310 results about "Structure learning" patented technology

The invention discloses a method and a device for constructing a sample efficient world model based on Transform. The method comprises the following steps: separating historical interaction data to obtain historical action sequence data and historical original state data; converting the original state data into multi-scale feature data and carrying out denoising enhancement processing to generate enhanced state data; performing adaptive quantization coding on the enhanced state data to obtain discrete latent variable data; causal structure data are constructed based on historical data, attention weights are designed, and causal enhancement representation data are generated; calculating uncertainty data based on the time sequence association data and generating multi-step prediction sequence data; decomposing the long-term prediction target to obtain layered value data and calculating strategy gradient data; and screening the new interaction data and updating model parameters. Through the methods of multi-scale feature extraction, causal structure learning, hierarchical value evaluation and the like, the prediction accuracy and the sample utilization efficiency of the world model are improved.

The invention discloses a concept recommendation method based on large language model structure learning and knowledge representation, and belongs to the field of concept recommendation. The method comprises the following steps: firstly, acquiring learning data and a concept knowledge graph of a student account in a learning process, and preprocessing the learning data and the concept knowledge graph; performing text coding on an enhanced text generated by the large language model by using a pre-training language model to obtain a final hidden vector of each concept, further constructing an enhanced concept knowledge graph, and pre-training a GraphGPS concept encoder based on the enhanced concept knowledge graph and a double-view cross graph contrast learning method; synthesizing education data by a TabDDPM model to pre-train a deep knowledge tracking model; and splicing the concept representation of question embedding, answer embedding and structure perception and the student knowledge state representation to form a final representation, further obtaining a coding sequence, pre-training a sequence coding model and finely adjusting a concept recommendation matrix, and finally forming a concept recommendation model for performing concept recommendation.

Systems and methods for unsupervised multi-modal causal structure learning for root cause analysis. System logs of a cloud system can be transformed to time-series data using a log-tailored language model to obtain system log features of the cloud system. A metric causal graph and a log causal graph can be predicted from modality-specific representations and modality-invariant representations of extracted system metric features and system log features, respectively, using the deep neural network. The metric causal graph and log causal graph can be fused to obtain a fused causal graph. Root causes of system failure can be flagged for system maintenance based on ranked entities obtained from the fused causal graph to obtain flagged root causes. System maintenance can be performed autonomously based on the flagged root causes from identified system entities to optimize the cloud system with an updated configuration.

The invention relates to the field of new energy, and discloses a wind power generation power prediction method based on space-time diagram convolution and gating attention, and the method comprises the steps: obtaining the geographic position information, meteorological information and historical wind power generation power data of each fan in a wind power plant, and obtaining the normalized data; constructing a dynamic adjacency matrix based on the maximum information coefficient among the historical power data of each fan in the wind power plant, and generating a graph structure; a node set of the graph structure corresponds to each station in the wind power cluster, and an edge set is dynamically determined by a maximum information coefficient of historical power data between the stations; spatial feature extraction is carried out by using a graph convolutional network, and a graph structure learning module is introduced; and inputting the sequence output by the graph structure learning module into a gating circulation unit, introducing an Informer encoder based on a sparse attention mechanism, and generating a wind power prediction result of a future time step. According to the invention, high-precision prediction of the wind power generation power in a multi-fan scene is realized.

The invention relates to the technical field of electroencephalogram signal processing, and discloses an electroencephalogram emotion recognition method and system based on a deep neural network. The method comprises the following steps: collecting and preprocessing a multi-channel EEG signal; constructing a graph data structure, extracting multi-domain features by taking electroencephalogram channels as nodes, and constructing a self-adaptive dynamic adjacency matrix; constructing a graph convolution long and short-term memory network, learning spatial features by GNN, and extracting time dependence by LSTM; enhancing emotion capture by using a multi-scale time-frequency feature fusion method in combination with STF and CWT; constructing global topological information of an FCN brain extraction region in combination with brain network features; and outputting alertness and other emotion indexes by means of the classification model. According to the method, graph structure learning and time sequence modeling are combined, EEG signal emotion recognition is optimized, and personalized adaptation and emotion recognition accuracy is improved.

The invention discloses a large model tool calling hierarchical dynamic optimization method and system based on reinforcement learning, and provides a model training mode based on a hierarchical decoupling architecture, which is characterized in that a reward mechanism is adjusted to form a format + tool calling correctness reward, so that the reward efficiency is improved. The correctness rewards are decomposed into three-level verification of names, parameters and values, and formats and correctness reward weights are dynamically adjusted in the training process; thus, the model realizes progressive training from basic structure learning to complex strategy optimization, the generalization ability of the model is enhanced, and fine-grained feedback in the training process is also realized, so that the model can perform gradient updating aiming at specific errors, and the accuracy of the model is improved. The problems of low training efficiency and poor model output accuracy in the traditional technology are avoided; therefore, according to the method, the generalization ability, the training efficiency and the output accuracy of the model are improved, so that the method is very suitable for large-scale application and popularization.

The invention relates to the technical field of medicine sales inventory management, and discloses a medicine sales inventory management system based on artificial intelligence, which comprises a multi-modal anomaly detection module, a multi-modal anomaly detection framework is constructed, and medicine sales anomaly is detected by using an isolated forest algorithm, a depth auto-encoder and a sequence anomaly detection algorithm; the causal structure learning and attribution analysis module is used for constructing a drug sales causal structure learning and attribution analysis system and analyzing abnormal reasons by adopting a NOTEARS algorithm and an SHAP attribution algorithm; the intervention strategy generation and evaluation module is used for constructing an intervention strategy generation and evaluation system and simulating different strategy effects through a causal intervention formula; and the inventory strategy optimization and execution module is used for implementing inventory strategy optimization and execution and dynamically adjusting inventory management. According to the invention, automatic detection, cause attribution and intelligent correction of sales abnormity are realized, and a closed-loop management system is formed.

The invention discloses a neurodegenerative disease diagnosis method for constructing a dynamic brain network based on adaptive graph structure learning, and belongs to the technical field of artificial intelligence. The invention provides a self-adaptive graph structure learning multi-level nerve disease diagnosis framework, which can automatically learn and discover complex and deep connection modes among a plurality of brain areas so as to construct a dynamic brain network. According to the method, a global structure of a brain network is embedded into brain network construction based on a time window through a window mapping module, so that richness and accuracy of brain network topology feature representation are improved; in the fusion process of window features, a learnable time graph convolution module is provided to automatically capture time connectivity across time windows and effectively integrate high-order dynamic topological features extracted from different time windows. And finally, disease diagnosis and classification are realized by using a multi-layer perceptron. According to the method, the identification and classification accuracy of the neurodegenerative diseases under the fMRI data can be effectively improved.

The invention relates to the technical field of organization transformation management and data analysis, and discloses a knowledge graph-based influence prediction method and system, and the method comprises the steps: constructing a double-layer knowledge graph which integrates an organization formal hierarchy and an informal social network; processing the double-layer atlas structure by applying a hierarchical graph convolutional network, and calculating multi-dimensional influence features; analyzing based on the multi-dimensional influence features, capturing an organization structure evolution rule by applying a time-varying graph structure learning algorithm, and identifying key opinion leaders; the key opinion leader information is utilized, the organization influence propagation process is simulated based on the Agent technology, and different intervention strategy effects are evaluated; generating a multi-dimensional influence prediction result; compared with a traditional method with a single network structure, the method has the advantages that the group influence prediction accuracy is improved, the key opinion leader recognition accuracy is improved, and the time sequence prediction error is reduced.

The invention discloses a learning achievement prediction method based on learning behavior data mining and a computer device. The method comprises the steps of collecting multi-dimensional learning behavior data of students, performing preprocessing and feature extraction, constructing a structured learning behavior feature set, then constructing a target machine learning model suitable for different prediction tasks, inputting structured learning behavior features of the students to be predicted into the model, and obtaining a continuous cumulative risk prediction curve; converting the cumulative risk prediction curve into discrete classification labels, and generating a classification prediction result with reserved time sequence characteristics; and expanding the structured learning behavior feature set by adopting a pseudo-label semi-supervised learning algorithm, iteratively training a target machine learning model, inputting the structured learning behavior features of a to-be-predicted student into the trained model to obtain a comprehensive prediction result, and generating a visual learning achievement prediction scheme according to a classification prediction result. The method can provide a comprehensive and accurate prediction result.

The invention discloses an industrial Internet of Things time sequence self-supervision anomaly detection method and a monitoring and early warning system, and relates to the field of industrial Internet of Things, and the method comprises the steps: S1, constructing an anomaly detection model, and S2, obtaining a training data set; s3, training and optimizing an anomaly detection model; s4, acquiring to-be-detected data in real time; s5, performing anomaly detection analysis on the to-be-detected data, and outputting an anomaly detection result; through a time sequence and relation learning module, a dynamic graph topological structure learning module and an enhancement module, internal characteristics of a time sequence in a time domain and a space domain are deeply mined. The time sequence and relation learning module comprehensively captures a multi-scale time pattern, and the dynamic graph topological structure learning module eliminates dependence on a predefined graph structure; the enhancement module enhances the invariant representation under noise, and improves the recognition capability of the model to a normal mode; through wide experiments, the advancement of the method in detection performance is verified, and reliable support is provided for intelligent manufacturing and infrastructure diagnosis.

The invention discloses a structured data self-learning method based on a graph neural network, and the method comprises the following steps: S1, analyzing structured data, extracting entity fields and relation fields, and constructing a structure candidate graph; s2, generating a node embedding feature matrix, and initializing and recording the adjacency relation of candidate edges; s3, constructing a graph neural network model, inputting node features and an adjacent matrix, and defining a task loss function; s4, evaluating the gradient contribution degree of edge connection by adopting a gradient sensitive sparse adjacency self-learning algorithm, and updating the graph structure representation; s5, introducing an embedded interpretability gradient backtracking mechanism, correcting an edge connection relation and enhancing interpretability; s6, training the graph neural network by using the corrected structure, and updating the node embedding and graph structure; and S7, outputting a final graph structure and an interpretability index, and generating a graph modeling visualization result. According to the method, efficient modeling and explanatory analysis of structured data are realized through a dynamic graph structure learning and gradient backtracking mechanism.

The invention relates to the technical field of data anomaly detection, in particular to an intelligent treatment anomaly detection method and system based on dynamic space-time hypergraph evolution. Self-adaptive modal decoupling and multi-view embedding are carried out based on the obtained original observation data to obtain initial node representation, and the initial node representation comprises variational modal-based signal decoupling and multi-view space-time embedding coding; performing dynamic evolution hypergraph structure learning based on the obtained initial node representation to obtain a deep feature tensor, including dynamic hyperedge generation based on metric learning and space-time hypergraph convolution evolution; performing multi-scale time sequence prototype memory prediction based on the deep feature tensor, including multi-scale time sequence feature extraction, prototype memory reading and reconstruction and future prediction of reconstructed features; according to the method, the problems that sudden anomalies are difficult to pre-judge and the depth model lacks interpretability are solved.

The invention discloses a scraper conveyor remaining service life prediction method, medium and equipment, and relates to the technical field of remaining service life prediction.The scraper conveyor remaining service life prediction method comprises the steps that multiple pieces of original graph data are constructed for obtained different sensor data of a scraper conveyor; taking graph data constructed after preprocessing as the input of a model through a convolution layer, extracting features, obtaining a dynamic dependency relationship between sensors through a graph structure learning layer, and fusing the spatial correlation of the sensors; spatial features of aggregation nodes are updated through a graph attention network in a space-time fusion layer, then a time attention mechanism and a bidirectional gating circulation unit are utilized to extract a dependency relationship between sequences, and finally fused space-time features are input into a full connection layer to determine the final remaining service life, so that life prediction of the scraper conveyor is realized.

The invention discloses a power plant secondary circuit fault tracing method based on causal reasoning. The method comprises the following steps: collecting and preprocessing multi-source operation data of a power plant secondary circuit system; based on the secondary circuit schematic diagram, the relay protection configuration table and the signal connection relation, establishing a topology constraint set, and generating an intervention node set; constructing an enhanced causal Bayesian network model based on the topology constraint set and the intervention node set; based on the topology constraint set, carrying out structure learning and parameter estimation on the enhanced causal Bayesian network model; inputting the standardized operation data set into the trained enhanced causal Bayesian network model, and executing reverse causal reasoning; and performing consistency verification on the fault root cause candidate set, the propagation path set, relay protection logic and interlocking rules, and generating a fault traceability report. According to the method, the enhanced causal Bayesian network is adopted, so that the power plant secondary circuit fault can be accurately traced.

The invention discloses a diversified epiphyseal development map generation method and device based on a potential diffusion model. The method comprises the following steps: making a data set, covering and collecting an original X-ray film image, preprocessing data, labeling a skeleton maturity level and desensitizing the image; an original image is encoded to a potential space by using a variational auto-encoder, and the image detail generation capability is improved by optimizing a loss function; a fuzzy learning module is introduced in the de-noising stage of the diffusion model, and the diversity of generated skeleton features is enhanced through operations such as batch normalization and random disturbance; a progressive alignment strategy is adopted, skeleton basic structure learning is emphasized in the initial stage, a skeleton grade evaluation network is introduced in the later stage, and skeleton detail features are emphasized; optimizing the generation model by using the skeleton recognition model; and inputting development levels, ages and genders of 14 skeletons, and generating a corresponding left-hand skeleton X-ray film image. According to the method, diversified and high-quality skeletal development images are generated through a potential diffusion model framework in combination with VAE optimization, an FLM module and a progressive alignment strategy, skeletal development features are truly reflected, the problems of detail loss and insufficient diversity in the prior art are solved, the method is particularly excellent in performance when sparse data and long-tail distribution are processed, and the method is suitable for large-scale popularization and application. And the authenticity of the generated image and the practicability of medical diagnosis are improved.

The invention discloses a quality detection method by combining a time sequence feature extraction module with a graph neural network, and relates to the field of data acquisition and analysis, and the method comprises the steps: 1, remodeling a one-dimensional time sequence into a group of two-dimensional tensors based on a plurality of cycles, processing the two-dimensional tensors through an Inception module, converting the learned two-dimensional representation into a one-dimensional space, and carrying out the processing of the two-dimensional tensors through an Inception module; and finally, aggregating different one-dimensional representations to obtain one-dimensional time sequence data. Step 2, inputting the one-dimensional time sequence data into the graph structure, regarding each node of the graph structure as a sensor, introducing a randomly initialized sensor code for each sensor, and then performing graph structure learning to obtain the weight of the edge of the graph structure; step 3, fusing one-dimensional time sequence data and sensor codes by introducing a feature extractor, calculating an attention coefficient between adjacent nodes, performing normalization processing on the attention coefficient, and diffusing and updating node states; and step 4, classifying the quality grades of the products.

The invention discloses a large language model semantic enhancement collaborative filtering recommendation method based on comparative learning, and belongs to the field of recommendation systems.The method comprises the steps that a user-item bigraph is constructed, user-item semantic information collaborative information is provided through GNN, user-item semantic information is extracted through cue words, a deterministic topology view enhancement strategy is adopted, and user-item semantic information collaborative filtering recommendation is achieved. According to the method, a semantic neighbor extension view is generated, a semantic neighbor reconstruction view is generated, collaboration and semantic information alignment are performed, double-view structure alignment is performed, and a loss function is integrally trained, so that the accuracy and the cold start capability of a recommendation system are remarkably improved through collaborative graph structure learning and semantic enhancement.

The invention relates to an industrial internet multilayer causal motif abnormal propagation path identification method and system, and the method comprises the steps: firstly carrying out the construction and extraction of a multilayer high-order motif, extracting a motif unit which expresses the local high-order structure features through the construction of a semantic hierarchical graph structure in combination with a frequent sub-graph mining and cross-layer motif alignment mechanism, and carrying out the recognition of the abnormal propagation path of the multilayer causal motif. Stable and uniform multi-layer motif representation is formed; then, on the basis of the structural equation model, motif variables are regarded as endogenous variables of a causal model, a causal path between motifs is mined by introducing conditional mutual information and a Bayesian structure learning algorithm, an average causal effect is calculated to construct a causal consistency matrix, and causal community division is realized in combination with a weighted modularity optimization method; and finally, quantifying the dynamic change of a community causal structure by constructing a causal deviation graph between an expected causal graph and an observed causal graph, and assisting in identifying a causal-driven abnormal propagation path. According to the method and the system, accurate detection and causal traceability of equipment-level and subsystem-level abnormal modes in an industrial system can be realized.

The invention discloses an IOT (Internet of Things) equipment fault prediction method based on GraphRAG. The IOT equipment fault prediction method comprises the following steps: step 1, collecting and preprocessing multi-source heterogeneous data of various IoT equipment terminals; 2, constructing a graph structure based on the preprocessed data, a dynamic edge weight mechanism and a knowledge sub-graph; according to the graph structure, physical connection, functional dependence and communication topology between IOT devices are naturally expressed, so that the relevance between the operation states of the devices is fully captured. Sensor data, historical maintenance records and equipment configuration information are coded into node attributes and edge weights, so that the system can realize unified representation of multi-source heterogeneous data. The GraphRAG framework combines the structure learning ability of a graph neural network and the knowledge fusion characteristic of a retrieval enhancement generation mechanism, and shows stronger generalization ability in the recognition of a non-fault mode. When an abnormal signal occurs in a certain device, the model can trace the potential influence range through the state propagation path of the adjacent node, and the accuracy of early warning is improved.

The invention relates to the technical field of satellite internet, and discloses a low-orbit satellite phased array multi-beam interference modeling and suppression method and system, and the method comprises the steps: selecting a Kaiser window as a core filtering method, and achieving the optimization of beam characteristics through the dynamic adjustment of a shape parameter beta; generating an initial beam directional diagram based on a digital phase matching method, and multiplying the Kaiser window function coefficient by the excitation weight of the 64-array-element linear array element by element to realize spatial domain weighted filtering; the method comprises the following steps: constructing a training data set containing multi-scene interference characteristics, calculating a corresponding covariance matrix and an accurate inverse matrix thereof to form a sample pair, designing a deep neural network architecture, inputting a flattened covariance matrix vector, and learning a complex nonlinear mapping relation from the covariance matrix to the inverse matrix through a multi-layer full-connection structure; a mean square error is used as a loss function to constrain network output precision, and a multi-beam interference system model is constructed; according to the invention, stable and efficient communication of the low-orbit satellite system in a complex electromagnetic environment and under rapid channel change is ensured.

The invention provides a multi-view structure learning method based on multi-expert cooperation, and relates to the technical field of graph neural networks and multi-view learning, and the method comprises the steps: reconstructing initial multi-view data, and obtaining multi-view structure data; respectively training the single-view expert model and the shared expert model by utilizing a first loss function and a second loss function based on the multi-view structure data to obtain a trained single-view expert model and a trained shared expert model; constructing a collaborative decision model by using the trained single-view expert model and the trained shared expert model; training the collaborative decision model by using a third loss function to obtain a trained collaborative decision model; and analyzing the graph data by using the trained collaborative decision model to obtain a joint decision result, and completing learning of the multi-view structure. According to the method, the problems of large structural noise interference, isolated expert model information and insufficient node classification accuracy when an existing graph neural network processes multi-relation graph data are solved.

The invention provides a scientific research project expert matching method based on dynamic academic cooperation network graph representation learning, and aims to improve expert matching efficiency and quality in scientific research project management. According to the method, a dynamic complex network diagram is constructed, scholars serve as nodes, cooperation relations serve as directed edges, and the previous research history and cooperation experience of the scholars are comprehensively considered. And feature vector learning is carried out on scholar nodes by utilizing graph structure learning and a Transform architecture, so that accurate matching between scientific research projects and experts and scholars is realized. According to the method, more than 80% of matching precision and more than 89% of standardized discount cumulative earnings are obtained from data sets in the fields of artificial intelligence and data mining, the limitation of the prior art in the aspects of operation cost and scholar feature information utilization is effectively solved, and an innovative solution is provided for recommending appropriate partners for scientific research projects.

The invention belongs to the technical field of article recommendation, and provides an article recommendation method and system based on big language model enhanced graph representation learning, and the method comprises the steps: obtaining a user-article graph recommendation data set; extracting semantic knowledge features of the obtained graph recommendation data set; according to the extracted semantic knowledge features and an article recommendation model, completing article recommendation learning; wherein the article recommendation model adopts a big language model to enhance a graph representation learning model, embeds semantic similarity through the big language model, adopts an adaptive graph structure learning mechanism to identify a semantic boundary so as to determine structure information, and performs bidirectional knowledge interaction transmission on semantic and structure information; interactive noise is filtered and relieved in combination with information bottleneck regularization, optimization of an article recommendation model is carried out with the purpose of minimizing a comprehensive multi-objective loss function, and article recommendation is completed by calculating preference scores of articles.

The invention discloses a small sample remote sensing image classification method based on hierarchical spatial structure learning. The method comprises the following steps: firstly, extracting multi-scale features of a remote sensing image by using a ViT (Visual Transform) model, and capturing rich semantic information and spatial structure relationships in the image; secondly, constructing a graph structure based on spatial adjacency and attention weight to model a structured relationship between samples, and encoding graph node features through a graph convolutional network (GCN) so as to enhance the discrimination ability of the features in a structural semantic space; thirdly, a residual enhancement mechanism is introduced to fuse global semantic information, and the discrimination capability of graph embedding is improved; then, based on the structural similarity between the support set and the query set, performing classification decision, and realizing accurate classification under a small sample condition; and finally, carrying out joint optimization on the whole model by adopting a training strategy of a small sample meta learning task and a supervision loss function.

The invention discloses a traffic flow prediction method and system of an adaptive graph learning space-time neural network. The invention provides a self-adaptive graph structure learning module, which comprises the following steps of: firstly, constructing a graph adjacency matrix consisting of a predefined adjacency matrix and an adjacency matrix which can be directly learned by training parameters; thirdly, a space-time attention module is designed to learn a space-time attention matrix, the traffic condition is predicted by stacking a plurality of space-time layers, and each space-time layer is subjected to convolution in the space dimension and the time dimension; meanwhile, a two-stage training strategy is provided to improve the performance of the model. According to the method, the space-time dependence of the traffic network can be adaptively learned from the data, and the utilization of a predefined structure is reserved at the same time, so that the prediction accuracy and generalization ability are enhanced. The application of the method in a traffic flow prediction system shows that the method can accurately predict the traffic flow in real time, so that traffic control and optimization strategy formulation are effectively guided.

The invention provides an industrial process fault detection method based on a space-time causal diagram autoencoder, and the method comprises the steps: 1, carrying out the data preprocessing of the space-time process data of all process variables collected in the operation process of a target industrial process for the target industrial process; step 2, establishing a causal graph space-time auto-encoder CGSTAE; 3, executing a three-step causal graph structure learning algorithm to realize training of a causal graph space-time auto-encoder CGSTAE, wherein the training comprises three steps of pre-training, causal extraction and fine tuning; and step 4, obtaining a fault detection result based on hidden layer features of the causal graph space-time auto-encoder CGSTAE and residual data output by reconstruction. According to the method, effective process monitoring and fault detection are realized by constructing two statistical magnitudes in a feature space and a residual space. Compared with other methods, the fault detection method provided by the invention can improve the reliability and interpretability of industrial process monitoring.

The invention provides an unmanned aerial vehicle fault important characteristic parameter optimal selection method and system based on causal reasoning and a medium, and the method comprises the steps: S1, collecting fault data which comprises flight fault data collected by a gyroscope, a GPS, an airspeed meter and the like; s2, performing data processing on the fault data; s3, causal structure learning: performing causal structure learning on the data after data processing through a VAE decoder and a VAE encoder in sequence to obtain a weighted causal DAG representing a causal relationship between the features; and S4, important fault feature selection: after the weighted causal DAG is obtained, node scores are calculated, then the node scores are sorted in a descending order, the top F fault feature variables are selected as feature subsets, and F is a positive integer. The method can effectively screen out the characteristic parameters of the fault of the unmanned aerial vehicle, can be used for fault diagnosis, facilitates the reduction of the fault frequency of the unmanned aerial vehicle, and improves the safety and reliability of the unmanned aerial vehicle.

The invention discloses a nuclear power device fault diagnosis method based on feature vectors and Bayesian reasoning, belongs to the technical field of Bayesian reasoning, and particularly relates to nuclear power device fault diagnosis. The method solves the problem that a traditional Bayesian model only uses parameter values as a fault reasoning basis and ignores a large amount of parameter time-frequency feature information, so that the model is difficult to distinguish feature similar faults and realize accurate positioning of the faults, and the fault diagnosis accuracy and the model robustness are affected. The method comprises the following steps: a model learning step: performing structure learning and parameter learning on a Bayesian model according to fault historical operation data to obtain a Bayesian vector reasoning model; the nuclear power device fault diagnosis method based on the feature vector and Bayesian reasoning is suitable for fault diagnosis of a nuclear power device.

The invention relates to the technical field of water quality parameter monitoring. The invention provides a water quality multi-parameter prediction method and model based on a space-time diagram convolutional network. On the time dimension, a mask code transformer module is designed, a pre-training task is self-supervised through a random mask to enable the model to extract trend features from long-period data, and the problem of response lag of a traditional model to a sudden change event is solved in combination with an accumulative effect of capturing water quality features through expansion causal convolution. And in the spatial dimension, constructing a dynamic graph structure learning module, and fusing the predefined adjacency matrix based on the station physical distance and the dynamic residual graph to generate a dynamic graph structure. Experimental results show that compared with other water quality space-time prediction models, the model has better prediction precision, and prediction R2 of all water quality indexes can reach 93% or above.