical cyclone scale monitoring, and predicting to obtain a tropical cyclone multi-scale monitoring result at a target moment. By adopting the method, the spatial-temporal information of the tropical cyclone can be fully utilized to realize the scale high-precision monitoring of the tropical cyclone.

The invention relates to the technical field of carbon metering, and discloses a carbon metering system and calculation method based on edge calculation, and the system comprises an edge sensing module which comprises a plurality of carbon emission monitoring sensors; the data preprocessing module is used for cleaning and formatting the acquired carbon emission data; the space-time coding processing module is used for receiving the monitoring data and constructing a space-time constraint matrix by using wavelet transform and a Hermite matrix; the federation calculation module is used for carrying out dynamic model updating on the spatio-temporal characteristics; and the verification and certification module is used for receiving the final processing data, verifying the accuracy of the carbon measurement data in real time and storing the carbon measurement data through a block chain. According to the method, real-time monitoring and processing of the carbon emission data are achieved through the edge computing technology, the technical effect of timely and efficient data processing is achieved, and compared with a traditional centralized data processing method in the prior art, the problems that data transmission is delayed and the system burden is too heavy are solved.

The invention relates to a universal system and device for an intelligent unmanned aerial vehicle with a body based on a modular architecture, and the system comprises a multi-source sensing module which collects the current multi-source sensing data of the unmanned aerial vehicle; the space-time coding module is used for fusing the current multi-source sensing data into the same space-time to obtain current natural language description; the reasoning large model module is used for reasoning the current natural language description to obtain a current flight control instruction; the feedback verification module is used for verifying the current flight control instruction; and the action module is used for executing the current flight control instruction passing the verification. The space-time coding module is adopted to solve the technical problem of a perception-decision gap, the reasoning capability of the reasoning large model module is utilized, the feedback verification module is used, the defects of an existing large model are eliminated, and the unmanned aerial vehicle can rapidly have the intelligent capability and can be rapidly generalized to other fields.

The invention discloses a traffic flow prediction method and system based on a space-time large language model, and a medium. The method comprises the steps of obtaining to-be-predicted traffic flow data; constructing a space-time encoder based on the recurrent neural network and the graph neural network, and encoding the to-be-predicted traffic flow data through the space-time encoder to obtain a first space-time feature; performing feature alignment on the first spatial-temporal feature through an alignment module to obtain a second spatial-temporal feature, so that the spatial-temporal encoder and the large language model are aligned in dimension; and obtaining a traffic flow prediction result through a large language model according to the second spatial-temporal characteristics and the constructed action chain instruction. According to the method, the space-time encoder is designed to help the large language model to fully understand and mine complex space-time modes of different scenes, and then the action chain instruction and alignment module is introduced to relieve the illusion of the large language model, so that accurate space-time prediction under the frozen large language model can be realized; the traffic flow prediction method based on the large language model can be widely applied to the technical field of data processing.

The invention provides a leaky-wave antenna and a frequency-modulated continuous wave generation method based on the leaky-wave antenna, and relates to the field of millimeter-wave radar, and the method comprises the steps: constructing a space-time coding matrix, configuring a radiation unit state regulation and control beam direction in a space domain, modulating a radiation unit state switching rule in a time domain to generate a frequency-modulated continuous wave, and achieving space-time dual-domain regulation and control. And frequency-modulated continuous waves with controllable bandwidth are generated. The space-time coding leaky-wave antenna is formed by alternately arranging 1-bit radiation units in an upper row and a lower row, switching of radiation / non-radiation states is achieved by loading a synchronous bias PIN diode across an annular gap, a space-time coding matrix is constructed, the states of the radiation units are configured in a space domain to regulate and control the wave beam direction, and the space-time coding leaky-wave antenna is formed. Modulating the switching rule of the radiation unit in a time domain, and generating a frequency-modulated continuous wave; the radiation efficiency is improved by the alternative arrangement of the radiation units, the control difficulty and the hardware cost are reduced, multiple functions are integrated into a single device, and the problem of low integration level of the traditional separation architecture is solved.

This invention discloses a multimodal traffic flow prediction method based on multi-source data feature fusion, comprising: acquiring traffic data; dividing the traffic data into training and testing sets; constructing a traffic prediction model and training the traffic prediction model using the training set; and validating the traffic prediction model using the testing set. The traffic prediction model includes: a cloud map encoder for extracting features from cloud map data to obtain cloud map features; a spatiotemporal encoder for extracting features from spatiotemporal data to obtain spatiotemporal features; a fusion module for fusing cloud map features and spatiotemporal features to obtain fused features; and adding the cloud map features, spatiotemporal features, and fused features, inputting the sum to the spatiotemporal decoder for prediction to obtain the final prediction result. This invention integrates multi-dimensional and multi-faceted information, simplifies redundant information in the spatiotemporal feature extraction process, and achieves efficient and high-precision prediction, which can be widely applied in the field of traffic flow prediction technology.

The invention relates to a precise operation method and system for an explosion-proof spraying robot driven by space-time coding. The precise operation method comprises the steps that target geometric parameters and material attribute information of a to-be-sprayed three-dimensional curved surface are obtained; a space-time coding sequence generated based on the information; planning a motion track of the spraying robot according to the space-time coding sequence; in the spraying process, environment state data are collected in real time and input into a preset state correction model to be analyzed and processed; the current spraying operation is dynamically adjusted through the obtained spraying deviation compensation parameters; according to the scheme, the spraying accuracy and quality can be improved.

The invention relates to the technical field of stereoscopic vision, and discloses a synchronous speed visual matching method and system, electronic equipment and a storage medium, and the method comprises the steps: synchronously collecting a stereoscopic video sequence with a predefined frame rate; executing multi-target hybrid tracking and motion induction detection, and outputting target motion information including position and velocity vectors; extracting hierarchical motion features of the target from continuous multiple frames of the stereoscopic video sequence, and performing unified space-time coding; under geometric constraints of stereoscopic vision, scale cosine similarity, direction similarity and trajectory consistency measurement are calculated and serve as observation evidences to be input into the probabilistic reasoning model for fusion, and a posterior probability representing matching reliability is output; the weight distribution of the speed similarity and the direction similarity is adjusted according to the motion characteristics of the targets in the scene, and the stable corresponding matching relation between the left view target and the right view target is established. According to the method, high-time-resolution information can be utilized, motion features and geometric constraints can be effectively fused, and the method has self-adaptive capacity.

The invention provides a space-time fusion coding method and a data relation generation method and system, and is applied to the technical field of physical world-oriented multi-source heterogeneous data organization management. Space information of a target data object is mapped to a geographic space subdivision grid coding system to generate a space code; the method comprises the following steps: standardizing time information according to preset time granularity (such as year, month, day and hour), mapping the time information to a layered time coding system to generate a time code, and fusing a space code, the time code and object identification information according to a preset rule to generate a unique space-time fusion code; and finally, the space-time fusion codes and attribute information are bound to form standardized data records, data objects of different sources and different formats are mapped to a unified space-time coding system, data islands are broken, and a data foundation is laid for constructing a unified space-time relation network.