530 results about "Cognitive network" patented technology

The invention relates to the technical field of tunnel engineering intelligent construction, and discloses a hard rock TBM shield tunneling machine auxiliary tunneling parameter dynamic adaptation regulation and control system, which comprises a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network and a high-precision multi-source sensing network, the TBM-geological environment digital twin predicts the tunneling short-term trend based on the high-fidelity physical simulation and data assimilation technology; and the multi-modal deep learning collaborative decision-making module deeply fuses real-time and prediction data and generates an optimal parameter solution set through a network trade-off tunneling multi-conflict target based on Pareto optimization. And the system executes a decision and forms closed-loop feedback through a parameter dynamic adaptation and adaptive learning module, and continuously optimizes a self model and a knowledge base. According to the method, passive response of TBM tunneling is converted into active pre-judgment, the decision accuracy, the construction safety and the comprehensive tunneling efficiency under the complex working condition are remarkably improved, and the method has the sustainable evolution capacity.

The invention discloses a method for reducing a large model illusion problem based on an RAG technology, and the method comprises the steps: extracting semantic entities, relation phrases and context features in a natural language query, and constructing a multi-source heterogeneous hypergraph; fusing the graph structure and sequence context information by using a graph attention network and a sequence perception network to form unified semantic representation; evaluating the illusion risk based on the confidence score, the evidence coverage rate and the semantic deviation index, and triggering reverse retrieval and fusion reinforcement; and the content is generated through causal consistency discrimination feedback control. According to the method, the illusion phenomenon of the generation result is remarkably reduced, and the method is widely applied to the field of intelligent question answering and information retrieval.

The invention discloses an urban rainwater pipe network blockage risk early warning method and system based on edge calculation, and relates to the technical field of urban drainage monitoring. The problems that existing pipe network blockage detection lags behind, and the early warning precision is insufficient are solved. Dynamic hydraulic parameters and sediment migration state data of a pipe section are collected in real time through edge calculation equipment deployed at a pipe network node, the hydraulic state deviation rate is calculated, and the local blockage risk is rapidly recognized; when the deviation rate exceeds a threshold value, a dynamic sensing network is established by the trigger nodes, and a pipe network hydraulic topological relation is established by integrating the liquid level and flow velocity characteristics of the upstream and downstream nodes; executing distributed collaborative analysis based on the topological relation, identifying an abnormal attenuation area, calculating a sediment dynamic equilibrium index, and generating a blockage diagnosis parameter; and further combining real-time rainfall intensity to predict an overflowing capacity attenuation curve, generating a multi-stage early warning instruction according to an attenuation slope, and distributing the multi-stage early warning instruction to an operation and maintenance terminal, thereby realizing accurate early warning and active regulation and control of the blockage risk of the rainwater pipe network.

The invention relates to the technical field of cluster video stream processing, and discloses a video stream real-time coding and decoding transmission method under a cluster. The method comprises the following steps: firstly, acquiring video stream coding parameter text data, link state time sequence data and equipment performance index data of multiple nodes of a target cluster to form a transmission link data set; semantic analysis is carried out on the coding parameter text data to obtain a coding semantic feature vector, dynamic fluctuation features are extracted from the link state time sequence data to obtain a link fluctuation feature vector, and cross-modal fusion is carried out to generate a fusion transmission feature set; generating an abnormal association degree score set by using a pre-trained multi-layer sensing network model, and obtaining an abnormal source node and an equipment defect type by combining root cause tracing according to the abnormal association degree score set; and finally, generating a dynamic optimization strategy and feeding back to the transmission control system to trigger parameter calibration. According to the method, the abnormal root cause can be accurately traced, the transmission parameters are optimized, and the cluster video stream transmission quality is improved.

The invention discloses a complex terrain three-dimensional modeling and earthwork volume calculation method based on a multi-source fusion point cloud, belongs to the technical field of surveying and mapping and engineering surveying, and mainly solves the problems of low terrain modeling precision and insufficient earthwork calculation efficiency in a complex scene. The method comprises the following steps: constructing a ground-air integrated multi-source sensing network to synchronously acquire laser point cloud, multi-view images and positioning data; adopting PointNet + +-based initial registration and multi-scale ICP fine registration fusion to generate a unified point cloud; combining semantic segmentation and penetration probability filtering to accurately extract a digital elevation model; constructing a similar triangular prism voxel model by using a constrained Delaunay triangulation network; and finally, the earth volume is rapidly calculated by adopting a GPU parallel voxel cutting and filling algorithm, so that high-precision terrain modeling and rapid engineering quantity calculation are realized, and the method can be efficiently and reliably applied to large-scale projects such as roads and mines.

The invention provides an unmanned aerial vehicle ad hoc network transmission and calculation integrated resource scheduling method based on task driving, and the method comprises the steps: building a multi-dimensional resource pool model which comprises the communication bandwidth, calculation resources and storage resources of an unmanned aerial vehicle, and collecting the resource state vector of each unmanned aerial vehicle node in real time; a dynamic topology sensing network is constructed, link duration is predicted through relative motion speed between unmanned aerial vehicle nodes, and a network structure chart with weights is generated; constructing a decision model based on a fusion architecture of a preset message passing neural network and a deep reinforcement learning network, and inputting the network topology features of the network structure chart and the resource state vector into the decision model; and outputting an optimal scheduling strategy including target node selection and multi-hop path planning through the decision model, and maximizing system benefits while meeting constraints of tasks on communication and computing resource quality. The problems that existing unmanned aerial vehicle networking communication is high in time delay, low in reliability and difficult to calculate and maximize utilization of resources are solved.

The invention discloses a flood disaster monitoring and early warning system and a flood disaster monitoring and early warning method. A cloud, rain, water and I integrated sensing network is constructed through a full-chain monitoring capability; a hybrid prediction model coupled with HEC-HMS and SWMM physical models and an LSTM-Transformer deep learning architecture is established, parameter deviation is dynamically corrected through NSGA-II and a symbolic regression multi-objective optimization algorithm, the flood prediction period is prolonged to 10 days (the precision of the southern watershed is larger than or equal to 90%, and the precision of the northern watershed is larger than or equal to 70%), the flood peak time error is compressed to be within 30 minutes, and compared with a scheme based on a static flood risk model, the method has the advantage that the flood prediction efficiency is greatly improved. The false alarm rate is reduced from 20% to 5% through the dynamic threshold calibration technology; hierarchical response and survivability communication are adopted, Beidou satellite and NB-IoT dual-channel redundant transmission is deployed, and a Mesh ad hoc network and frequency modulation subcarrier technology are combined, so that the direct rate of early warning information in extreme weather is ensured to be greater than or equal to 99%; and three-dimensional GIS platform dynamic rendering is supported, and collaborative visualization of a submerging thermodynamic diagram, a material scheduling path and ecological flow monitoring is realized.

The invention relates to an unmanned aerial vehicle detection countering method, which comprises the following steps of: deploying a multi-dimensional sensing network to carry out full-band instantaneous scanning, synchronously acquiring noise characteristics of radio signals, material characteristics of a terahertz wave band and Doppler effect characteristics, mapping to a virtual twinborn body, reconstructing physical parameters, a communication protocol and a behavior mode of a target unmanned aerial vehicle, performing depth comparison by combining a disguise sample library generated by the antagonism generation network, and generating a holographic target file containing a three-dimensional motion vector, an energy feature and a potential threat intention; a target quantum encryption communication link is interfered through a quantum entanglement signal, a controllable plasma cloud cluster is generated, navigation and image transmission signals of the controllable plasma cloud cluster are selectively attenuated, the controllability of the target is judged, and an uncontrollable target is repelled through coded sound waves; and sending a control instruction containing a biological heuristic obstacle avoidance algorithm in stages, and guiding the control instruction to an intelligent recovery cabin. According to the invention, accurate detection, intelligent identification, safe countering and reliable takeover of the unmanned aerial vehicle are realized, and the airspace safety guarantee capability is improved.

The invention discloses a dynamic scheduling optimization method for a low-altitude logistics distribution network, and the method comprises the steps: a server side builds a multi-source sensing network through satellite remote sensing, an unmanned plane airborne sensor and ground traffic monitoring, fuses meteorological data, airspace control data and order distribution data which are collected in real time, and generates a four-dimensional space-time grid map; based on the four-dimensional space-time grid map, the server side adopts a TD3-GA hybrid intelligent algorithm to carry out path planning of the logistics distribution network; the edge calculation end generates an optimal scheduling scheme of the unmanned aerial vehicle group through a multi-objective optimization function based on the global path; and the server side performs security risk assessment on the optimal scheduling scheme by using a Bayesian network model, and dynamically adjusts a space-time routing strategy of the unmanned aerial vehicle cluster according to an assessment result. According to the method, in a large-scale unmanned aerial vehicle concurrent scheduling scene, the scheduling efficiency can be effectively improved, the response time delay is reduced, the risk prediction accuracy is improved, and the timeliness and safety of a low-altitude distribution network are remarkably improved.

The invention relates to a TSN network configuration automatic generation method and system based on an anti-Agent. The system is composed of a network state perception and intention reflection module, a YANG model configuration parameter and structure decision module and a YANG model intelligent generation module. The network state sensing and intention reflecting module is responsible for structurally sensing the network state and resource information, the YANG model configuration parameter and structure decision module fuses multi-source knowledge and context information, intelligently generates deployable TSN scheduling parameters and a YANG configuration structure for service requirements, and sends the TSN scheduling parameters and the YANG configuration structure to the network state sensing and intention reflecting module. And the YANG model intelligent generation module automatically generates a corresponding YANG configuration model according to an optimized result, and issues the YANG configuration model to target equipment after completing grammar and semantic verification, so that automatic deployment of network configuration is realized. According to the method, the intellectualization level of TSN network configuration and the deployment automation capability are remarkably improved by introducing the reflection reasoning and feedback optimization technology.

The invention relates to the technical field of chemical enterprise production management, in particular to a chemical enterprise safety production informatization management system which comprises a sensing network layer architecture, a digital twin management platform, a risk management and control and decision center, a process closed-loop management center and an emergency cooperative processing platform. According to the chemical enterprise safety production informatization management system and method, through construction of a chemical knowledge graph, on the basis of a dynamic risk matrix and an AI prediction model library, equipment remaining service life prediction, process tiny anomaly capture and accident consequence simulation processing are realized; meanwhile, a root cause analysis module is used for associating low-level alarm and traversing a knowledge graph to automatically infer a fault root, physical equipment and a virtual model are bound through an entity-model mapping engine, the model state is dynamically updated in combination with a real-time rendering technology, and technological parameter optimization, accident deduction and scheme verification can be completed in an analogue simulation engine; and the management intuition and the decision-making scientificity are effectively improved.

The invention discloses an intelligent sand excavation supervision system based on multi-source data fusion, and relates to the technical field of machine learning, and the system collects target river reach data in real time through a multi-source sensing network module; the spatial-temporal feature fusion module generates a dynamic state fingerprint matrix; the adaptive baseline monitoring module establishes and dynamically updates a normal state baseline under multiple conditions, and triggers an abnormal disturbance alarm by calculating a mahalanobis distance between a real-time fingerprint and the baseline and combining collaborative deviation verification of acoustics, turbidity and water flow characteristics, and the multi-task analysis module adopts a parallel neural network architecture, so that a multi-task analysis result is obtained. The illegal operation type probability, the strength estimation value and the environment disturbance level are synchronously output; the three-dimensional visualization early warning module generates an early warning interface based on the analysis result; the dynamic knowledge management module and the self-adaptive optimization module are used for improving the analysis accuracy and continuously optimizing the system performance by using historical experience; the method has the advantages that abnormal disturbance events such as illegal sand excavation and the like can be accurately and intelligently supervised in real time, and powerful capability is provided.

The invention discloses an oil storage tank oil-water interface measurement system and method and a storage medium, and relates to the technical field of oil-water interface measurement, and the system comprises a data acquisition module, a data processing module, a decision support module and a data security module. According to the oil-water interface measuring system and method for the oil storage tank and the storage medium, a three-dimensional sensing network (distributed sensor network) is constructed by adopting a pressure sensor array and phased array ultrasonic scanning technology and combining dielectric property detection of a microwave dielectric constant sensor; the pressure, the dielectric constant, the liquid level and the vibration data in the tank body are obtained, basic data are provided for subsequent processing, the comprehensiveness of data obtaining can be improved through the sensing network, the pressure data are dynamically calibrated through Takagi-Sugeno fuzzy logic, the influence of temperature drift is effectively eliminated, and the accuracy of data obtaining is improved. The ultrasonic signals are subjected to feature enhancement through the generative adversarial network, and the emulsion layer boundary is accurately recognized.

The invention belongs to the technical field of smart power grids, and discloses a distributed photovoltaic cluster-based smart power grid optimal scheduling method and system, and the method comprises the steps: fusing meteorological and power grid data through a cross-modal sensing network, combining a meta-learning framework to quickly adapt to a new power station, and narrowing a fluctuation interval through a prediction-correction dual-channel mechanism; according to the method, implicit association between weather and a power grid is mined, prediction robustness of low-probability events is improved through extreme scene intensive training, output prediction precision is remarkably improved, and the problem that traditional prediction lags behind actual fluctuation is effectively relieved; a dynamic role multi-agent system is adopted, and a credit scoring mechanism of block chain evidence storage and an improved auction algorithm are combined, so that distributed efficient decision making is realized; the intelligent agent dynamically switches roles according to the load state, the credit condition is linked with the scheduling priority, the problem that the topology adaptability of centralized decision making is poor is solved, and a trusted collaborative environment is constructed through transaction records which cannot be tampered.

The invention discloses an intelligent monitoring method for a hydrogeological environment, and the method comprises the steps: 1, constructing a space-air-ground integrated sensing network, and dynamically adjusting the monitoring frequency through a self-adaptive sampling strategy; 2, developing cross-platform data middleware, realizing conversion from multi-protocol data to a WaterML 2.0 standard, constructing an anomaly detection model in combination with a deep residual network, and correcting regional parameter deviation; 3, designing a hybrid fusion architecture, processing time sequence data by using an improved Transform at a bottom layer to capture long-term dependence, constructing a graph neural network at a top layer to fuse spatial connectivity, and outputting a prediction result with a confidence interval through an attention mechanism and a Bayesian network; step 4, establishing an edge-cloud hierarchical computing system, deploying lightweight model real-time early warning at an edge end, integrating complex models such as 3D geological modeling at the cloud end to generate a regional report, and adopting federal learning cooperative training and embedding differential privacy protection; and 5, developing a three-dimensional geological visualization platform and an intelligent decision-making system.

The invention relates to the technical field of wireless communication, and particularly discloses a high-voltage transformer remote metering monitoring system based on 5G network slicing, which senses network performance in real time by constructing a multi-dimensional network state matrix, performs path rehearsal and resource allocation calculation by using a digital twinning technology, and realizes remote metering monitoring of a high-voltage transformer. Generating a candidate transmission path set with optimal time delay consistency and a corresponding wireless resource block allocation scheme; calculating a path resource allocation factor and combining with network load prediction to generate an optimal resource scheduling strategy; and finally, converting the strategy into a specific network control instruction, establishing a special transmission channel with deterministic service quality, and implementing real-time monitoring and maintenance.

The invention relates to the technical field of smart home security and protection, and discloses a smart home security and protection system based on a body, and the method comprises the steps: collecting real-time sensing data through Wi-Fi multipath scattered waves, constructing a body sensing network, recognizing behavior feature information, optimizing a preset security and protection model decision architecture, and obtaining an optimized security and protection process. The method comprises the following steps: firstly, generating a security response path, determining a personnel activity space-time distribution state, calculating a security efficiency value, then determining an optimization direction according to the security efficiency value, generating a dynamic protection organization unit, calculating a misjudgment attenuation rate, and finally determining a security state and analyzing a security dimension according to the misjudgment attenuation rate, thereby constructing a more accurate and intimate intelligent security scheme. According to the invention, the intelligent level of autonomous decision making and active protection of home security can be improved.

The invention provides an intelligent substation inspection method and system based on the Internet of Things, and the method comprises the steps: collecting a multi-mode perception data set, and constructing a real-time three-dimensional map of a substation; constructing a cross-modal generation type fault detection model, and extracting fusion features to obtain a diagnosis result; formulating an optimal inspection path and plan by using a double-layer reinforcement learning framework and an evolutionary algorithm; and executing inspection based on the map to obtain a result. According to the method, multi-source equipment data are fused through the bionic sensing network, and the complex fault recognition capability is improved in combination with the generative fault detection model; the real-time three-dimensional map is based on laser radar SLAM and UWB fusion positioning, and is dynamically updated by using an ICP algorithm, so that the static map problem is solved, and the collision risk is reduced; double-layer reinforcement learning and an evolutionary algorithm are combined to realize multi-objective collaborative optimization, so that high-efficiency routing inspection is guaranteed, energy consumption is reduced, and time is shortened; and a closed loop verification mechanism dynamically adjusts the strategy, so that the adaptive capacity of the system is enhanced, and the inspection accuracy, efficiency and flexibility are improved.

The invention discloses a dynamic energy-saving control method and system for a cold storage based on deep learning and multi-source data fusion, and relates to the technical field of cold storage energy-saving control, and the method comprises the steps: constructing a thermal field sensing network through a fixed sparse sensor array and a mobile thermal field scanning robot, and collecting temperature, humidity and thermal imaging data; reconstructing a three-dimensional thermal pixel field through registration, interpolation and other processing, and performing space-time alignment on the three-dimensional thermal pixel field, equipment parameters and inventory thermal physical properties; constructing digital twinborn deduction thermal field evolution based on a physical information neural network, and recognizing a high-temperature hot spot and a low-temperature safe area in combination with a space-time diagram attention network; the method comprises the following steps: by taking power consumption cost minimization and over-temperature risk controllability as targets, solving an optimal control strategy containing tuyere parameters and compressor frequency by using a multi-agent depth deterministic strategy gradient algorithm, driving equipment to execute targeted cold supply, and enabling a system to comprise a thermal field sensing layer, a data fusion layer, a digital twinning and prediction layer, a decision optimization layer and an execution layer. And accurate temperature control and energy conservation are realized.

The invention provides an abnormal sound detection method based on multi-scale time-frequency feature perception, and relates to the technical field of acoustic detection for industrial machine state monitoring, and the method comprises the steps: inputting an original sound signal, and carrying out the dual-branch feature extraction to generate a time domain coding spectrogram and a logarithmic Mel spectrogram; executing hybrid data enhancement; splicing the enhanced spectrogram and inputting the enhanced spectrogram into a dense encoder for compression modeling; extracting features through a two-stage multi-scale time-frequency sensing network, processing along a time dimension in the first stage, and processing along a frequency dimension in the second stage; inputting the high-order features into a lightweight classifier to output an abnormal score; and comparing the abnormal score with the gamma distribution threshold to judge abnormity. According to the method, the weak anomaly detection rate and the cross-equipment stability are remarkably improved, and the perception and discrimination capability of the model on the multi-scale time-frequency characteristics is effectively enhanced.

The invention relates to the technical field of water pollutant concentration detection, and discloses a water pollutant concentration detection system and method based on big data analysis, and the water pollutant concentration detection method based on big data analysis comprises the following steps: constructing a multi-level sensing network, the multi-layer sensing network comprises a fixed monitoring station layer, a water surface unmanned ship layer and an underwater robot layer; carrying out multi-modal data fusion by utilizing a knowledge constraint data assimilation algorithm to generate consistent water quality parameter estimation; a double-layer cognitive structure combining a knowledge graph and a causal model is constructed, and water pollutant propagation understanding and tracing are achieved; continuous optimization of system configuration is realized through a multi-objective reinforcement learning framework; through collaborative optimization of a multi-layer sensing network, the system realizes three-dimensional monitoring of a water body, the monitoring coverage range is expanded, and meanwhile, the high-precision detection capability is kept.

The invention relates to the field of intelligent transportation and urban landscaping management, and discloses an LIM transportation junction landscaping landscape intelligent operation and maintenance method and system and electronic equipment, and the method comprises the following steps: constructing a landscaping landscape information model integrating a three-dimensional space model, physiological attributes and infrastructure topology; deploying a multi-source sensing network based on the model, and collecting environment, vegetation and operation and maintenance data; performing time-space association on the data and the model entity, and establishing a dynamic data management platform; constructing a vegetation digital twinborn prediction engine in combination with the physical information neural network and outputting a result; based on the prediction result, adopting multi-target reinforcement learning to generate a maintenance strategy; and executing an intelligent maintenance task bound with the model space coordinates according to a strategy. According to the method, unified integration and dynamic management of greening landscape data are realized, vegetation state sensing precision and prediction capability are improved, intelligent decision-making of multi-objective optimization is supported, and scientificity, efficiency and resource utilization rate of greening maintenance are effectively improved.

The embodiment of the invention provides a quantum key distribution method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of quantum key distribution, and the method comprises the steps that a quantum key distribution network control center responds to a quantum key distribution request, obtains the network state information of a quantum key distribution network, and sends the network state information to a server; the method comprises the steps of receiving network state information, determining a path set for distributing quantum keys based on the network state information, then determining a path weight of each reachable path based on the network state information, determining a distribution mode of the quantum keys based on the path weights, and determining a target path from the path set; and sending the distribution mode and the target path to a source node. According to the embodiment of the invention, the network state can be sensed in real time, the routing strategy can be adjusted, the problem of quantum link quality fluctuation or node resource shortage can be effectively solved, quantitative analysis of path security efficiency and resource efficiency is realized, and the robustness and flexibility of the system are improved.

The invention discloses a language-guided structure-aware network architecture and method for camouflage target detection, and relates to the technical field of camouflage target detection. According to the method, the text is guided to focus on a potential target, text semantic and visual features are fused through the CLIP model, the target mask is generated, the problems that a traditional model lacks semantic guidance and is difficult to focus on a camouflage area are solved, and background interference is greatly reduced; edge details can be accurately extracted, a Fourier edge enhancement module (FEEM) is combined with spatial domain edge enhancement and frequency domain high-frequency information capture, the problem of a fuzzy pain point of a camouflage target boundary is effectively solved, and the edge positioning precision is improved; structural and local dual optimization is proposed, a structural awareness attention module (SAAM) is fused with semantic and edge information, and a coarse guidance local refinement module (CGLRM) is designed through double branches of global guidance and local refinement, so that the regional consistency and structural integrity of a segmentation result are guaranteed, and local details are prevented from being lost.

The invention discloses a real-time adaptive temperature monitoring intelligent sensor based on a deep learning algorithm, and belongs to the technical field of environmental parameter measurement and artificial intelligence. The sensor is composed of a main temperature measuring unit, an auxiliary environment sensing unit, a data acquisition circuit, a microcontroller, a deep learning processing module, a memory, a communication module and a power supply module. Multi-modal data are obtained through the auxiliary environment sensing unit, and the multi-modal data are preprocessed and then input into the self-adaptive temperature sensing network. In the adaptive temperature sensing network, a multi-mode encoder extracts time sequence characteristics of each channel, a convolution auto-encoder reconstructs a normal temperature measurement mode and performs anomaly detection, and a fusion decoder fuses output of each sensor into a corrected temperature value based on a dynamic weight formula. The method has the advantages of high real-time performance, high measurement precision, timely anomaly detection, high self-learning capability, low power consumption and the like.

The invention discloses a tunnel lining construction full-process intelligent monitoring and early warning platform, which relates to the technical field of tunnel construction intelligent monitoring and comprises a field sensing unit, an edge computing unit and a central management center. According to the invention, the field sensing unit synchronously acquires multi-source data template stress, template deformation, template displacement, surrounding rock and environment parameters and personnel and equipment positioning, and displacement sensors are alternately arranged to realize temperature drift correction, so that the data precision is improved; through fusion of a dual-spectrum camera and an ultra-wideband label, sub-meter personnel positioning precision and safety protection article identification are realized in a low-illumination environment, a full-dimension sensing network covering physical states, environments and behaviors is formed, and an edge computing unit adopts an embedded chip to provide computing power. And multi-modal feature fusion and physical consistency constraint deep learning reasoning are executed, an online change point detection mechanism is introduced to dynamically adjust an early warning threshold value, and different geological conditions and construction stages are adapted.

The invention discloses a shield tunnel-oriented duct piece dislocation detection method, which comprises the following steps of: acquiring an original multi-source data set, and constructing a ring-level data packet set indexed according to ring numbers according to pre-stored propulsion encoder information; extracting local features of the circular seam, constructing self-calibration optimization data by utilizing the spatial deviation of an actual circular seam and a designed circular seam, jointly solving external parameters of a sensor and a ring-level attitude correction amount, and generating a circular seam alignment result set containing an accurate circular seam spatial position; mapping the circular seam alignment result set to a polar coordinate domain, and extracting an attention enhancement feature set through a pre-configured parallel patch sensing network; and in combination with geometric information in the circular seam alignment result set and visual features in the attention enhancement feature set, calculating a slab staggering geometric vector decomposed into multidirectional components, and generating a slab staggering classification result set. According to the method, the problem that sensor drifting and tiny slab staggering characteristics are difficult to recognize in long-distance tunneling is effectively solved, and full-process automatic accurate control over the segment assembling quality is achieved.

The invention relates to the technical field of electrical digital data processing, in particular to a hardware test resource allocation method based on dynamic topology adaptation, which comprises the following steps of: 1, constructing a dynamic topology sensing network: acquiring electrical connection signals between devices on a test backboard in real time, generating and periodically updating a topology connection matrix, and establishing a dynamic topology sensing network; the matrix element value represents the on-off state of a physical link between devices, and when a device plugging event is detected, matrix updating is triggered immediately; 2, quantifying resource and task attributes; 3, dynamic resource matching of topology driving is executed; and 4, responding to non-interruption migration of topology change: when the topology connection matrix is updated, executing the step 3 again on the influenced task, and realizing non-interruption switching through new and old resource parallel initialization and signal deviation fault-tolerant verification. Through the dynamic topology adaptation technology, test resources can be dynamically adjusted and matched according to real-time topology and task requirements, and efficient utilization of the resources is ensured.