142 results about "Spatial graph" patented technology

The invention relates to the technical field of slope catastrophe intelligent early warning and data modeling, and particularly discloses a strip mine high and steep slope multi-source monitoring and early warning method based on a graph neural network and Transform, and the method comprises the following steps: S01, carrying out the data preprocessing and disturbance variable construction of monitoring data; s02, constructing a heterogeneous space diagram structure by taking the monitoring points as nodes and taking geography, lithology and dynamic response relationships as edges; s03, constructing a space-time end-to-end multilayer coding framework based on the graph attention network and the integrated deep neural structure; s04, on the basis of graph coding and time sequence output, introducing a disturbance variable embedding mechanism, and designing a joint attention fusion structure; and S05, generating a deformation trend prediction value of the slope in a future period of time and performing corresponding risk grade judgment. The invention aims to solve the key technical problem of weak adaptability and interpretability of an early warning system.

The invention discloses a dementia identification method based on brain computer network space cross attention fusion. The dementia identification method comprises the steps that resting-state electroencephalogram signals are acquired, preprocessing and brain network construction are carried out, the frequency band power ratio is calculated, and a training set and a test set are divided; the brain network fuses the spatial information, and spatial features are obtained through an isotropic graph neural network and a local feature enhancement module; the frequency band power ratio is coded by a multi-layer perceptron to obtain frequency spectrum statistical characteristics; a bidirectional cross attention module is input, and complementary fusion features are extracted; and performing mixed pooling and splicing on the complementary fusion features, then sending the fused features to a Chebyshev Kolmogorov-Arnold network classifier, and outputting an Alzheimer's disease / frontotemporal dementia / health control (AD / FTD / HC) classification result and a cognitive scale evaluation score (MMSE). According to the method, the multi-feature complementary information is effectively integrated through joint modeling of the spatial diagram features and the global spectrum features, and the accuracy, stability and generalization ability of AD and FTD classification in a complex brain network are improved.

The invention relates to the technical field of micro-grids, in particular to a micro-grid source load prediction matching method based on a graph neural network and an attention mechanism. The method comprises the following steps: acquiring multi-dimensional data of a micro-grid, and constructing a heterogeneous graph based on the multi-dimensional data; based on the heterogeneous graph, space graph convolution features and time sequence features are extracted, and unified space-time features are obtained through space-time graph attention network fusion; constructing a hierarchical attention network, and outputting the enhanced spatial-temporal characteristics of each node; respectively predicting an available power output sequence, a power demand sequence and a cost matrix through three decoders; calculating an optimal matching matrix with the minimum total transmission cost; and converting the optimal matching matrix into an equipment control instruction, issuing the equipment control instruction to an equipment controller, and taking an execution result and the newest state of the power grid as new multi-dimensional data. According to the method, high-precision matching prediction is realized, and a real-time and optimal control strategy is generated, so that the operation economy, safety and new energy consumption capability of the micro-grid are remarkably improved.

The invention discloses a traffic state prediction method, device and equipment for a road side edge platform, and a medium, and relates to the technical field of computers, and the method comprises the steps: carrying out the data preprocessing and aggregation of traffic state observation data, extracting a space rule by using a first time sequence gating convolution layer and a space graph convolution layer in a target space-time convolution block of the initial traffic state prediction model, and extracting time sequence dynamic characteristics based on a second time sequence gating convolution layer; and performing feature enhancement by using a three-dimensional attention mechanism, determining an obtained output result as a new input feature, determining a new target space-time convolution block, and skipping to a step of extracting a time rule by using a first time sequence gating convolution layer in the target space-time convolution block of the initial traffic state prediction model based on the input feature, and obtaining a target feature output by the target traffic state prediction model, and determining a future traffic state prediction result based on the target feature. And the accuracy of traffic state prediction is improved.

The invention discloses a block file space topology aggregation and cross-modal indexing method based on HDF5, and relates to the technical field of electric digital data processing. The method comprises the following steps of point cloud space division, space data separation and storage, multi-modal data fusion and multi-level index construction. The method comprises the following steps: performing space division on a point cloud space through an octree algorithm to obtain octree nodes, analyzing the relevance between the space division and a memory in real time to determine whether to perform octree division dynamic adjustment, then encoding each octree node, performing space block data separation storage, and simultaneously performing point cloud data access optimization. According to the method, the multi-level index system of the point cloud space is established, then spatial attribute aggregation is performed to establish the spatial diagram, multi-modal data integration fusion is performed, and finally the multi-level index system of the point cloud space is established, so that the efficiency of accessing and retrieving the image block file space is improved, and the problem of low cross-modal retrieval efficiency of the point cloud space divided based on an octree algorithm in the prior art is solved.

The invention discloses an arch bridge tensioning method and system based on a physical constraint space-time diagram attention network, and the method comprises the steps: constructing a dynamic topological graph of an arch bridge cantilever construction structure, taking a structure initial state parameter, a control force system parameter and a real-time environment parameter as node input features, and taking a structure state response parameter as an output label, generating a training data set based on finite element model simulation; designing a deep learning network model combined with space diagram attention and time sequence causal convolution, and modeling a space-time dependency relationship; defining a mixed loss function of the data fidelity item and the physical residual loss item of the structural mechanical control equation; and training the model through a back propagation algorithm and deploying the model to a field decision control system. According to the method, through graph structure abstraction, spatio-temporal joint modeling and physical constraint embedding, multi-source monitoring data driven structure state online prediction and tension control strategy intelligent optimization are achieved.

The invention discloses a building structure health monitoring method and system based on multi-source data fusion, and the method comprises the steps: obtaining multi-source physical parameters related to a building structure, and carrying out the preprocessing; and carrying out online quality evaluation by using a conditional variation auto-encoder model trained based on health reference data, and calculating the dynamic reliability weight of each sensor according to an error. And constructing a sensor space diagram fusing physical proximity and signal correlation, performing deep feature learning by using a space-time diagram attention network, and outputting a global feature vector and a key node feature vector representing the overall health state of the structure. And constructing a reliability weighting-dynamic Bayesian network model, and outputting the structural health state confidence evolved along with time. And outputting a deterministic health state recognition result and a corresponding confidence coefficient by adopting a dual-threshold decision-making mechanism in combination with state transition verification and persistence analysis. The damage position, the damage degree and uncertainty quantification are output, and the robustness of building structure health monitoring is improved.

The invention discloses a grid runoff forecasting method based on a space-time diagram convolutional network, and particularly relates to the technical field of runoff forecasting. The method comprises the following steps: firstly, performing grid division on a target drainage basin based on a landform and hydrological response unit, and constructing a space map structure; combining historical rainfall, runoff and remote sensing meteorological data to construct a space-time input feature sequence; extracting a spatial dependency relationship by using a graph convolutional network, extracting a time evolution feature by combining a long-short term memory network, and generating a node representation fused with the spatial-temporal feature; outputting a runoff prediction result of multiple time steps through a full-connection neural network; for a heavy rainfall event, input characteristics and a space structure can be updated in real time, dynamic completion and local graph structure reconstruction of missing data are realized, and finally a gridding runoff distribution graph in a future time period is generated. The method has high precision, high timeliness and high adaptability, and is suitable for runoff intelligent prediction in complex terrains and sudden heavy rainfall scenes.

The invention discloses a wind turbine blade internal damage diagnosis method based on a sound field graph neural network, and belongs to the technical field of wind turbine blade state detection, and the method comprises the steps: deploying a microphone array in a cabin, and collecting an acoustic signal when a blade rotates; constructing a space sound field graph structure by taking the microphone as a node and the sound wave propagation path as an edge; extracting nonlinear acoustic features by using a physical constraint graph neural network PC-GNN; generating a damage embedding vector based on self-supervised training contrast learning; and outputting a damage probability thermodynamic diagram and positioning information. According to the method, a directional microphone array is deployed in a cabin, and a sound wave propagation space diagram structure is constructed; designing a physical constraint graph neural network PC-GNN, and embedding an acoustic wave equation as a regularization item; the problem of scarcity of damaged samples is solved by adopting self-supervised contrast learning; and finally outputting a positioning thermodynamic diagram of the internal damage of the blade.

The invention discloses a water supply network pipe burst diagnosis and positioning method and device, electronic equipment and a program product, and the method comprises the steps: collecting sensor data and GIS geographic information of a plurality of pipeline intersections of a water supply network through an AIoT platform, and obtaining a topological structure of the water supply network; constructing a digital twin map of the water supply network according to the sensor data, the GIS geographic information and the topological structure; inputting the digital twin map into a pre-trained pipe burst diagnosis positioning model to obtain a diagnosis positioning result; performing pipe explosion early warning or fault warning according to a diagnosis positioning result; wherein the pipe burst diagnosis positioning model is obtained based on training of a hierarchical graph neural network, and the hierarchical graph neural network comprises a space graph convolution layer, a time sequence convolution layer, a feature fusion layer and an output layer. The method improves the accuracy and timeliness of diagnosing and positioning the pipe burst of the water supply network, and can be applied to the technical field of the Internet of Things.

The invention relates to the technical field of artificial intelligence, and discloses an AI-based water quality parameter hyperspectral data inversion method and system. Comprising the following steps: acquiring a hyperspectral image through a hyperspectral imager, acquiring water temperature and illumination intensity auxiliary parameters by combining a GNSS positioning module and an environment sensor, and constructing a multi-dimensional data set of space-time alignment; inputting the multi-dimensional data set into a denoising model based on a GAN (Generative Adversarial Network), distinguishing a real spectrum and noise through a discriminator, and reconstructing a pure spectrum through a generator to suppress atmospheric scattering and instrument noise; constructing a spectrum-space joint feature vector; calculating the contribution degree of each wave band to a target parameter by utilizing a wave band attention mechanism and a spectrum-space joint feature vector, and weighting to generate an enhanced spectrum feature; modeling the superpixel spatial adjacency relation through a graph neural network GNN to capture spatial correlation, and generating spatial graph features; and inputting the enhanced spectral features and the spatial diagram features into a cross-modal interaction layer, and finally outputting predicted values of the water quality parameters.

The application relates to a large-scale road network traffic control method based on a deep reinforcement learning large model and belongs to the technical field of intelligent traffic control. The method comprises the following steps: perceiving real-time multi-modal road network information including urban road intersections, highway entrance ramps and emergency lanes, generating a space-time fusion representation vector representing the current traffic network state by fusing a space graph construction method and a time series embedding method; splicing the space-time fusion representation vector and a historical state memory to serve as input, performing state feature distillation on a backbone network of a pre-trained large language model to enhance state representation, and outputting a traffic control decision through a policy network with a hierarchical action space; and guiding and optimizing the training process of the deep reinforcement learning algorithm through cross-modal knowledge transfer and a progressive curriculum learning strategy to improve the model training efficiency and generalization ability. The application improves the generalization performance and accuracy of the control strategy while ensuring real-time response speed.

The invention discloses a medical record generation method, a related device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the voice recognition based on the doctor-patient conversation voice in an inquiry process, and obtaining a doctor-patient conversation text; performing information extraction based on the doctor-patient dialogue text to obtain a time information element, a medical event corresponding to a time interval and a spatial information element; constructing a symptom evolution time sequence chain based on the time information element and the medical event corresponding to the time interval, and constructing a symptom spatial map based on the spatial information element; performing feature extraction based on the symptom evolution time sequence chain to obtain a first feature, performing feature extraction based on the symptom space map to obtain a second feature, and performing feature extraction based on the doctor-patient dialogue text to obtain a third feature; and generating an electronic medical record text based on a fusion feature of the first feature, the second feature and the third feature. According to the scheme, dynamic information of disease evolution can be reflected in medical record generation.

This invention belongs to the field of food or beverage processing technology, specifically relating to an intelligent evaluation and brewing control system for tea infusion quality based on multi-source sensor fusion. The system uses a multi-wavelength optical sensor array and distributed temperature sensors to acquire spectral absorption peak shift characteristics and temperature gradient vectors, respectively, and then concatenates them to construct a three-dimensional tensor containing time, spatial height, and wavelength dimensions. This three-dimensional tensor is input into a spatiotemporal graph convolutional network. Its spatial graph convolutional layer extracts spatial features using temperature nodes at different height levels as vertices and thermal conduction physical relationships as edges; the temporal convolutional layer extracts temporal features; and the fully connected layer outputs the predicted solute release rate and the deviation value of the target temperature gradient. The controller generates pulse-width modulation signals based on the deviation values ​​to adjust the heating plate power and the timing of the water injection valve. This invention jointly analyzes heat distribution and solute concentration changes, and corrects control commands based on the physical release state of the solute, ensuring that temperature field changes match solute diffusion.

The invention discloses a hierarchical support decision-making method and system for surrounding rock of a water-rich tunnel, and relates to the technical field of geotechnical engineering.The method comprises the steps that data of different sections of a target tunnel are collected, and space-time dual alignment is conducted; calculating a water pressure index and a corrosion index according to the collected data, and calculating a baseline value based on physical and mechanical parameters of the surrounding rock; correcting the baseline value according to the water pressure index and the corrosion index, generating a regression label and a grading label, and forming a training sample; constructing edges by taking the sections as nodes to obtain a space diagram, and constructing environment time sequence embedding; the space diagram and the environment time sequence are embedded and fused to serve as input, a water pressure correction coefficient, a corrosion correction coefficient, a regression label and a grading label serve as output, a space-time diagram attention network is trained, and a prediction model is obtained; and inputting the data of the to-be-evaluated section into the prediction model to obtain a prediction result, and generating a support suggestion. The method can effectively improve the surrounding rock stability evaluation precision in the complex water-rock coupling environment.

The invention relates to the technical field of trajectory data mining and identity recognition, in particular to a trajectory user linking method and device based on graph edge weight optimization and a storage medium. The model is composed of a local graph representation learning module fused with grid semantics, a global relation graph representation learning module of adaptive edge weight, a layered space-time attention network and a track user link module. The method comprises the following steps: carrying out gridding processing on an anonymous track, extracting hierarchical semantic embedding of POI categories, and constructing a local space graph and a global relation graph; introducing a semantic consistency coefficient into the local graph to re-calibrate an edge weight, and dynamically modeling an interaction relationship between tracks and between users and tracks in the global graph through a self-adaptive edge weight learning mechanism; fusing local space-time features and global interaction representation through a layered space-time attention network; and finally, the features are projected to the user space for matching, so that the accuracy and robustness of the track user link are remarkably improved.

The present application relates to the technical field of marine data processing and spatio-temporal prediction, and specifically discloses a sea surface core variable prediction method and system based on a wavelet-enhanced graph neural network.The method comprises the following steps: obtaining multivariate graph sequence data of a target sea surface; performing multilevel wavelet decomposition and gated fusion on each variable through a variable-level multiscale wavelet gated fusion module, and outputting enhanced multiscale time series features; inputting the enhanced multiscale time series features into a KAN-LSTM encoder module, performing recursive updating through gated fusion of a conventional convolution and a KAN convolution branch, and outputting encoder spatio-temporal features; inputting the encoder spatio-temporal features into a signed adaptive spatial graph convolution module, learning a signed sparse adaptive adjacency matrix and performing multi-order diffusion aggregation, and outputting a prediction result.The present application realizes long-term prediction of a target sea surface with high precision and high stability.

This invention discloses a traffic flow prediction method and system based on Transformer, belonging to the fields of intelligent transportation and deep learning technology. Addressing the technical problems of existing traffic flow prediction models, such as difficulty in simultaneously considering long-term and short-term dependencies, inability of static road network topology to characterize dynamic spatial heterogeneity, and poor modeling performance of spatiotemporal feature coupling, this invention proposes a multi-timescale adaptive graph attention Transformer model. This method first reconstructs the original traffic data at low, medium, and high time scales, and then aggregates spatiotemporal features through a temporal convolutional network and a compressed excitation network. Next, an adaptive data graph generation module learns node embedding vectors to generate an adaptive adjacency matrix that integrates static topology and dynamic associations. Finally, an encoder incorporating temporal one-dimensional convolutional multi-head attention and spatial graph attention, and a decoder integrating causal convolution and temporally gated convolution, are constructed to achieve high-precision multi-step prediction of traffic flow. This invention effectively captures the spatiotemporal dependencies of traffic flow, with prediction accuracy and generalization superior to mainstream models, and can be widely applied to urban intelligent traffic management, dynamic path planning, and traffic congestion mitigation scenarios.

This invention belongs to the technical field of natural language processing and dialogue systems. It discloses a multi-target dialogue recommendation method based on hierarchical prompt optimization, including hierarchical target label modeling, learnable prompt embedding initialization, graph attention enhancement mechanism, multi-task learning framework, and response generation model. Hierarchical target label modeling encodes the hierarchical relationships between target labels into a graph structure. Learnable prompt embedding initialization integrates label semantics and deep hierarchical information into the encoder input space. The graph attention enhancement mechanism enhances the label vector structure perception ability through information transmission and aggregation between label prompts. The multi-task learning framework jointly optimizes masked language modeling and hierarchical multi-label classification. The response generation model uses the predicted target as a control signal to guide generation. This method effectively models the hierarchical dependency relationship between target types and target entities, enhances the structure perception ability of label vectors, maintains the model's language understanding ability, and improves the accuracy of target prediction.

The invention discloses a traffic network data completion method fusing MFD and deep learning, and the method comprises the steps: describing a traffic flow feature relation of traffic network operation variables through selecting an optimal MFD function form; in combination with a space-time deep learning model, a space graph convolutional neural network layer and a time sequence GRU neural network layer are constructed to accurately model correlation between nodes and between different time periods in the traffic network, and space-time evolution characteristics of the traffic network are captured; and constructing a loss function, and realizing data fusion of the deep learning module and the MFD structure module. The method can adapt to various types of data missing modes and different missing rates, has high-precision recovery capability for data missing in the traffic network, better adapts to randomness and dynamic change of real traffic network data set missing values, and improves the robustness and generalization capability of the model. The method not only improves the accuracy of traffic data completion, but also has a better completion effect under a higher data missing rate.

The application discloses an ecological coastline wave parameter prediction method based on a space-time graph neural network, which comprises the following steps: acquiring time-series marine observation data of a preset ecological coastline marine area to obtain a training set and a verification set; constructing a prediction model for ecological coastline wave parameters, which comprises a space graph construction module based on wave causal driving, a time feature acquisition module based on LSTM time coding, a space feature acquisition module based on GCN space propagation, a cross-attention fusion module and a prediction output module; training the prediction model through the training set to obtain an optimized prediction model, and verifying the optimized prediction model based on a model loss function according to the verification set to obtain an optimal prediction model; and predicting the ecological coastline wave parameters according to the optimal prediction model. The application solves the problems of the prior art, such as low modeling accuracy, ineffective consideration of spatial structure, lack of causal relationship explanation and incomplete observation data.

A method of providing information related to placing an object in a space includes obtaining three-dimensional spatial data corresponding to the space and object-related data for first objects in the space, obtaining a spatial graph including positional relations between the first objects in the space, based on the three-dimensional spatial data and the object-related data, receiving a user input for changing an object placement in the space, based on the user input, adding, to the spatial graph, an empty node representing a second object to be placed in an empty region in the space in which the first objects are not placed, updating the spatial graph by applying, to a graph neural network (GNN), the spatial graph to which the empty node has been added, and outputting object placement change-related information for the space, based on the updated spatial graph.

The invention discloses an automatic operation and maintenance platform operation and maintenance method and system based on big data. Establishing an anomaly detection model through a topological dependency relationship between space layer modeling equipment in the ST-GNN space-time diagram neural network and utilizing a time layer to capture time sequence features of monitoring indexes; based on the anomaly detection model, combining a GAN confrontation generation network to generate confrontation sample data for training, and obtaining a target anomaly detection model; and inputting the fusion feature data into the target anomaly detection model, outputting a potential fault event and a confidence coefficient, and generating an optimal operation and maintenance strategy according to the potential fault event and the confidence coefficient based on an RL reinforcement learning framework. The long-term operation and maintenance stability is improved, and the management efficiency of large-scale complex operation and maintenance scenes is improved.

This application relates to the fields of artificial intelligence and computer technology, specifically providing a risk decision-making method and system for the entire lifecycle of power data. The method includes: acquiring multi-source heterogeneous log data from the entire lifecycle of a power information system; using an unsupervised feature extraction module based on a variational autoencoder to extract latent feature vectors and determine the VAE reconstruction error; performing spatiotemporal feature extraction on the latent feature vectors to obtain the temporal behavior probability from the time-series analysis stream and the spatial graph embedding distance from the spatial analysis stream; and constructing a dynamic decision-making module based on a Bayesian network, using the VAE reconstruction error, temporal behavior probability, and spatial graph embedding distance as multi-source evidence nodes for the dynamic decision-making module to calculate the posterior probability of risk events. This application, through the collaborative fusion of spatiotemporal features and causal probabilistic inference, can significantly improve the safety risk perception and dynamic control capabilities of power data throughout the entire process of acquisition, transmission, and processing.

Aspects related to how to predict the vehicle's destination include: Process a graph of the vehicle user's local settings, showing nodes corresponding to locations the user has previously visited. First, using the first graph neural network, at least one spatial graph processing is performed. This displays information about the geographical proximity of a location. The temporary graph shows information about... The locations visited by users one by one, the time between visits, and a preference graph displaying the data. Regarding the locations visited one by one and the frequency of visits, as analyzed by the neural network. The second graph combines the results of processing by the first neural network graph and the results of processing by the second graph. By constructing a second neural network graph using at least one layer of artificial neural networks and applying the results of... At least one neural network layer to predict the destination position;

The invention discloses a coal mining machine anomaly detection method based on a graph attention twin network, and relates to the technical field of mineral processing. According to the method, the representation mode of the running state of a coal mining machine is redefined on the graph structure level; position coding and edge-level physical coupling weight are introduced at a node level, and through adaptive feature fusion of space and time dimensions, the network can simultaneously capture structural dependence and time dynamics between devices. The design does not depend on recursive modeling of a single time sequence, but more comprehensively depicts a complex coupling relationship among multiple parts of the coal mining machine through topological propagation characteristics of graph attention, so that the feature expression ability under a small sample condition is fundamentally improved, the phenomena of false alarm and missing detection are effectively reduced, and the coal mining efficiency is improved. And the accuracy of anomaly detection of the coal mining machine is ensured.

This invention discloses a target tracking method and system based on UAV ID tracing, relating to the field of low-altitude security technology. The method includes: performing target detection on the current frame image to obtain detection boxes and detection confidence levels; simultaneously receiving UAV Remote ID information and extracting radio frequency fingerprint features to construct a multi-factor identity identifier; constructing a local geometric neighborhood for the detected target, calculating relative geometric relationships, and encoding them as geometric feature vectors; expanding the spatial graph of consecutive multiple frames into a spatiotemporal heterogeneous graph, embedding the multi-factor identity identifier, and outputting an association confidence matrix through a spatiotemporal graph neural network; implementing a three-level cascaded association based on dynamic thresholds, including high-confidence deterministic matching, medium-confidence fine association, and re-identification comparison of unmatched targets; updating the trajectory state and outputting a trajectory sequence with identity identifiers for flight tracing. This invention solves the problems of UAV identity hopping, occlusion loss, and ID spoofing in complex environments.

The application provides a spatial data retrieval method and device based on a Siamese network, equipment and a storage medium. The method comprises obtaining relevant spatial data, preprocessing the spatial data, storing spatial information contained in the corresponding data in a graph data structure feature, storing attribute information of the spatial data in node features in the graph data to enhance semantic feature representation of the nodes, storing spatial relationship information between the spatial data in edge features in the graph data to enhance spatial relationship features of the graph data, so that the enhanced spatial graph data structure is more consistent with the qualitative thinking mode of the human spatial data retrieval. Then, a corresponding Siamese network framework is constructed to ensure that the least sample information and shared weights can be used in the training model, and the spatial structure of the spatial data is learned by combining the information transmission advantage of the graph convolutional neural network. The spatial data retrieval and query are realized on the premise of considering the spatial data attribute features and the spatial relationship features.

The invention relates to the technical field of non-electrical variable adjustment, and discloses a path adjustment method for an aircraft transfer platform in a multi-layer transfer scene, which comprises the following steps: acquiring the quality of a controlled object borne by a first positioning unit, mapping the fundamental frequency resonance amplitude of a system based on the quality of the controlled object, and setting a dynamic dead zone threshold value; translating the physical displacement deviation into a feature node coordinate in a parameterized spatial map, extracting an instantaneous geometric feature of a non-uniform rational B-spline curve and converting the instantaneous geometric feature into a driving compensation vector, monitoring a pose deviation generated by residual mechanical oscillation of the first positioning unit, and when the pose deviation exceeds a dynamic dead zone threshold value, determining that the first positioning unit is in a dynamic dead zone; according to the method, a static waiting time sequence is reconstructed into a dynamic accompanying compensation process, the restriction of mechanical oscillation on the operation rhythm is eliminated, and the material flow volume of a transfer system is increased.

1. Name of the product in this design: Digital Spatial Graphical User Interface for Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design features of this product are the graphical user interface displayed on the screen panel of the electronic device. 4. The image or photograph that best illustrates the design's key features: the front view. 5. This electronic device is of conventional design, therefore other views are omitted. 6. Purpose of the graphical user interface: The graphical user interface on the product interface of this design is used for interaction in the digital space of the project. 7. Description of the changing states of the graphical user interface: The main view displays the homepage of the project in the digital space, showing all the cards of the projects the user has participated in; when the user triggers a project card on the main view, the interface shown in Figure 1 will be expanded to the changing state, where the user can view the contract execution status of the currently triggered project, the various communication channels, and the user's tools; when the user triggers the "My Tools" button in Figure 1, the user can enter the changing state expanded to Figure 2, where the user can view the various tool operations available in the current project, such as: project contracts, digital space members, signing and creating groups, and meeting tools.