426 results about "Graph encoding" patented technology

The invention belongs to the technical field of network security, provides a network attack detection method based on dynamic graph coding, and solves the problems of poor dynamic adaptability of an attack path and missing of timing constraint in the prior art. The method comprises the following steps: constructing a dynamic threat map, extracting a triple of heterogeneous threat intelligence by using a RoBERTa model, and adding a timestamp and a confidence attribute; a dynamic graph encoder for time sequence perception is designed, semantic and evolution laws are fused through periodic time coding and a multi-head time sequence attention mechanism, and feature weights are adjusted in combination with a gating residual layer; an event-driven incremental updating strategy is adopted, and node similarity is calculated to achieve local subgraph updating; a time sequence rule base is established, three-dimensional parameter verification attack chain time sequence logic is defined, and abnormity is judged through conflict scores; and finally, integrating a graph updating module, a dynamic coding module and a constraint analysis module to realize multi-source threat feature matching and attack detection. According to the method, the adaptability of attack path evolution is improved through dynamic graph modeling and real-time increment updating.

The invention relates to the technical field of big language models, and discloses a big language model training method based on knowledge graph enhancement, comprising the following steps: S1, constructing a multi-source heterogeneous knowledge graph; s2, coding the mixed attention heterogeneity map; s3, bidirectionally mapping a pre-training task; and S4, position specific gating fusion. According to the big language model training method and system based on knowledge graph enhancement, a same proton graph is established for a structured triple and text entity description, nodes are connected across graph edges to form a heterogeneous graph, associated edges are established through entity linking and syntactic analysis, multi-source knowledge is modeled in a unified mode, and the problem of low fusion efficiency is solved; mixed attention coding adopts a layering mechanism, a semantic level calculates weights according to type compatibility, a node level calculates similarity aggregation features through cosine distance and path length, entity vectors are generated through pooling, map structures and semantics are explicitly learned, reasoning accuracy is improved, and the problem of knowledge understanding superficial layer is solved.

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on multi-modal gating and comparative learning, which comprises the technologies of comparative learning, graph neural network, cross-modal alignment, gating attention and the like. Firstly, data standardization and graph construction are carried out, and molecular fingerprint embedding is extracted; secondly, a heterogeneous dual-channel graph coding architecture is adopted, one path captures atom short-range interaction through an attention mechanism, the other path integrates a molecular global structure and long-range dependence, and complementary molecular representation is generated; then, a cross-modal attention mechanism is introduced, bidirectional association of graph and fingerprint features is achieved, and modal weights are adaptively and dynamically distributed through a gating fusion module; and finally, a comparison pre-training strategy is adopted, a molecular graph and fingerprints are utilized to construct a sample pair, and discriminative molecular representation is learned on unlabeled data. According to the method, the accuracy of molecular property prediction is remarkably improved, and an efficient and reliable calculation tool is provided for virtual drug screening and lead compound optimization.

The invention discloses a novel CAD model feature recognition method based on a graph and a point cloud neural network, and the method comprises the steps: creating a data set containing heterogeneous machining features, converting a B-Rep model into a surface adjacent graph with surface-edge attributes, a surface UV grid, and a surface point cloud input neural network model; the neural network model integrates a graph encoder, a point encoder and a multi-task head, and the graph encoder and the point encoder learn classifiable embedded vectors from shape primitives of different scales of points, edges and surfaces respectively; and the multi-task head converts the embedded vector into corresponding semantic segmentation, instance segmentation and bottom surface segmentation results through three meta-learners. According to the method, insights can be extracted from different representation forms of a CAD model, semantic segmentation, instance segmentation and bottom surface segmentation tasks of heterogeneous processing features are processed in parallel, and experiments prove that Gamp is a Gamp; the PCNet has effectiveness and superiority in non-heterogeneous and heterogeneous processing feature recognition, and has application potential in real industrial scenes.

The invention relates to a vehicle track prediction method based on interactive perception and map adaptation, and belongs to the technical field of automatic driving. The method comprises the steps of collecting a historical track and geographical semantic information of a vehicle; the method comprises the following steps of: modeling interaction between dynamic objects by utilizing Transform, and fusing map information when a state sequence is decoded; a hierarchical attention mechanism is adopted, the future state of the vehicle is considered, and the consistency and accuracy of prediction are ensured; in addition, a multi-resolution map coding module is also constructed, the module fully utilizes a Vision Transform module to efficiently capture local and global scene context information, and then map representation is closely associated with a vehicle state to accurately model an interaction process of a vehicle and a scene, so that a more reasonable future trajectory prediction result is generated. The method shows wide application potential, can effectively improve the calculation performance, and is suitable for a more complex traffic environment.

The invention relates to the technical field of computer-generated holographic three-dimensional display and deep learning, in particular to an unsupervised deep learning method for realizing three-dimensional holographic display. The method comprises the steps of generating a depth map corresponding to a two-dimensional image; the depth image and the two-dimensional image are spliced in the channel dimension to serve as input of a hologram encoder, and a double-U-Net cascade neural network architecture serves as the hologram encoder; angular spectrum diffraction back propagation is carried out through the generated pure phase hologram to realize three-dimensional scene discretization reconstruction, a reconstructed image with a specified depth is obtained, a depth map is uniformly quantized to obtain a plurality of binary masks, loss calculation is carried out on the reconstructed image and a target image superposed with the corresponding depth binary masks, network parameter optimization is carried out, and a target image with the depth corresponding to the target image is obtained. And when the training of the double U-Net cascade neural network architecture is converged, the training stage is ended. According to the method, high-quality three-dimensional hologram reconstruction is realized through layered angular spectrum propagation, and the method has relatively high precision and detail reduction capability.

The invention provides a source code vulnerability detection method and device based on a fusion code attribute graph and electronic equipment, and the method comprises the steps: obtaining to-be-detected source code data, extracting the code representation of the source code data, and converting the code representation into the fusion code attribute graph; inputting the graph coding features of the fused code attribute graph into a vulnerability detection model to obtain a vulnerability detection result; and interpreting the vulnerability detection result based on the interpretable model to obtain a fine-grained interpretation result of the code statement related to the vulnerability in the vulnerability detection result. Compared with a traditional code attribute graph, the fusion code attribute graph has more advantages in the aspect of covering a grammar structure and a function calling relation, the context correlation of codes can be reflected more comprehensively, and support is provided for accurately recognizing complex code vulnerabilities; the interpretable model interprets the vulnerability detection result, points out code statements related to the vulnerability, provides a fine-grained interpretation result, and can help subsequent developers to quickly repair the vulnerability.

The embodiment of the invention provides a video generation method and device, equipment and a medium. The method comprises the following steps: acquiring a source video, a camera track parameter and a video description text; converting the source video into point cloud data, performing point cloud rendering processing on the point cloud data according to the camera track parameters to obtain a point cloud rendering result, and encoding the point cloud rendering result into view encoding features; encoding a source video into a reference encoding feature, and encoding a video description text into a text encoding feature; converting the view coding feature into a first self-attention feature, and converting the text coding feature into a second self-attention feature; performing cross attention processing on the first self-attention feature and the reference coding feature to obtain a video interaction feature, and obtaining a multi-modal coding feature according to the video interaction feature and the second self-attention feature; and carrying out decoding processing on the multi-modal coding features to generate a target video. By implementing the embodiment of the invention, the generation quality of the target video can be improved.

The invention provides a geological data driving path optimization and settlement prediction method for pipe jacking construction, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, acquiring sparse drilling point location geological data, constructing a geological data set including soil layer types, forming continuous geological feature tensors through multi-dimensional interpolation, and generating a path vector sequence by combining starting and ending points and building distribution to represent crossing tracks of different paths under geological conditions; and constructing a path graph topological structure through a graph coding neural network, performing feature propagation, obtaining a path comprehensive score vector, and screening an optimal path meeting structural integrity and settlement response constraints. Further identifying settlement trend sensitive points by utilizing the settlement risk prediction values and the accessibility scores of the nodes, constructing a settlement trend map and calculating settlement transaction coefficients. And the transaction coefficient is fed back to a path generation link and is used for iteratively optimizing path sampling density and node distribution so as to realize dynamic correction and stable convergence of a path scheme.

The invention proposes a hard negative sampling comparison recommendation method based on a graph generation diffusion model, and the method comprises the following steps: coding a user-article interaction graph through a graph convolutional neural network, obtaining a node embedded vector, and carrying out the inner product of the node embedded vector, and obtaining a recommendation probability; the model parameters of the graph convolutional neural network are obtained through the following training steps: S1, generating a comparison view 1 and a comparison view 2 from the node embedding vector through an inline diffusion model view generator and a parameter topological noise reduction view generator; the inline diffusion model view generator comprises an inline diffusion model, and the parameter topological noise reduction view generator comprises a parameter topological noise reduction network; and S2, carrying out comparative learning on the comparative view 1 and the comparative view 2. According to the method, a signal alignment strategy is combined, a double-view generator comparison learning framework is built, and the problems that the model generation capacity is insufficient and the influence of data noise is large are solved, so that the performance of a recommendation system is improved.

The invention provides a real-time video image compression method based on deep learning, and relates to the technical field of video image compression, and the method comprises the steps: carrying out the key feature recognition through employing an attention mechanism; performing convolution training optimization on the video image sample data set by using a deep learning network structure; a self-encoder structure is designed to carry out feature map encoding compression; a video image compression adaptive network is generated through series fusion; a real-time video image frame is collected for preprocessing, and feature compression processing is performed on a standard video image frame based on a video image compression adaptive network. According to the method and the device, the technical problem that the video compression quality is reduced due to the fact that the generalization ability is insufficient in the face of various scenes and the video compression strategy is difficult to adaptively adjust according to different scenes in the prior art can be solved, the adaptive network is constructed through the combination of deep learning and the auto-encoder, and the video compression quality is improved. And the video compression strategy is dynamically adjusted according to the contents of different video images, so that the video compression quality is improved.

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 relates to a method for realizing BEV local map real-time perception by fusing SD Map, which comprises the following steps: encoding a navigation map to obtain a broken line sequence of the navigation map, and processing the broken line sequence of the navigation map by using a Transform encoder to obtain features of the navigation map; extracting features from the vehicle-mounted sensor data by using a map encoder, and converting the features into uniform BEV features; and based on a preset local map feature fusion network, fusing the features of the navigation map with the BEV features by using a multi-head cross attention mechanism to obtain fused BEV features, inputting the fused BEV features into a preset lane topology model decoder, and predicting a lane center line, traffic elements and an affinity matrix associated with the lane center line and the traffic elements. Therefore, the problems that in the prior art, a high-precision map is high in cost and difficult to maintain, and local map generation stability is poor are solved, and robustness and accuracy of a sensing system are improved.

The invention provides a method and equipment for constructing a text entity recognition model based on large model data enhancement. The method comprises the following steps: firstly, constructing an initial model comprising a preprocessing unit, a syntax dependency analysis unit, a data correction enhancement unit, a context coding unit, a syntax enhancement unit, an expression fusion unit and a sequence decoding processing unit; preprocessing the training sample to obtain a preprocessed text, and establishing a preliminary dependency graph through syntactic analysis; correcting and enhancing the text and the dependency graph by the large language model to obtain a text sequence and a dependency graph for subsequent use; encoding the text sequence to obtain an initial representation vector, and enhancing the vector in combination with the dependency graph; after fusion, decoding and outputting a prediction label containing a lexical entity label; and calculating loss by using a function containing conditional random field structure loss, judging convergence, and if not, updating parameters and continuing training until a target model is obtained. According to the method, data are optimized and expanded by means of a large language model, syntactic dependency enhancement features are combined, and the recognition capability of the complex text named entities is improved.

The invention discloses a copywriting generation method based on artificial intelligence. The method comprises the following steps: constructing a propagation technique knowledge base and a creation technique knowledge base of an advertisement copywriting according to demand information; retrieving a matched creative strategy combination from the creative technique knowledge base, and encoding the creative strategy combination as a first cue word fragment; outputting a Top-K sub-graph, and encoding the Top-K sub-graph into a second cue word fragment; splicing the first cue word segment, the second cue word segment and the target audience portrait and style control parameters, generating a complete cue word, and generating an advertisement copywriting first draft; performing quality evaluation and generating a hallucination probability graph; and outputting the finally generated advertisement copywriting until the illusion probability output by the rewritten advertisement copywriting meets the requirement. According to the method, a closed loop of copywriting generation-evaluation-optimization is realized, and the quality of the copywriting can be effectively improved through a small number of iterations.

The invention provides a ship abnormal behavior detection method based on an improved graph attention neural network, and belongs to the field of ship abnormal behavior detection. Through AIS data, a graph structure including attribute features such as ship position, speed and course is constructed, attribute features of ship tracks are expanded by using dynamic cavity graph convolution, and a space-time attention mechanism is fused into a multi-head attention mechanism, so that the model has the capability of processing time dimension features to better extract space-time features of the ship tracks, and the time-space attention mechanism is optimized. The detection effect of the abnormal behavior of the single ship is improved; in the aspect of abnormal behavior detection between ships, a two-layer graph coding structure is constructed, the first-layer graph coding adopts a coding mode of single ship detection to obtain trajectory features of the ships, and the second-layer graph coding constructs relation features between ship trajectories based on the trajectory features of each ship. Effective detection of abnormal behaviors between ships is realized in combination with the attention mechanism of the GAT, and the method has good accuracy and robustness and high practical value.

The invention discloses a human body grid reconstruction method based on Transform, and the method comprises the steps: firstly obtaining a public human body data set, and carrying out the standardization of an image in the human body data set; secondly, images in the human body data set generate a plurality of visual angle features through two branches of a front view encoder and a visual angle conversion network respectively, optimization and enhancement are carried out respectively, the optimized and enhanced features are fused, and a multi-visual angle aggregation feature is generated; and then, in combination with the optimization of a Transform encoder, initial joint features are extracted from the images in the human body data set, and final posture features are generated. And finally, constructing a grid regression module, adding the multi-view aggregated features and the attitude features, and inputting the added features to the grid regression module to generate a final human body grid reconstruction result. According to the method, the feature information of different visual angles is fused, so that the precision and detail representation of human body grid reconstruction are effectively improved under the conditions of complex posture change and shielding.

The invention belongs to the technical field of computers, and particularly discloses a method and system for integrating a code structure into a large model based on multiple modalities, and the method comprises the steps: obtaining a code attribute graph corresponding to a code text; inputting the code attribute graph to a graph encoder, and obtaining a graph structure representation output by the graph encoder; the code text and the graph structure representation are input to a large language model, a code processing result output by the large language model is obtained, and the large language model is used for processing the code according to the code processing task based on the code text and the graph structure representation; wherein the graph structure representation and the serialization language representation are kept aligned in a representation space, and the serialization language representation is obtained by performing serialization processing on a code text by a text encoder in a large language model. According to the method, the multi-modal encoder is introduced, so that different modal information, such as grammar, semantics, control flow and data flow, of the code can be fused and processed, and the understanding ability of a large model to a code structure can be remarkably improved.

The invention belongs to the field of medical vision-language pre-training, and relates to a medical vision-language pre-training method based on multi-view and text combination, which comprises the following steps of: preprocessing chest X-ray image-report data; inputting the preprocessed image into a view encoder to obtain local features VF and VL and global representations gF and gL of a positive view and a side view, wherein the local features VF and VL of the positive view and the local features VL of the side view and the global representations gF and gL of the positive view and the side view are compared with the report sequence XT of the mask and the report sequence # imgabs0 # of the mask; respectively inputting the sequence XT and # imgabs1 # into a report encoder to obtain a local feature T, a global representation gT and a mask report representation # imgabs2 #, and inputting VF, VL, T, gF, gL, gT and # imgabs3 # into a positive side view feature integration module to obtain a mask report generation text T'and a prediction probability P thereof; inputting the VF, the VL and the T into a positive and lateral feature alignment module to obtain fine-grained representations F and L of positive and lateral views; calculating a loss function value according to P, F, L, gF, gL and gT, and updating model parameters according to the loss function value until a pre-trained medical vision-language general model is obtained; according to the method, the lateral view is introduced into pre-training, so that the diagnosis accuracy is improved.

The invention relates to a power distribution network multi-source heterogeneous data fusion processing method, and belongs to the technical field of power system data processing, and the method comprises the following steps: carrying out the preprocessing of multi-source heterogeneous data collected by a power distribution network monitoring device, and generating a two-dimensional data matrix; extracting spatial topological features through a graph neural network in a space-time graph encoder, capturing time dynamic features in combination with a long-short-term memory network, and outputting a hidden state vector fused with space-time features; after the vector is input into a multi-criterion generator, an evaluation criterion is generated by a plurality of criterion sub-modules; in the process, the adversarial training module dynamically optimizes a criterion to generate a threshold parameter through a generator-discriminator architecture to form a closed-loop feedback mechanism; and finally, the dynamic output layer comprehensively optimizes the criterion, and outputs a state evaluation report including operation state evaluation, accurate fault diagnosis and a resource optimization configuration scheme through space-time correlation feature cross analysis.

The invention discloses a 3D digital human facial expression synthesis method based on audio driving and a visualization system. The method comprises the following steps: acquiring a data set; inputting the original audio in the data set into an audio encoder in the model, and extracting audio features; inputting the audio features into a KAN-based decoder in the model to generate 3D digital human facial expression actions; inputting 3D digital human facial expression actions into a graph encoder in the model, and extracting lip features; collaborative optimization and model training of multi-modal features are realized by constructing a joint loss function fusing audio features, 3D facial expression parameters and lip features; and inputting an original audio to be tested into the trained model, and generating corresponding 3D digital human facial expression actions through the audio encoder and the KAN decoder in sequence. According to the invention, the generated 3D digital human facial expression action is more vivid and closer to the real character expression.

The embodiment of the invention provides an image recognition method based on a visual language, a controller, a robot and a medium, relates to the technical field of artificial intelligence, and is suitable for the fields of financial science and technology and medical health. The method comprises the following steps: acquiring article use record information of a target user; generating an auxiliary object searching knowledge graph based on the object use record information; carrying out graph coding on the auxiliary object searching knowledge graph to obtain graph structured semantic features; collecting a scene where the target user is located to obtain a current scene picture, and performing visual coding on the current scene picture to obtain current picture features; carrying out attention fusion processing on the map structured semantic features and the current picture features to obtain map picture joint features; and obtaining an object searching natural language instruction containing the target object description information, and determining an object searching condition according to the object searching natural language instruction and the map picture joint features. According to the embodiment of the invention, the article searching accuracy in a specific scene can be improved.

The invention relates to an APT attack detection and machine learning technology, in particular to an APT attack detection method fusing comparative learning and cross-domain recommendation. A traceability graph is constructed based on a data set, and a bipartite graph is constructed according to the traceability graph; generating an r-ego network for all nodes in the bipartite graph; generating positive and negative sample pre-training graph encoders in the source domain, and transferring the pre-trained graph encoders to the target domain; generating initialization embedding in a target domain by using a pre-trained graph encoder; the matrix decomposition model is finely adjusted by using the initialized embedding, and the final embedding of each node in the target domain is obtained by using the finely-adjusted matrix decomposition model; and predicting whether the two nodes interact based on the final embedding. According to the method, the high-order connectivity is formed by using the side information of the system entities to predict the possibility of interaction between the entities. And meanwhile, a cross-domain recommendation method is used, so that the problem of data sparsity of APT attacks is relieved while the recommendation performance on a target domain is improved.

The invention discloses a graph encoder recommendation method based on guide diffusion model enhancement, and the method comprises the specific steps: S1, initializing a user-article interaction adjacency matrix, carrying out the normalization processing, and generating a Laplacian matrix; s2, adaptively calculating a mask probability for each click behavior, generating a partially visible mask bipartite graph, taking the mask bipartite graph as a mask graph encoder input in a model training process, and enhancing a supervision signal from a structure level; and S3, generating representations of the user and the article by using a mask graph encoder, performing de-noising processing by adding directed noise and introducing a diffusion model guided by a cooperative signal, and enhancing a supervision signal from a semantic level. And S4, generating user and article representations by using a graph encoder, optimizing semantic information of the representations, and finally predicting articles which may be clicked by each user. According to the method, by combining a structure and semantics dual enhancement strategy, the accuracy and recall rate of a recommendation system are remarkably improved, and meanwhile, the robustness of the system to noise information is improved.

The invention discloses a code abstract generation method oriented to semantic structure feature joint enhancement. According to the method, an encoder-decoder architecture is followed, and a multi-form combined semantic feature enhancement method is adopted to automatically generate a code abstract. Wherein the encoder learns code semantics at token and statement levels, and the decoder receives code embedding and generates a code abstract in combination with an attention mechanism. Unlike a single tree or graph coding process, the method uses graph representation to enhance the interval divided tree structure. The joint decoder aggregates the text sequence information and the structure sequence information containing the location node features, and predicts and generates a model understood code summary text. According to the method, the problems of repetition and redundancy of code texts with different lengths caused by disordered code text features are relieved. In a relatively complex code text data set, a word ambiguity phenomenon caused by insufficient knowledge information understanding is reduced, and a code abstract with relatively high quality is generated. Meanwhile, the method has good convergence performance.

The invention relates to the technical field of automatic driving risk analysis, in particular to a traffic risk analysis method and analysis device based on a graph neural network, and the method comprises the steps: sequentially carrying out the coding of a graph encoder and the coding of an attention mechanism based on the obtained historical track information of an intelligent agent and the lane line information of a map, and obtaining an attention mechanism; obtaining an attention mechanism coding result; decoding by using a cross attention mechanism based on an attention mechanism coding result; and performing prediction trajectory decoding by using a multi-layer perceptron based on a cross attention mechanism decoding result, performing decoding by using a deconvolution network based on a decoding result, calculating risk matrix loss by using a probability loss function, and performing model training and verification to obtain a risk analysis model based on a graph neural network so as to predict traffic risks. Therefore, the problems that the risk analysis depth of traffic participants is limited and future intentions and potential risks of the traffic participants are difficult to accurately describe by traffic risk analysis methods in related technologies are solved.

The invention discloses a multi-language code generation method based on self-supervised pre-training, which comprises the following steps: acquiring and cleaning multi-language code data to form a training corpus; the method comprises the following steps: representing code data as an abstract syntax tree, extracting a control flow diagram and a data flow diagram of the code data, and obtaining unified semantic representation through combination of a diagram encoder and a sequence encoder; designing a self-supervised pre-training task, and pre-training the semantic representation based on the training corpus; constructing a multi-language pre-training model based on the structure-improved recurrent neural tensor network and the multi-language embedding matrix; when a user inputs a natural language, generating a target language code by using the multi-language pre-training model; and target language code correction is carried out through conventional function testing and grammar checking. According to the method, multi-channel recursive combination and a hierarchical recursive expansion mechanism are combined with self-supervised pre-training, so that accurate generation and performability improvement of cross-language codes are realized.

The invention provides an anti-interference target detection method and system based on automatic driving scene multi-modal fusion, and the method comprises the steps: employing a semantic segmentation and instance segmentation combination method based on a camera image in an image view coding layer, and employing a Mask-RCNN model, and precisely judging a possible shielded object and region; in a feature processing link, a time self-attention mechanism is introduced, a feature map is weighted from a time dimension, an occluded object is focused, irrelevant information is inhibited, and the capability of capturing features of the occluded object is enhanced; besides, a caching mechanism is arranged in the model, a target confidence coefficient change method is adopted, the detection confidence coefficient of continuous video frames is stored, and the shielding condition is recognized by analyzing the confidence coefficient change condition of a target object in the continuous video frames. According to the invention, the detection precision and reliability of the shielded object are improved, and a more efficient and more accurate solution is provided for the object detection of the unmanned driving technology.

The invention provides a single Beidou differential positioning enhancement method based on NLOS signal identification, and the method comprises the steps: obtaining satellite signal data, and constructing a sky satellite map; a multipath signal identification model based on a Siamese neural network is constructed; unsupervised pre-training is carried out on a graph Transform encoder by adopting an asymmetrically enhanced sky satellite graph; performing feature extraction on the sky satellite feature map by using a trained map Transform encoder; constructing an ambiguity fixed strategy, and optimizing the ambiguity fixed strategy based on an NLOS recognition result; and calculating the position of the user by using the optimized ambiguity fixing strategy. According to the invention, the ambiguity fixing efficiency and reliability are further improved; and the position of the user is calculated through an optimized ambiguity fixing strategy, so that the positioning precision of the single Beidou satellite in the urban complex environment is improved.