607 results about "Graph structured data" patented technology

The invention relates to the technical field of red tide anomaly detection, in particular to an ocean red tide anomaly detection method and system fusing multi-source remote sensing and a graph neural network. The method comprises the following steps: acquiring remote sensing image data, unmanned aerial vehicle image data and monitoring data of a monitoring point; performing data preprocessing on the acquired remote sensing image data and unmanned aerial vehicle image data; performing feature extraction and feature fusion on the remote sensing image and the unmanned aerial vehicle image to obtain remote sensing feature data; constructing a space-time diagram structure based on the monitoring data of the monitoring points to obtain diagram structure data; based on a cross-modal comparison self-supervised learning mechanism, carrying out consistency representation learning on a remote sensing feature mode and a graph structure feature mode; by introducing multi-source heterogeneous data and fusing a graph neural network modeling means, the limitation of a single data driving method in the aspects of coarse red tide recognition granularity, low space-time precision and the like is effectively broken through, and the meticulous property and global perception ability of red tide feature modeling are remarkably improved.

The invention discloses a slope deformation monitoring and dynamic early warning method and system based on multi-sensor data, and relates to the technical field of slope monitoring, and the method comprises the steps: collecting multi-source sensor data by using pre-deployed multi-class sensors, constructing graph structure data according to the sensor distribution and the pre-processed multi-source sensor data, and carrying out the graph structure data; a graph convolutional network is used for modeling, and a slope deformation monitoring model is constructed; introducing a clustering federation learning strategy to carry out joint training on the slope deformation monitoring models of the plurality of sites, and carrying out risk grade division by using the trained slope deformation monitoring models; key influence factors of landslide disasters are extracted, an improved firefly algorithm is introduced to dynamically optimize an early warning threshold value, the optimized early warning threshold value and the current risk level are used for judgment, and early warning information is generated. According to the invention, the reliability of monitoring and the timeliness of early warning are improved through multi-source data fusion and intelligent analysis, and the crossing of slope deformation monitoring from single-point static state to networked intelligence is realized.

The invention discloses a mountain torrent disaster risk level prediction method based on a knowledge graph and a graph neural network, and relates to the technical field of risk level prediction, which is used for collecting data related to mountain torrent disaster risks, carrying out data preprocessing operation, constructing a risk level prediction model combining the knowledge graph and the graph neural network and training the risk level prediction model. According to the mountain torrent disaster risk level prediction method based on the knowledge graph and the graph neural network, entities, relationships and attributes can be represented in a graph form through the knowledge graph, complex knowledge and information can be effectively integrated and expressed, graph structure data can be effectively processed through the graph neural network, relationships between nodes can be captured, and the risk level of mountain torrent disasters can be predicted. Modeling and analysis of a complex system are achieved, a knowledge graph and a graph neural network are introduced into mountain torrent disaster risk level prediction, and the method aims at fully utilizing multi-source data, mining potential risk modes and laws, improving prediction accuracy and reliability and providing scientific basis and decision support for disaster prevention work.

The invention relates to the technical field of fault detection, and particularly discloses a prediction reconstruction framework causal perception space-time network for explaining anomaly monitoring in a complex industrial process, comprising the following steps: S01, constructing graph data E (V) and a causal graph; automatically adjusting the fusion proportion of the time-frequency characteristics according to the data characteristics so as to ensure that the model can comprehensively capture the information of the data in the time domain and the frequency domain; secondly, introducing a residual image attention network (RGAT), and converting the image data E (V) into image structure data G (S (V), E (V)); and S03, reconstructing a prediction error by adopting a variational automatic encoder (VAE), learning an error mode of normal data, providing an anomaly judgment AD (V) for anomaly detection, analyzing a causal relationship between data in combination with a causal graph, and positioning an anomaly reason according to an anomaly score, so as to form a prediction result. The network solves the problem that a traditional monitoring network is high in false alarm rate.

The invention requests to protect a rotating machine noise robust fault diagnosis method based on a mixed domain graph neural network. Comprising the following steps: collecting vibration data, and dividing an original vibration signal into a plurality of sections of subsamples with the length of 1024 by using non-overlapping sampling; converting the constructed subsample signal vibration signal into a frequency domain signal by using fast Fourier transform (FFT); taking the subsample frequency domain signals as nodes, the frequency spectrums as node features and the fault types as node labels, and then constructing the nodes into a graph structure; dividing the generated graph structure data set into a training set and a test set; and performing fault diagnosis on a test set by using the trained model, realizing self-adaptive weight adjustment by using a gating mechanism to fuse mechanical features, outputting a diagnosis result, calculating a performance index and performing result visualization analysis. The result shows that compared with a single-domain graph neural network, the method has higher accuracy and noise immunity, and end-to-end rotating machine fault diagnosis under the strong noise condition can be effectively achieved.

The invention discloses an intelligent construction and retrieval method for a medical knowledge graph based on GraphRAG and LLM. The intelligent construction method of the medical knowledge graph comprises the steps of data collection and processing, text segmentation, entity and relation extraction, entity analysis, weak connected component identification, word distance filtering, LLM evaluation, element abstract generation, graph community generation and community abstract generation. The intelligent retrieval method comprises the steps of knowledge graph vector library construction, text vector library construction, user question entity extraction and vectorization processing, multi-layer entity and relation retrieval, text retrieval, LLM summarization, result output and user feedback collection. According to the method, the GraphRAG and the LLM large model technology are combined, and efficient retrieval and accurate presentation of the medical information are achieved. According to the system, the accuracy and efficiency of medical literature retrieval are remarkably improved through deep integration of graph structured data and natural language processing capacity.

The invention discloses a head-mounted brain body workability assessment method based on a space-time dynamic fusion network, belongs to the technical field of artificial intelligence and human body signal acquisition, and solves the problems that an existing method cannot comprehensively reflect the comprehensive state of an individual, and interaction and potential risks among multiple factors are difficult to reveal. The method comprises the steps that physiological parameter signals are collected based on wearable equipment, and an attention and fatigue prediction result is output by an LSTM prediction model; according to the method, the graph structure data packet is analyzed and processed based on the pre-constructed space-time dynamic fusion network model, the working state signal characteristics of the plateau high-risk operating personnel can be accurately evaluated, the space-time graph neural network (STGNN) can capture the complex relation, the high-dimensional topological characteristics are extracted, and the high-risk working personnel can be accurately evaluated. The dynamic event-driven pulse neural network (deSNN) can convert the pulse sequence features into pulse sequence features so as to describe feature values more accurately, iterative optimization enables the pulse sequence features and the pulse sequence features to cooperate, and the accuracy and reliability of time-space dynamic fusion network model evaluation are improved.

The invention relates to the technical field of machine learning, and discloses a decision tree data model establishment method, which comprises the following steps of: acquiring heterogeneous data sources such as a structured data table, a time sequence data stream and graph structure data through distributed nodes, sampling the time sequence data stream by using a dynamic sliding window, and vectorizing the graph structure data through a graph embedding algorithm; a multi-stage feature selection model is constructed to screen features, and a dynamic decision tree generation framework adopting an adaptive splitting criterion is established based on the features. A tree structure is adjusted by applying a multi-objective optimization algorithm, and the performance is improved by introducing an incremental pruning mechanism. And the online model updating module monitors data distribution change, reconstructs a local sub-tree in good time, and injects noise to protect data privacy in combination with a differential privacy protection mechanism. According to the method, heterogeneous data is effectively processed, the model classification precision is improved, the complexity is reduced, the generalization ability is enhanced, the model can be updated online, and the data privacy is protected. The electronic equipment calls related instructions to execute the method, and efficient data processing and analysis can be achieved.

The invention discloses an intelligent management system and method for knowledge graph quality evaluation and self-repairing, belongs to the technical field of knowledge graphs, and aims to solve the problems that in traditional knowledge graph management, manual auditing efficiency is low, an effective automatic repairing means is lacked, and data complexity and real-time changes are difficult to deal with. The system firstly collects multi-source heterogeneous data in a target field, cleans the data through a deep learning noise recognition model, extracts entities and relationships by using a natural language processing technology, and adds metadata to convert the entities and relationships into graph structure data; then, a graph framework is defined based on the ontology, entity semantic alignment is achieved in combination with a graph neural network, and a knowledge graph is constructed by complementing implicit relations with the help of a pre-training language model. Then, the quality of the atlas is quantitatively evaluated through a four-layer quality evaluation system, meanwhile, a repair scheme is generated based on vulnerability feature extraction, knowledge base matching and decision fusion, and intelligent self-repair is achieved; the map can be monitored in real time and evaluated regularly, a repair strategy and a knowledge base are optimized through reinforcement learning, it is ensured that the map is kept accurate and time-efficient for a long time, and the practical value is improved.

The invention provides a network traffic data security assessment method and system based on deep learning. The method comprises the following steps: converting original network traffic data into a graph structure data set comprising a topological structure, node attributes and time sequence behaviors; in the process, the time-space fusion input tensor is formed through the association strength between adjacent matrix and Laplacian matrix coding network entities and the fusion of time sequence characteristics extracted by time window slices. Compared with traditional flow analysis which only pays attention to a single protocol or a rate threshold value, the method achieves global relevance expression of network behaviors through graph structure modeling. Through graph structure modeling, multi-dimensional feature fusion and probabilistic risk assessment, the method can adapt to dynamic change of network topology and continuous evolution of an attack mode, so that a final assessment result is more accurate.

The invention discloses a bank anti-money laundering monitoring method based on a graph neural network. The method comprises the following steps: S1, collecting data from a transaction database of a bank; s2, integrating all the collected data into multi-dimensional graph structure data by using a data fusion technology; s3, constructing an enhanced transaction network graph based on the multi-dimensional graph structure data; s4, using a depth map neural network model to carry out deep learning on the enhanced transaction network graph; s5, performing intelligent monitoring and analysis on new or continuously performed transaction activities by using the trained depth map neural network model; s6, according to a monitoring result in the step S5, continuously optimizing and updating the graph neural network model by using a real-time feedback loop and a theme perception adaptive mechanism; and S7, generating a report. According to the invention, the depth map neural network model is introduced, and the map neural network model considers the complexity and dynamic nature of a transaction network, so that money laundering behaviors can be identified more accurately.

The invention discloses an equipment operation and maintenance decision-making method based on large-scale knowledge graph multi-granularity learning reasoning, which comprises the following steps of: firstly, extracting a multi-granularity sub-graph of a subject entity in an equipment operation and maintenance knowledge graph, and integrating multilevel information such as an equipment state, a fault mode and an environmental factor to form a relation path set; secondly, performing joint coding on a question text and a relation path through an attention enhanced word embedding module based on a graph convolutional network, forming a word embedding vector, calculating a similarity score between a relation in the path and the question, pruning a sub-graph, and reducing the calculation amount; and finally, fusing a plurality of particle size sub-graphs after pruning processing, inputting the fused particle size sub-graphs into a reasoning decision module, extracting feature information from graph structure data by the reasoning decision module through graph convolution operation, dynamically adjusting a GCN learning process in combination with LSTM, finally realizing question reasoning and answer generation, and ensuring the accuracy of an equipment operation and maintenance decision.

The invention discloses an electric power Internet of Things equipment anomaly detection method and system based on federal map learning. The method comprises the following steps: constructing graph structure data; in a client layer, using a graph neural network as a local node classifier of a downstream task, and using an adaptive graph interpolation generator to repair sub-graphs and lost cross-sub-graph connections; adopting a plurality of servers as central nodes, and uploading obtained model parameters to the central server of a local area after a client is locally trained to converge; in the server layer, potential global features of covered clients are obtained according to an automatic encoder, model parameters are allowed to be transmitted between adjacent servers, and a global model is obtained by adopting an aggregation algorithm and issued to each client; and after each client updates the model, the next round of training is continued until the optimal detection model is obtained. Potential connections between sub-graphs are mined by using an adaptive graph interpolation generator, and the blank of lack of cross-client topological information is filled.

The invention discloses a multi-layer network flow dynamic monitoring and analysis method based on a graph neural network. The method comprises the following steps: S1, collecting data of each node in real time and preprocessing the data; s2, dividing the preprocessed data based on a preset time window, and constructing graph structure data; s3, carrying out topology learning on the constructed graph structure data by adopting an improved EvolveGCN, and generating final feature representation of dynamic change of a network topology structure; s4, carrying out modeling on the preprocessed data by utilizing Graph WaveNet, and extracting final feature representations of space-time dependency and local spatial correlation; s5, performing feature fusion to generate a comprehensive feature vector; s6, generating a final monitoring result based on the comprehensive feature vector; and S7, transmitting the final monitoring result to a network flow management platform for data processing and analysis. According to the method, an efficient and scientific optimization scheme can be provided in dynamic monitoring of the network traffic, and remarkable technical values and economic benefits are brought to practical application.

The invention relates to the technical field of mining equipment management and maintenance scheduling, in particular to a mining equipment maintenance task priority intelligent control method and system. Comprising the steps of performing data preprocessing on acquired mining equipment operation parameters; generating graph structure data based on the preprocessed mining equipment operation parameters; performing feature representation on the graph structure data by using a multilayer graph convolutional neural network, and outputting a health score; calculating a resource constraint score according to the obtained resource data; calculating a maintenance task priority based on the health score and the resource constraint score; carrying out descending sorting on the maintenance tasks according to the priority; and carrying out dynamic planning optimization based on a multi-stage decision, and solving to obtain a global optimal scheduling scheme. According to the method, GNN is introduced into mining equipment health state evaluation for the first time, and the evaluation precision is remarkably improved through equipment associated information modeling and node embedding representation learning.

The invention relates to the technical field of unmanned aerial vehicle anti-collision path planning, in particular to an unmanned aerial vehicle ad hoc network anti-collision path planning method based on a graph neural network, and the method comprises the steps: obtaining the topographic data of a task airspace, recording the initial positions, target positions and motion constraint conditions of all unmanned aerial vehicles, and constructing unmanned aerial vehicle graph structure data; according to the unmanned aerial vehicle graph structure data, taking each unmanned aerial vehicle as a unit node, and representing a potential collision relationship between two adjacent unmanned aerial vehicles by a side between the nodes; initializing features of the nodes and the edges, and completing training of an unmanned aerial vehicle graph neural network model based on the input features of the nodes and the edges as input of the preset unmanned aerial vehicle graph neural network model; and performing collision risk prediction based on the unmanned aerial vehicle graph neural network model, and generating and outputting an optimal flight path. According to the invention, through the graph neural network and multi-objective optimization, intelligent cooperative flight of the unmanned aerial vehicle group is realized, the collision risk and energy consumption are significantly reduced, and the network connectivity and task efficiency are ensured at the same time.

The invention provides a TSP path combinatorial optimization method and system based on reinforcement learning, and the method comprises the steps: constructing an anisotropic graph neural network as a backbone network of a diffusion model through the combination of solution distribution learning and combinatorial optimization target learning, capturing a complex relation in graph structure data through the representation capability of the network, and carrying out the optimization of a TSP path. And probability distribution is modeled by using a diffusion model in a single-to-Markov forward process. In addition, two kinds of accelerated sampling methods DDIM and DPM-solver of diffusion models are introduced, the sampling process of denoising is accelerated, and the training efficiency is improved. According to the method, direct preference optimization can be introduced into a diffusion model to further provide preference-guided combinatorial optimization (PGCO), so that the generalization ability and adaptability of a traveling salesman problem (TSP) solving model are improved, and a more efficient and high-quality model for solving large-scale combinatorial optimization is provided.

The invention discloses an unmanned aerial vehicle charging path optimization method based on deep learning, and the method comprises the following steps: S1, collecting the flight state data of an unmanned aerial vehicle, and constructing a state input sequence; s2, normalizing the state input sequence to form modeling input data; s3, inputting the modeling input data into the improved closed continuous time state evolution model to generate a state trajectory prediction result; s4, generating a path adjustment instruction according to a prediction result, and constructing path diagram structure data; s5, inputting the path graph structure data into the improved graph pointer network to generate an optimal path instruction; s6, fusing the optimal path instruction and modeling input data, iteratively updating and re-predicting a state trajectory; s7, generating a charging path execution scheme; and S8, forming an unmanned aerial vehicle path optimization control instruction sequence. According to the method, the path planning and charging scheduling efficiency of the unmanned aerial vehicle in a complex environment is improved, and the method has relatively high intellectualization and adaptability.

The invention discloses a motor fault diagnosis method and system based on color image fusion symmetric point mode, and the method comprises the steps: converting a vibration signal and an electromagnetic signal of a motor into symmetric point mode images, and generating a color signal image fusing feature information; respectively abstracting the color signal images fused with the feature information into nodes and edges in a high-dimensional semantic space so as to construct graph structure data; and performing diagnosis classification on the graph structure data of the vibration signals and the electromagnetic signals by using respective capsule graph network models, and fusing diagnosis classification results of the vibration signals and the electromagnetic signals through a voting mechanism to obtain a final diagnosis classification result. According to the method, multi-channel time domain signals are converted into image expressions with dense information and consistent geometry, and unified feature modeling is carried out on the images based on a depth map structure network with topology perception capability, so that motor fault diagnosis with high diagnosis precision and strong robustness is realized.

The invention provides a traffic flow prediction method, system and device and a medium, and belongs to the technical field of intelligent traffic. The method comprises the steps of obtaining multi-source heterogeneous original traffic flow time sequence data, performing preprocessing to generate time sequence tensor data, and constructing graph structure data based on road network topology and traffic flow correlation; inputting the time sequence tensor data and the graph structure data into a pre-constructed time-space diagram neural network model, processing the graph structure data through a graph convolution module of the time-space diagram neural network model to extract spatial features, and processing the time sequence tensor data through a time sequence modeling module of the time-space diagram neural network model to extract time features, fusing the spatial features and the time features to form joint spatial-temporal features; inputting the joint spatial-temporal characteristics into a prediction layer of a spatial-temporal diagram neural network model for processing, and outputting predicted value data of the traffic flow in a future period as prediction result data; and feeding back prediction result data to the traffic control and management system to drive the traffic control and management system to execute control operation.

The invention relates to the technical field of graph neural network and causal representation learning, in particular to a graph neural network method for separating time-varying and time-invariant causal features, which comprises the following steps: acquiring graph structure data at a plurality of moments, and establishing a dynamic graph sequence; coding the dynamic graph sequence by adopting a time sequence graph neural network to obtain a node representation matrix; respectively adopting a time-varying attention head, a time-invariant attention head and a shortcut attention head to carry out attention score calculation, mask construction and sub-graph decomposition processing on the node representation matrix to obtain a time-varying cause sub-graph, a time-invariant cause sub-graph and a shortcut feature sub-graph; performing independent graph neural network coding and dynamic gating fusion to obtain fusion features; determining a current predicted value according to the fusion feature; according to the method, the causal discovery accuracy, robustness and interpretability can be improved.

The invention provides a brain age estimation method based on a dynamic fuzzy learnable brain network, and belongs to the technical field of medical image processing and artificial intelligence. According to the technical scheme, the method comprises the following steps that S1, brain nuclear magnetic resonance imaging of a subject is collected, and preprocessing and data division are carried out; s2, constructing graph structure data, and performing feature extraction and position information embedding on the data; s3, constructing a dynamic fuzzy learnable brain network model comprising a main branch and a local branch, and respectively extracting global and local connection features; s4, introducing a dynamic fuzzy multi-head self-attention module into the main branch to realize effective modeling of global features; s5, a local branch dynamically models a dependency relationship between channels through a convolution filter and a learnable graph attention module; s6, after the features of the main branches and the local branches are fused, brain age prediction is carried out through a multi-layer perceptron. According to the method, the modeling capability of the brain function connection mode is improved, and the brain age prediction task can be more effectively completed.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent curriculum schedule arrangement method and system, and the method comprises the following steps: defining teacher nodes, student nodes, curriculum nodes and classroom nodes based on the information of teachers, students, curriculums and classrooms, building the relation between the nodes, analyzing the time conflict, burden and classroom capacity, endowing each edge with a corresponding weight, and carrying out the arrangement of the curriculum schedule. And obtaining graph structure data. According to the method, the information node relation network is constructed, the course arrangement problem is converted into graph structure data processing, teacher arrangement, student demands, course capacity, time conflicts and other constraint conditions are quantified, node states are updated layer by layer, the course arrangement influence is accurately evaluated, the optimization efficiency is improved, constraint weight distribution is dynamically adjusted, key contradictions are preferentially solved, and the course arrangement efficiency is improved. According to the method, balance and rationality are considered, a curriculum schedule is adjusted in combination with real-time data, student course selection and teacher time change are rapidly adapted, resource waste and insufficient coordination of static arrangement are avoided, and an efficient and flexible curriculum arrangement scheme is realized.

The invention belongs to the technical field of engineering simulation data processing, and particularly discloses a finite element grid graph structure construction method and system, a terminal and a medium. Comprising the following steps: analyzing original full-amount grid data exported by finite element simulation, segmenting unstructured grid data into a grid vertex coordinate set and a grid unit mark number set, and constructing a graph edge topological structure corresponding to a grid based on a unit mark number relationship; on the basis, loading physical field data and adopting a tolerance-based coordinate matching algorithm to realize accurate mapping of physical field labels and material attributes with grid nodes; and generating standardized finite element grid graph data which can be directly used for graph neural network processing. By means of the method, high-consistency and high-physical-reliability conversion from the finite element simulation data to the graph structure data is achieved, and the physical field modeling and simulation acceleration capacity based on graph learning is improved.

The invention provides a knowledge graph completion method based on a large language model and a graph neural network. The method comprises the steps of obtaining definitions and descriptions of a head entity, a tail entity and a relation in a triple, and obtaining a problem instruction for triple classification; a multi-hop sub-graph with an entity as the center is obtained, a word embedding model is used as a feature extractor, the multi-hop relation between triples is analyzed, and therefore high-quality semantic features existing in multiple hops are obtained. Secondly, complex graph structure information is captured by using a graph neural network, and an interaction relationship between nodes is learned, so that graph structure data can be better understood. And finally, fine-tuning the large language model in combination with the question and answer instruction to help the large language model focus on the key task. According to the method, the characteristics of strong context perception capability and complicated sentence structure and context understanding of a large language model are combined with aggregated node structure information to generate embedded representation with better expression capability, so that the performance of knowledge graph completion is improved.

The invention discloses a building electrical safety protection method, which comprises the following steps of performing real-time data acquisition on a building electrical system to obtain multi-modal time sequence data; constructing the multi-modal time sequence data into graph structure data with a node-edge topological relation, and encoding the multi-modal original observation data corresponding to each node into a multi-modal initial feature vector of the node; graph neural network feature extraction is carried out on the graph structure data, and a global feature vector sequence used for representing the operation state of the electrical system is obtained in combination with an attention mechanism; inputting the global feature vector sequence into a pre-constructed time sequence prediction model to perform operation state prediction, and judging whether potential abnormality exists or not; and when the residual error exceeds a preset threshold value, fault backtracking positioning is carried out on the key node according to the attention weight, and an electrical fault point is generated in combination with node characteristics. According to the invention, the fault response speed and the emergency disposal efficiency can be effectively improved, and the safety accident rate is reduced.

The invention discloses an urban territorial space multi-element coupling intelligent optimization method and system, and the method comprises the steps: quantifying the functional partition transition probability of an ecological protection region, a basic farmland and an urban development region based on a Markov model, and simulating the spatial distribution of functional partitions through a future land utilization simulation model, constructing a first multi-target optimization model in combination with resistance surface analysis of the minimum cumulative resistance model; collecting urban POI data, screening residential, commercial and public service types as optimization objects, dividing the optimization objects by utilizing clustering analysis, extracting core points, and constructing a second multi-target optimization model; and constructing graph structure data based on the road network, defining node and edge features, training the graph structure of the road network by using a graph neural network, extracting the features, and constructing a third multi-target optimization model to make a multi-target optimization decision of the road network.

The invention relates to a wind power cluster short-term power prediction method and device of a space-time diagram neural network fused with physical information and computer equipment, and the method comprises the steps: obtaining related information data of each wind power plant in a wind power cluster, and carrying out the preprocessing; forming a physical prior data set through an engineering analysis model fusing the wake flow analysis model and the blocking effect model; taking each wind power plant as a node of the graph, constructing graph structure data for predicting the power of the wind power plant, and forming a dynamic adjacent matrix; constructing a space-time diagram neural network WB-STGNN model architecture comprising a diagram convolutional neural network module, a gating time convolutional network and a multi-layer perceptron; the method comprises the following steps: pre-training by using a physical prior data set, and then performing formal training based on historical power data and a dynamic adjacency matrix to obtain a space-time diagram neural network WB-STGNN model; inputting the wind speed of the prediction day, and predicting the active power of the whole wind power cluster in 24 hours of the prediction day. By adopting the method, the precision and efficiency of wind power cluster power prediction can be effectively improved.

The invention discloses a graph structure data reachability index construction and query method and system meeting time and step length constraints, and the method comprises the steps: obtaining tense graph data, and constructing an index based on a 2-hop label; optimizing the index by using a pruning technology, and constructing a hierarchical index based on time merging and step length merging; reading corresponding indexes according to the query, and filtering indexes which do not meet conditions according to constraints; and executing the query algorithm to obtain a query result. According to the Hop label index construction method combined with the pruning technology, tense correlation pruning is added on the basis of the traditional 2-hop index technology, and the index scale and redundant path storage are greatly reduced. The time merging and step length optimization technology is adopted, the query efficiency is remarkably improved through index merging and step length control, and meanwhile storage occupation is reduced.

The invention relates to the technical field of stem cell biology, and discloses a stem cell quality evaluation system and method, and the system comprises a data collection module which collects local environment data and image data of stem cells in a preset range; the morphological recognition module is used for obtaining cellular morphological data of all stem cells in a preset range through a cellular morphological recognition model; the graph structure data construction module is used for constructing graph structure data; the score marking module is used for measuring the multiplication capacity, the genetic stability capacity and the surface marker expression capacity of the stem cells after the preset duration, and obtaining the quality score of the stem cells through manual marking; the model training module is used for training a stem cell quality scoring model through an optimization algorithm; according to the method, the cellular morphology data of the stem cells are extracted through the multi-branch network of the cellular morphology recognition model, and the interaction among the stem cells is comprehensively considered through the stem cell quality scoring model, so that the accuracy of stem cell quality evaluation is improved.