153 results about "Spatiotemporal encoding" patented technology

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 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 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 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 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 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 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.

The invention relates to the technical field of digital content production, and discloses an interactive digital content production system based on a Transform architecture. According to the system, text, image and audio data of original digital content are acquired through a content feature extraction module, cross-modal feature alignment is performed by using a multi-head attention mechanism, and content feature tensors with space-time relevance are generated; the dynamic weight distribution module calculates relative importance scores of different modal features based on the tensor, and adopts a gating mechanism to perform dynamic weight fusion to form content semantic enhancement representation; the interaction intention analysis module performs space-time coding matching on the enhanced representation and the user operation instruction stream, and analyzes an intention distribution matrix of user operation on the content dimension; a hierarchical decoding generation module constructs a multi-scale content generation path in a Transform decoder according to the intention distribution matrix; and the real-time rendering engine module loads implicit representation output by the path, so that efficient and intelligent digital content creation is realized.

The invention relates to the technical field of bioengineering and digital twinning, and discloses a digital twinning modeling regulation and control system for a stem cell culture process, which comprises a signal acquisition module, a space-time coding module, a dynamic modeling module, a regulation and control decision module, a closed-loop execution module and an interactive interface module, according to the method, multi-modal data is collected through a composite sensor and coded into a space-time tensor, a gene noise-metabolic force field cross-scale dynamic model is constructed, a microenvironment regulation and control instruction is generated in combination with a multi-agent game, closed-loop cooperative regulation and control of a biochemical gradient field and mechanical parameters are achieved, and dynamic interactive calibration is achieved through a three-dimensional thermodynamic diagram. According to the method, a stem cell differentiation cross-scale dynamic model is constructed through multi-modal data fusion, microenvironment closed-loop regulation and control are realized by using space-time tensor coding and a multi-agent game strategy, and prediction precision and regulation and control real-time performance of a digital twin system are improved in combination with a three-dimensional visual interaction platform.

The invention relates to the technical field of big data stream processing, and discloses an efficient real-time big data stream processing method and system.The efficient real-time big data stream processing method comprises the steps that a four-dimensional index is generated through space-time coding, and time slices and space partition features are fused; constructing a four-dimensional tensor, and integrating space-time, event types and feature vectors; dynamically adjusting a tensor decomposition rank to realize storage compression; establishing event intensity model optimization window parameters; predicting a resource demand through online tensor regression and performing incremental updating; boolean tensor rule matching is combined with causal constraint to resolve conflicts; and carrying out multi-parameter combined gradient optimization and closed-loop feedback control. Through dynamic tensor decomposition and causal constraint injection technologies, in combination with multi-objective optimization and a closed-loop feedback mechanism, efficient processing of four-dimensional data streams, dynamic resource scheduling and rule conflict resolution are realized, and the system processing precision and the resource utilization rate are improved.

The invention discloses a gesture recognition method, device and equipment based on multiple sensors, a medium and a program product, and the method comprises the steps: decomposing multi-sensor data into a plurality of action frames according to a time sequence based on sensor timestamps, and generating an original sequence data set based on the plurality of action frames; the original sequence data set is preprocessed, a hand action time sequence is generated, and preprocessing comprises the step of compensating the original sequence data set based on the environment data; and distributing data weights of the sensors based on the contribution degrees of the sensors, performing feature fusion on the multi-modal hand action data of the time points in the hand action time sequence based on the data weights, performing time-space coding on the fused features, and inputting time-space coding vectors into a gesture recognition model for gesture recognition. According to the method, effective fusion of multi-modal sensor data is realized, and data compensation is performed by combining environmental data, so that the noise influence of the environment on the sensor is effectively reduced, and the gesture recognition precision is greatly improved.

The invention discloses an infrared small target detection method and system based on space-time coding. The problem that in the prior art, long-distance detection on an infrared small target is not accurate in the environment with the extremely low signal-to-noise ratio is solved. The method comprises the following steps: acquiring a multi-frame infrared image with an extremely low signal-to-noise ratio through an infrared detector; performing spatial domain transformation and time domain transformation on the multiple frames of infrared images to obtain a transformation domain matrix and a weight factor matrix; obtaining a transform domain image matrix according to the transform domain matrix and the weight factor matrix; decomposing the transform domain image matrix according to an iterative low-rank coefficient matrix decomposition algorithm to obtain a low-rank matrix and a sparse matrix; performing inverse transformation on the sparse matrix to obtain an enhanced target detection result; according to the method, accurate detection of the target in an extremely low signal-to-noise ratio environment is realized.

The invention provides a video moment retrieval method and device, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, and is suitable for the fields of financial science and technology and medical health. The method comprises the following steps: performing space-time coding on a target video to obtain an initial space-time feature map; performing feature transformation on the initial spatial-temporal feature map through a first hierarchical adaptive granularity converter to obtain a first hierarchical spatial-temporal feature map; performing feature transformation on the first-level spatial-temporal feature map through a second-level adaptive granularity converter to obtain a second-level spatial-temporal feature map; performing feature fusion on the first-level spatio-temporal feature map and the second-level spatio-temporal feature map to obtain multi-scale spatio-temporal features; retrieval demand information is obtained, and then text coding is carried out to obtain retrieval demand information features; and carrying out feature decoding on the retrieval demand information features and the multi-scale spatial-temporal features, and then carrying out moment prediction to obtain starting and ending moments of the target fragment. The video time retrieval efficiency can be improved.

The application discloses a gesture recognition method, device, equipment, medium and program product based on multiple sensors, which comprises the following steps: decomposing multiple sensor data into multiple action frames in time sequence based on sensor timestamps, and generating an original sequence data set based on the multiple action frames; preprocessing the original sequence data set to generate a hand action time sequence, wherein the preprocessing comprises compensating the original sequence data set based on environmental data; distributing data weights of each sensor based on the contribution degree of each sensor, performing feature fusion on multi-modal hand action data at each time point in the hand action time sequence based on the data weights, performing time-space coding on the fused features, inputting the time-space coding vector into a gesture recognition model for gesture recognition, effectively fusing multi-modal sensor data, and effectively reducing the noise influence of the environment on the sensor by combining environmental data for data compensation, thereby greatly improving the gesture recognition accuracy.

The invention discloses an intensive care unit patient hospitalization time prediction method based on time-space historical features, and the method comprises the steps: screening patients from a public database MIMIC-IV, respectively extracting the static features of the patients and the dynamic features divided according to the hour granularity, and eliminating the dynamic features with a high missing rate. According to the method, features per hour of hospitalization records of a patient are input into a space-time encoder, features with time and space dependence are extracted, then original features and space-time historical features of historical time points are fused and input into a decoder, global and local space-time dependence is fully extracted, and the decoder outputs predicted remaining hospitalization time. In this way, the global and local dependency relationships among the dynamic data at different time points in the hospitalization records of the patient can be better captured. And finally, resource allocation is optimized and medical cost is reduced for the intensive care unit according to a prediction result, and meanwhile, scientific auxiliary decision support is provided for clinicians.

The embodiment of the invention discloses a power system information acquisition and control method and terminal equipment. The method comprises the following steps: collecting multi-modal data; the multi-modal data comprises system-level data and region-level data; extracting input features from the multi-modal data; performing space-time coding on the input features by using an artificial intelligence AI model to obtain time-dependent features and spatial features; processing the time-dependent features and the spatial features by using a double-layer attention mechanism to obtain at least two levels of weights; according to real-time power grid data in the multi-modal data, two-stage weights are dynamically adjusted, and transient event information is output; obtaining a first configuration instruction according to the adjusted two-stage weight and the transient event information; optimizing the first configuration instruction based on the region-level data to obtain a second configuration instruction; the second configuration instruction comprises an acquisition instruction and / or a resource scheduling instruction; the resource scheduling instruction is used for scheduling software and hardware resources of the terminal equipment.

The application discloses a multi-view video synchronization and target association method based on space-time coding, comprising the following steps: step one, multi-view video acquisition; step two, space-time coding generation; step three, inter-frame synchronization based on space-time coding; step four, cross-view target detection; step five, construction of a space-time association cost matrix; step six, global target association; and step seven, output of an association track. The application uses space-time coding to replace absolute time stamps, effectively resists differences in frame rates of different cameras and time drift problems, and reduces the requirement of the system on hardware synchronization; the space coordinate information is integrated into the coding, so that the target association no longer simply depends on the appearance, and the association robustness in complex scenes such as light change and dramatic view change is greatly improved; through coding matching and global optimization, the video synchronization and target association tasks can be completed at the same time, and the calculation efficiency and system integration are improved.

The present application relates to a kind of construction safety risk early warning method and system based on multi-source data fusion, first acquisition including positioning trajectory, sensor signal, inspection text and video features and so on multiple types of real-time data, in combination with the construction field knowledge graph of semantic consistency verification, realize unified space-time datum and semantic association mapping;Through multimodal feature enhancement, space-time coding and cross-modal fusion, high-dimensional hidden state sequence is established;Causal structure model is constructed using knowledge graph weak causal relationship edge, and counterfactual disturbance response modeling is carried out on high-dimensional data, and the association of context and risk event is extracted;Dynamic adjustment risk assessment result, in combination with risk threshold realizes closed loop early warning response, and through feedback self-optimizing model parameter.The present application improves the precision and self-adaptive ability of construction site safety risk identification and response.

The invention discloses an internet resource long-term storage management and storage system, which comprises a space-time coding layer which is based on an FPGA (Field Programmable Gate Array) main control chip, comprises a parallelized tensor processing unit and is used for converting digital resources and metadata and context relationships thereof into a space-time coding body with a time self-description capability; the storage unit is used for receiving the space-time coding body from the space-time coding layer and permanently solidifying the space-time coding body in a tamper-resistant physical form; and the four-dimensional continuation control layer is used for dynamically managing the integrity, readability and authenticity of resources in the whole life cycle through an autonomous evolution algorithm and a distributed verification network. Through collaborative innovation of a space-time coding body (an information layer), a holographic cold memory (a physical layer) and a continuation control network (a management and control layer), the basic challenges of long-term storage of Internet resources in the aspects of authenticity, integrity, availability, safety, cost, technical dependence and the like are systematically handled.