64 results about "Topology inference" patented technology

The invention relates to the technical field of knowledge graph construction and maintenance, and discloses an AI knowledge graph construction and maintenance method based on ontology engineering, and the method comprises the steps: obtaining multi-source heterogeneous data to construct an initial graph ontology model; loading the constraint rules and the entity alignment parameters to carry out cross-modal knowledge fusion, and generating a dynamic knowledge evaluation model; executing ontology topology reasoning based on the model, and constructing time sequence knowledge evolution simulation data; training the semantic conflict resolution model to generate a knowledge fusion optimization model, and outputting map maintenance parameters; knowledge increment simulation information is generated, a multi-dimensional optimization model is constructed to iteratively adjust the ontology structure, and optimal knowledge fusion path parameters are generated; and dynamic ontology matching is realized by combining real-time reasoning and historical track updating maintenance strategies. According to the method, the construction efficiency, the fusion quality and the self-adaptive maintenance capability of the knowledge graph are improved, the method is suitable for an intelligent knowledge management scene of multi-source heterogeneous data, and the defects of a traditional method in the aspects of semantic conflict resolution, ontology dynamic evolution and the like are overcome.

The invention provides a power grid wiring diagram automatic analysis system and method based on deep learning. The method comprises the steps of S1, data preprocessing and model training; s2, carrying out equipment identification and association; step S3, topology reasoning and verification are carried out; s4, standardized output: converting the verified power grid network model into a regulation and control cloud platform standard graphic file, and optimizing graphic arrangement through an adaptive layout algorithm; step S5, performing closed-loop optimization; and S6, performing manual intervention and iteration. According to the method, the wiring diagram analysis efficiency and accuracy can be remarkably improved, manual intervention is reduced, standardized graph generation of a power grid regulation and control cloud platform is supported, and a reliable data basis is provided for power system analysis and decision making.

The invention discloses a space-ground integrated dynamic traffic flow analysis and road topology reasoning method and device. A cloud server generates and issues real-time traffic information through multi-level processing such as map matching, traffic flow analysis, prediction and early warning and road topology analysis and updating; specifically, firstly, the high-precision positioning and communication capability of a low-orbit satellite is utilized, data fusion of an inertial measurement unit and a wheel speed sensor is combined, and a multi-source fusion algorithm is constructed to improve the positioning precision; secondly, traffic flow dynamic analysis and prediction are carried out based on a Transform interaction analysis model, and real-time updating of a road topological structure is realized at a cloud end by using a clustering algorithm and a graph theory model; finally, the traffic flow and road topology information is sent to related vehicles and a traffic management system, and real-time monitoring, early warning and optimization of the traffic state are achieved. According to the invention, the accuracy and real-time performance of traffic information acquisition, processing and issuing can be effectively improved, and the intelligent level of traffic management and decision making is enhanced.

The invention discloses an Internet of Things equipment topological relation identification method based on a graph neural network, and the method comprises the steps: capturing a time sequence evolution mode of historical connection and communication states of equipment through fusion of a time sequence attention mechanism and a self-adaptive time sequence attenuation factor, and constructing an initial equipment relation graph; carrying out incremental learning by adopting a quantum heuristic optimization algorithm, and dynamically updating the topological structure of the equipment; extracting a cross-protocol communication mode by using a meta-path guided graph convolution operation, and generating a graph structure fingerprint irrelevant to a protocol; a zero sample learning framework is combined to map into a standardized embedded vector; and deploying a hierarchical federated learning framework to execute topological reasoning on edge equipment, and dynamically outputting an equipment relation confidence score. According to the method, the real-time performance and compatibility of topological relation identification are improved, and the method is suitable for a heterogeneous Internet of Things environment.

The invention provides a causal enhanced soft measurement modeling method based on joint causal topological reasoning. The method comprises the following steps: firstly, obtaining a joint causal weight between an input variable and a target variable based on multivariable joint causal topological reasoning of a Copula theory; secondly, correcting an ordered prior structure based on dynamic feedback of independent confidence, and performing feedback correction on expert knowledge based on data contribution degree; then, based on correlation graph structure enhancement guided by causal weights, the causal weights are fused on the basis of correlation, a causal graph structure matrix is obtained, meanwhile, a fusion causal time sequence graph network is designed, and extraction of causal and time sequence information and final modeling of target variables are achieved with alignment loss as a constraint. According to the method disclosed by the invention, the correlativity in a data form and the causal relationship in an expert experience form can be organically combined to realize soft measurement modeling with high stability and interpretability, and the method can be widely applied to industrial sites with requirements on soft measurement modeling.

The invention provides a multi-modal fusion road topology reasoning method and system, and the method comprises the steps: converting an extracted lane mask into a semantic seed point in a historical point cloud, and carrying out the pseudo-label setting based on the semantic seed point. And performing model training based on the historical image with the pseudo tag and the historical point cloud to obtain a lane detection model. Obtaining lane line information from the real-time image and the real-time point cloud by using a lane detection model, mapping the lane line information to a bird's-eye view feature map, calculating a topological relation between lane lines based on the lane line information in the bird's-eye view feature map, obtaining a topological reasoning matrix, and obtaining a topological reasoning result; and obtaining a corresponding road topology reasoning result based on the graph neural network according to the topology reasoning matrix. According to the scheme, on the basis of a pseudo label generation mechanism, dependence on manual marking is reduced, data preparation cost is reduced, effective fusion of a lane line representation method based on bird's-eye view space unified mapping and lane topology reasoning is introduced, and a lane topology reasoning result with high precision and good topology consistency can be obtained.

The invention discloses a defect hidden danger data diagnosis method based on a distribution network SVG single line diagram. The method comprises the steps that basic analysis is conducted on the input SVG single line diagram, basic geometric figure elements are extracted and normalized, a topological connection relation is preprocessed, and a multi-level index structure is constructed; a dynamic topology path reasoning mechanism is constructed, and reasoning and self-adaptive optimization of the topological relation of the electrical equipment are realized through path consistency calculation, topological entropy minimization and a dynamic topology updating strategy; a self-adaptive implicit relational knowledge graph is constructed, and semantic association between the electrical equipment is established through explicit topological relation fusion, implicit function dependence reasoning and dynamic semantic embedding optimization; and constructing a multi-modal time sequence feature vector, calculating the hidden danger risk of the equipment by adopting a space-time defect probability estimation model, and giving an alarm through a dynamic risk early warning strategy to realize prediction and early warning of the hidden danger of the defects of the electrical equipment. According to the method, the problems of semantic understanding, topological reasoning, state inference and defect prediction of the SVG single line diagram are effectively solved.

The invention relates to the technical field of Beidou inspection data processing, and discloses an automatic inspection data acquisition and space-time association method under Beidou space-time reference. The method comprises the following steps: acquiring historical working condition data of inspection equipment based on nanosecond-level time synchronization and centimeter-level space positioning information of a Beidou system, and constructing a space-time dynamic prediction model; acquiring an equipment sensor measurement value, a moving track point set and a timestamp sequence in real time, and outputting a theoretical inspection value through a model; performing space-time multi-dimensional comparison on the theoretical value and the actual value, analyzing a time accumulation error, a space deviation degree and data flow form consistency, and generating a difference feature tensor; inputting the tensor into a geocoding topological reasoning network, simulating anomaly diffusion and generating an anomaly probability cloud picture in combination with topographic map node attributes and equipment distribution, and delineating an anomaly range; polling parameters are dynamically configured according to the cloud atlas, enhanced collection is started for a high-probability abnormal area, and data credibility inspection is executed on peripheral equipment.

The invention provides a low-light road perception and topological reasoning method and system based on track prior, and the method carries out the enhancement processing of a low-light image through an image pre-training model, thereby restoring the details of the image, improving the brightness of the image, and reducing the distortion phenomenon of the image in the enhancement process. Moreover, crowdsourcing trajectory data is effectively utilized as prior information, and the rasterized thermodynamic diagram and vectorized trajectory information are constructed in combination with the trajectory prior data, so that the accuracy and robustness of the map construction process are enhanced, and the difficulty and cost of obtaining the prior information are reduced. According to the scheme, the capability of dealing with an automatic driving scene under the conditions of low illumination and complex illumination can be effectively improved, and the road perception and topological reasoning precision is improved.

The invention discloses an HAZOP analysis method and system based on digital delivery, and relates to the technical field of chemical safety risk analysis, and the method comprises the steps: obtaining to-be-analyzed digital delivery data; carrying out digital delivery model integration and dynamic verification by adopting a topological correlation theory and a rule engine method; performing multi-source process data standardization processing and risk element mapping by adopting a statistical learning and semantic matching method; performing model-driven deviation intelligent generation and causal chain analysis by adopting a topological reasoning and analogue simulation method; carrying out cross-professional collaborative optimization and measure closed-loop tracking by adopting a collaborative filtering and closed-loop control method; and carrying out full-life-cycle data linkage and iterative optimization by adopting a feedback learning and knowledge iteration method to generate a standardized report. According to the method and the system, full-process digitization, intelligentization and full-life-cycle management and control of chemical process risk analysis can be realized, and the precision and the efficiency of HAZOP analysis are improved.

The invention relates to the technical field of underground pipeline detection, in particular to an underground nonmetal pipeline detection positioning method and system. The method comprises the steps that multi-source heterogeneous data such as road surface vibration, echo imaging, a thermal radiation sequence and environment field intensity in a target area are acquired and preprocessed, and an original observation set containing spatio-temporal information is constructed; time-frequency joint representation of vibration and echo is realized based on a self-supervised multi-mode feature coding network, thermal radiation texture anomaly is extracted by a space-time convolution converter, and domain-invariant high-dimensional features are formed; conformal attention cross-modal alignment is adopted, the features are mapped to a unified hidden space, key boundaries are screened through gating mutual information, and a comprehensive feature map containing dielectric constant gradient and elastic wave guiding characteristics is generated through fusion; and carrying out topological reasoning by combining a graph neural network with a as-built graph and facility position priori, generating a candidate pipeline network, checking pruning by ray tracing arrival time consistency and a thermal diffusion time delay rule, and outputting pipeline space distribution and three-dimensional coordinates.

The invention discloses a network topology generation method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: collecting network equipment operation data from a plurality of heterogeneous data sources, and generating equipment relation feature data based on the network equipment operation data; inputting the equipment relation characteristic data into a topological reasoning neural network model so as to predict a connection relation between network equipment and a network hierarchical structure by using the topological reasoning neural network model, and generating topological structure data; determining a corresponding target layout algorithm according to network topology characteristics represented by the topological structure data, and generating a network topological graph based on the topological structure data by using the target layout algorithm; and pushing the network topological graph to a visual interface for rendering display. According to the invention, the accuracy of network topology generation is improved.

The invention discloses a dynamic optimization method, device and equipment based on heterogeneous network topology and a storage medium, which are applied to the technical field of network optimization, and the method comprises the following steps: obtaining equipment state data of a target network; processing the equipment state data to obtain equipment fingerprint data; processing the equipment state data and the equipment fingerprint data by using an incremental maximum likelihood topological reasoning technology to obtain a real-time topological graph; inputting the real-time topological graph into a fault diagnosis model for processing to obtain a root cause analysis result; performing state modeling processing on the traffic time sequence data, the equipment fingerprint data and the root cause analysis result by using a generative technology to obtain a deduction result; and performing strategy optimization processing on the deduction result by using a multi-agent reinforcement learning technology to obtain a network dynamic optimization scheme. The method provided by the invention can solve the technical problems in the prior art that the network management response is lagged, the flexibility is poor, and the global state is difficult to perceive in real time and the fault is difficult to locate accurately.

The application discloses a kind of complex component full-path weld extraction method and system based on multi-dimensional feature and topological reasoning, belong to robot welding vision technical field.Aiming at the problem that box type, I-beam and other components are not complete due to structural obstruction, prone to misjudgment of rolling fillet and other pseudo-weld, the application collects three-dimensional point cloud and two-dimensional image to extract initial geometric intersection line;Fusion three-dimensional gap depth mutation and two-dimensional point solid weld texture features, true and false discrimination and weighted screening are carried out on initial intersection line;Based on real weld and intersection, a space topological graph with node degree attribute is constructed;Using node degree constraint and orthogonal priori, collinear extension and orthogonal complete reasoning are carried out on suspended breakpoint, and complete welding path is reconstructed.The application can effectively eliminate the interference of pseudo-weld, realize accurate reconstruction of full path under limited field of view, and improve the robustness and efficiency of welding automation system.

This invention relates to the field of earthquake disaster emergency monitoring technology, specifically to an intelligent system and method for identifying earthquake surface rupture zones. The system includes modules for heterogeneous data registration, feature extraction and fusion, rupture zone segmentation, active learning, and morphological optimization. It achieves accurate processing of remote sensing data and intelligent identification of rupture zones. The system employs multi-scale feature point matching and adaptive weight registration, improving data registration accuracy by over 30% and overcoming the reliance on human experience in traditional methods. Through multi-modal feature decoupling and attention-guided feature fusion, combined with multi-scale spatial pyramid pooling and deformation-sensitive segmentation, the system effectively identifies rupture zones and assesses prediction uncertainty. The active learning module optimizes training data through Bayesian estimation and a hybrid sampling strategy, while the morphological optimization module utilizes geological constraints and fault topology inference to ultimately output accurate rupture zone distribution and attribute information, improving the intelligence level and efficiency of earthquake disaster emergency monitoring.

The application discloses an IPv6-oriented network layer analysis topology detection method, and belongs to the technical field of network space security, and the method comprises the following steps: based on the improved Back-to-Back detection packet, the shared path length between each two network target nodes in a network target node set is acquired through a covariance additive characteristic quantity with a penalty term; the two network target nodes corresponding to the maximum value of the shared path length are taken as a base node pair; the basic topology structure of the base node pair is inferred by using an SRv6 detection packet, and a parent node is obtained; the SRv6 detection packet is used to select an insertion position meeting a preset condition on the basis of the parent node, and neighbor nodes are added from bottom to top to form an overall topology structure; the overall topology structures acquired by different detection sources are topologically fused through the distribution characteristics of sub-tree leaf nodes, and the detection of a logical loop is realized; the method can improve the accuracy and recall rate of topology inference, and can be implemented in a large-scale IPv6 network.

The application provides a steel plate surface defect recognition method fusing visual attention mechanism, and belongs to the technical field of defect detection based on computer vision; multi-source images of a steel plate, production line process and quality detection data are synchronously collected to construct a standardized tensor benchmark with aligned physical attributes and unified data modalities. A visual attention feature coding network fusing physical information constraints is built, and PINN constraint correction is used to remove the interference of ambient light brightness gain, so that the intrinsic reflection attribute features of defects are accurately extracted. Through spatiotemporal collaborative alignment and correlation modeling of defects and process heterogeneous data, the contribution weight of process parameters to defects is quantified by adopting double-track machine learning, and a process target evaluation baseline is built. Finally, the defect topology reasoning and multi-task prediction are completed by relying on the graph attention network GAT, the local visual features and global process constraints are deeply fused, and the defect position mask and category recognition result are output; the application significantly improves the steel plate quality inspection accuracy and system decision reliability.

The invention discloses a coronary artery segmentation method based on physical constraint and topological correction, which comprises the following steps: firstly, preprocessing a coronary artery CTA image through standardization strategies such as resampling, intensity truncation and normalization to generate a golden standard topological graph; then, a deep learning model containing global-local mixed feature extraction, graph neural network topology reasoning and physical perception of flow resistance loss is constructed, CNN and Swin Transform are utilized in parallel to extract features, a multi-scale tubular perception unit is introduced to enhance small blood vessel response, GCN is utilized to predict blood vessel topology fracture, and the blood vessel topology fracture is corrected through micro layering; finally, a flow resistance loss function based on the Poiseuille law is utilized, the network is forced to conform to fluid mechanics connectivity in training, high-precision and function-reliable segmentation can be output in complex scenes such as calcification and low contrast without manual post-processing, and the efficiency and the automation level of CT-FFR analysis are remarkably improved.

The application provides a kind of vehicle-mounted detection equipment data processing method and device applied to fault detection, by the time sequence scene correlation modeling of original operation data collected by vehicle-mounted detection equipment, generate time sequence scene correlation mapping body and abnormal starting time sequence marker cluster, and based on both, construct fault correlation semantic topology, generate fault semantic correlation graph and scene-semantic correlation mapping table, then carry out fault mode qualitative clustering, generate fault mode classification label set and fault-scene correlation binding cluster, then carry out fault root cause topology reasoning, construct the logical correlation topology chain of fault explicit semantic representation and potential cause, generate fault root cause reasoning topology chain and fault propagation topology path set, based on both, carry out fault risk trend topology deduction, generate fault risk early warning semantic package and risk propagation trend topology graph.The application can improve the accuracy and system of vehicle fault detection as a whole, reduce analysis bias.

The invention relates to the technical field of intelligent operation and maintenance of new energy power generation and power electronic equipment, in particular to a photovoltaic inverter operation abnormity fault diagnosis system, which comprises an edge data acquisition and dynamics reconstruction unit, a data processing unit and a fault diagnosis unit, the topological section feature extraction unit is used for generating a discrete two-dimensional intersection point coordinate set; the topological fingerprint generation unit is used for generating a topological fingerprint matrix; the cloud topology reasoning unit is used for generating a diagnosis state result; the abnormity tracing closed-loop control unit is used for monitoring the diagnosis state result in real time, generating a backtracking locking instruction when the diagnosis state result is abnormal or suspected fault, triggering the edge controller to stop overwriting operation on the annular buffer area and locking high-frequency original waveform data at the corresponding moment; according to the method, the microcontroller with limited resources can complete feature extraction within microsecond-level time, and the real-time performance of edge side processing is guaranteed.

This application discloses a brain decoding method based on multi-view self-supervised brain topology inference, comprising: constructing an initial topological structure model based on preset brain region information and a graph learner; using the brain topology in the initial topological structure model as the first view brain topology; obtaining the connection relationships of different brain regions based on the brain adjacency matrix in the initial topological structure model, aggregating the original brain data according to the connection relationships to obtain a second view brain topology; updating the first view brain topology through the second view brain topology; stopping the update iteration when the mutual information between the two view brain topologies no longer changes, and using the target features in the obtained second view brain topology as the global brain features after decoding the original brain data. This method can adaptively update and learn the view brain topology based on the dynamic plasticity of the original brain data, avoiding the inheritance and superposition of erroneous information in the topology, and obtaining accurate and comprehensive decoding information.

The present application provides a method, device, electronic device and storage medium for determining a traffic topology map, the method comprising: obtaining an image of the environment in which the vehicle is located and a navigation map corresponding to the environment, performing lane recognition on the image based on a target lane recognition model to obtain a lane recognition result, wherein the target lane recognition model is obtained by using the navigation map as prior information and fine-tuning the lane recognition model according to the image; performing traffic element recognition on the image through a traffic element recognition model to obtain a traffic element recognition result; performing topological analysis based on the traffic element recognition result and the lane recognition result to obtain a traffic topology map. The present application recognizes images by using a navigation map with lane information as a prior information target lane recognition model, thereby improving the accuracy of lane recognition results, enhancing the topological reasoning ability of the vehicle in complex traffic scenarios, and improving the accuracy and robustness of the traffic topology map.

The invention discloses an electric power data multi-label text classification method and system, and relates to the technical field of electric power data process.The electric power data multi-label text classification method comprises the steps that electric power text data are obtained, vocabularies, grammar structures and semantic information of texts of the electric power text data are converted into quantum state expressions through a quantum encoder, and text quantum states are obtained; based on the text quantum state and a predefined power domain knowledge quantum state, performing feature fusion through a multi-mode quantum fusion model to obtain a fused quantum representation; based on the fused quantum representation, analysis processing is carried out through a quantum topology reasoning network, and a quantum hypergraph state containing a tag association relationship is obtained; based on the quantum hypergraph state, multi-label classification is carried out through topological quantum gradients, and an initial classification result is obtained; and based on the initial classification result, eliminating a false correlation through dynamic quantum causal intervention to obtain a final classification result, so that the accuracy of multi-label text classification is improved.

The invention specifically relates to an unmanned cluster identification method based on motion topology reasoning, and belongs to the technical field of artificial intelligence and unmanned systems. The method comprises the following steps: firstly, collecting a historical track sequence of unmanned cluster nodes, and constructing an interpretable feature system containing three types of physical features of motion, interaction and topology; inputting the feature vector into a self-adaptive encoder containing dynamic graph construction, multi-head attention and time sequence aggregation components, and outputting a high-order cluster feature vector of time sequence aggregation; then, target identity recognition and node interaction topology reasoning are completed in sequence, and a cluster node future trajectory prediction result is output through a dual decoder; and finally, identifying and predicting performance through joint optimization of a multi-objective loss function, and updating model parameters in real time to adapt to a dynamic task scene. According to the method, the problems of poor scale adaptability, task decoupling and feature black box of an existing method are effectively solved, and end-to-end joint optimization of identity recognition and trajectory prediction is realized.

The present invention discloses a network topology inference method, system, device and medium. The inference method includes obtaining dynamic network data; sorting all links in the dynamic network data by time, and dividing all the time-sorted links by using a window to generate continuous snapshots; constructing a sample data set with continuous Q snapshots as a sample; constructing a network topology inference model; wherein, the network topology inference model includes a node feature extraction module, a spatial evolution information extraction module, a fusion module, a temporal information extraction module and an output layer; training and testing the network topology inference model by using the sample data set to obtain a target network topology inference model; and performing network topology inference by using the target network topology inference model. The present invention improves the accuracy and efficiency of network topology inference.

The present application relates to the technical field of true and false identification of South Red Agate, and particularly relates to a true and false intelligent identification method of South Red Agate based on a knowledge graph. The method comprises the following steps: collecting original data of South Red Agate samples, and preprocessing the original data to obtain preprocessed data; processing the preprocessed data through a multi-dimensional feature convolutional neural network to obtain comprehensive feature data of the South Red Agate samples; introducing a graph reasoning layer into the multi-dimensional feature convolutional neural network, and reasoning through a dynamic topology reasoning algorithm based on the comprehensive feature data of the South Red Agate samples to obtain an optimal path; and based on the optimal path, intelligently identifying the true and false of the South Red Agate samples. The method solves the technical problems of insufficient accuracy and low efficiency of traditional true and false identification methods of South Red Agate.

The embodiments of the present application relate to the field of autonomous driving technology, and in particular to a topological reasoning method, system, device and storage medium for driving scenes, the method comprising: extracting multi-scale image features; various types of instances obtain information from the multi-scale image features, and obtain feature vectors of instances corresponding to traffic elements and lane centerlines respectively; obtain embeddings of traffic element instance feature vectors, and interact and exchange messages between instance feature vectors and embeddings of traffic element instance feature vectors through scene topological relationships to obtain and aggregate important knowledge; based on the scene knowledge graph, knowledge extraction is performed on traffic element instances of different categories, and topological maps of traffic elements, lane centerlines and driving scenes are predicted respectively. The present application proposes a topological reasoning network, generates high-quality driving scene topology by predicting instances (lanes, traffic elements) and relationships between instances in the driving scene, and predicts the driving scene topology in an end-to-end manner.

The present invention relates to a method for inferring a wireless network topology based on spatio-temporal characteristics, including: setting up a sensor network in a target area; detecting the power of a non-cooperative network; dividing the target area into M preset grids; inputting the power of the preset grids into a neural network model to obtain the coordinates of non-cooperative nodes; obtaining the distance between two non-cooperative nodes according to the coordinates of the non-cooperative nodes; obtaining the start time and end time of the transmission information of each non-cooperative node according to the time information of the interaction between non-cooperative nodes; obtaining the mean value of the frame intervals and the variance of the frame intervals of each node pair; inputting the mean value of the frame intervals, the variance of the frame intervals, and the distance information of the node pairs into a trained classification model to obtain the connectivity relationship of each node pair. The method for inferring a wireless network topology based on spatio-temporal characteristics in the present invention combines node positioning and topology inference into a system to achieve topology inference when the positions of target nodes are unknown.

This invention discloses a method and system for assessing postoperative abdominal organ ischemia risk based on image analysis, belonging to the field of image analysis technology. The method includes acquiring enhanced abdominal CT images of the target patient and outputting them after standardization processing; employing an improved 3D U-Net++ architecture with multi-task collaborative learning to jointly segment the target patient's organs and target regions, integrating geometric priors and topological inference mechanisms; its key technical points are: using multi-task segmentation to focus RPPR calculation on the real ischemic area, avoiding average dilution of the signal across all organs, and topological correction to ensure that vascular features reflect the real anatomy; furthermore, through the joint analysis of low-perfusion area prediction masks and vascular VTIF, it reveals two ischemic subtypes: structural occlusion and functional hypoperfusion, promoting the individualization of clinical intervention strategies and defining the necessary vascular intervention or conservative treatment, making the overall solution both innovative and clinically applicable.

The application provides a space-time joint topology inference method for non-cooperative tactical networks, and relates to the technical fields of electronic countermeasures and communication network reconnaissance. The method of the application comprises the following steps: inputting a multi-dimensional observation sequence of intercepted signals containing time stamps, angles of arrival and target identifications; performing cluster analysis on the angle of arrival data to extract the real spatial orientation of nodes; then, introducing the angle of arrival as a spatial constraint variable on the basis of the time dimension of the traditional Hough process, and constructing a space-time conditional intensity function containing a time decay kernel function and a Gaussian spatial kernel function; finally, calculating the spatial confidence weight of the event based on the function and iteratively solving to reconstruct the topology of the tactical network. The application uses spatial consistency to perform secondary verification on potential causal excitation relationships, which serves as a gating mechanism to effectively suppress false samples with matched identity tags but conflicting spatial logic, and can still maintain high-precision network topology inference under the condition of low recognition accuracy.