735 results about "Space mapping" patented technology

The invention relates to the field of security surveying and mapping, in particular to a network security space surveying and mapping method, system and device based on multi-source data fusion, and the method comprises the steps: obtaining network security data in real time, and constructing a dynamic network topological graph; calculating a time-varying vulnerability score based on the topological graph and a historical attack log, and predicting an attack path and a propagation probability through a Bayesian network; performing cross-domain fusion on equipment, service and user behavior characteristics by adopting a federated learning framework to generate a dynamic asset portrait; generating a risk thermodynamic diagram in combination with spatial autocorrelation analysis and a multi-index fusion algorithm; a defense strategy effect is simulated based on an attack graph reconstruction engine, a Pareto optimal strategy combination is generated through an NSGA-II algorithm, and closed-loop verification and dynamic parameter correction are realized by utilizing honeypot deployment and flow traction. Therefore, the problems of topology update lag, single risk assessment dimension, cross-domain threat association fracture, defense strategy static stiffness, non-closed loop of a verification system and the like in the traditional technology are solved.

The invention relates to the technical field of knowledge graph construction, and discloses a knowledge graph construction method and system based on a large language model technology. The method comprises the following steps: receiving a multi-source heterogeneous data stream, and completing semantic space mapping and cross-modal feature fusion to generate a unified semantic representation vector set; constructing an initial knowledge graph skeleton; performing incremental optimization on the skeleton, and performing entity relationship disambiguation and conflict detection; and iteratively updating the knowledge representation, and outputting a target knowledge graph meeting semantic consistency. The system comprises a data receiving module, a semantic fusion module, a skeleton construction module, an optimization module and a knowledge updating module. According to the method, multi-source heterogeneous data is effectively processed, the accuracy, the dynamic updating capability and the semantic consistency of the knowledge graph are improved, and the method has wide application prospects in the fields of intelligent question answering, information retrieval and the like.

The invention discloses a point cloud data rapid processing system based on 3D vision, and relates to the technical field of point cloud data processing. The binocular acquisition module generates an anti-interference high-precision point cloud; the preprocessing module is used for reducing noise and dimensionality and retaining key geometric features; the hierarchical feature extraction module fuses curvature weight and a non-maximum suppression strategy, and improves the robustness of an ORB algorithm in rotation, scale and noise scenes; the industrial scene segmentation module accurately separates stacked objects through normal vector clustering and a dynamic region growing algorithm, and secondarily verifies boundaries in combination with a lightweight semantic model; the two-stage registration module dynamically adjusts a threshold value based on error feedback to realize pose optimization from coarse registration to fine registration; the coordinate mapping module establishes a space mapping model through multi-attitude calibration and robot multi-axis linkage trajectory optimization. The problem that in the prior art, a 3D machine vision algorithm is high in time delay is solved, the real-time performance and rapidity of 3D model recognition and feature extraction are improved, and the efficiency requirement of industrial production is met.

The invention provides an enhanced generation method based on question matching retrieval, and belongs to the field of matching generation, and the method comprises the following steps: S1, a semantic feature coding stage: carrying out real-time feature extraction and vector space mapping on a natural language query input by a user by adopting a deep neural network model, generating high-dimensional distributed representation with semantic representation capability; s2, a knowledge base intelligent retrieval stage: executing multi-dimensional semantic matching in the vectorized knowledge base based on an approximate nearest neighbor search algorithm, and screening out a candidate knowledge set highly related to query semantics through a similarity measurement function; s3, retrieval matching results are automatically associated to the structured knowledge base through the established semantic-knowledge mapping relation, the preprocessed standardized response content is directly obtained, and the response content adopts a multi-modal data organization form and comprises a structured data entity and retains a rich text expression form.

The invention relates to the technical field of data processing, and particularly provides an operation intention recognition method, system and device based on multi-mode fusion and a medium, and the method comprises the steps: synchronously collecting interaction data of at least two modes of a user, the modes comprising at least two of gestures, voice and eye gaze; carrying out alignment processing on the interaction data, wherein the alignment processing comprises time synchronization and space mapping to a unified coordinate system; recognizing structured semantic information from each piece of aligned modal data, wherein the structured semantic information comprises a gesture type, a voice text and a fixation point coordinate; and based on a preset semantic rule and context memory, performing semantic association and anaphora resolution on the structured semantic information to obtain an operation intention. The method effectively overcomes the inherent defects of unnatural single-mode interaction, easy ambiguity and poor fault tolerance.

The invention discloses a power transmission image defect detection and defect duplicate removal method and system based on a deep learning image segmentation algorithm, and belongs to the technical field of intelligent inspection of power equipment. According to the method, real-time tower identification and adaptive shooting are realized through a lightweight YOLO model deployed at the edge end of an unmanned aerial vehicle; pixel-level segmentation is carried out on the infrared image by using an MSAN-Net network, the network integrates a ResNet encoder, a cross-scale attention mechanism and a multi-level feature pyramid, and boundary learning is enhanced by using a composite loss function; based on a multi-view three-dimensional reconstruction technology, two-dimensional defects are mapped into space rays through feature point matching and pose estimation, and defect de-weighting is achieved through ray intersection judgment. Through the MSAN-Net network, the segmentation precision of the infrared component under a complex background is remarkably improved through an attention mechanism and multi-scale feature fusion, and the problem of repeated defect detection in multi-view inspection is effectively solved in combination with a three-dimensional space mapping method.

The invention relates to a dynamic load distribution method, device and equipment based on a vehicle body controller and a medium, and the method comprises the steps: collecting the mechanical load, electrical load and thermal load data of a vehicle body in real time through a distributed sensor network, and generating multi-source heterogeneous initial load data; performing standardization processing on the initial load data, and constructing a three-dimensional data cube type standard load data set; comprehensive characterization features are extracted through feature space mapping, and a fusion load feature set containing a threshold range is dynamically generated in combination with historical data; constructing a load correlation model based on the fusion features, and mapping to generate a steering and braking control parameter combination; and according to the real-time load threshold cross-border state, dynamically adjusting the task priority, allocating real-time calculation core resources to the chassis system, and generating an optimal allocation scheme. According to the method, the problems of multi-source load data fusion failure, resource allocation rigidity and safety task response delay in the prior art are solved, and dynamic load optimization of the vehicle body control system under complex working conditions is realized.

The invention discloses a natural resource engineering monitoring method and system based on cloud computing, and relates to the technical field of engineering monitoring. A cloud access layer is constructed, a standardized monitoring data set is formed, a monitoring quality abnormity label set is established, and an engineering state vector set is generated based on a space-time alignment monitoring data set; according to the method, standardization and unified management of multi-source monitoring data are achieved through cloud access, the data accuracy is guaranteed in combination with time synchronization, space mapping and a quality label mechanism, the method constructs an engineering state profile and generates consistent snapshots, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. Historical comparison and tracing are supported, a risk event set and a risk evidence chain are generated through causal atlas inference, risk full-chain tracing is achieved, a scheduling instruction is further generated and combined with an execution receipt to form closed-loop management, differential evaluation and dynamic adjustment are supported, and therefore linkage and continuous optimization of monitoring, risk identification and engineering scheduling are achieved.

The invention discloses a network space security situation awareness detection analysis system and method, and the system comprises a heterogeneous network flow feature deep extraction unit which obtains a flow feature vector through a distributed probe array and an adaptive sampling strategy; the edge computing gateway multi-dimensional threat feature mapping unit realizes feature space mapping through a multi-dimensional feature mapping matrix; the dynamic weight multi-head attention feature fusion unit fuses features by using an optimized multi-head attention mechanism; the time-space sequence security situation evolution modeling unit constructs an evolution model in combination with the time sequence and the fusion features; the security threat risk quantitative evaluation unit carries out risk quantification; and the heterogeneous security policy collaborative response unit generates a response instruction. According to the method, flow collection, feature mapping, fusion, modeling, quantification and response operation are sequentially executed based on all units of the system. According to the method, efficient perception and accurate response of the network space security situation are realized through a multi-unit cooperation and innovation model.

The invention discloses a mine disaster prediction method based on multi-source data, and relates to the technical field of mine safety, and the method comprises the following steps: collecting original data from different types of sensors in a mine, the data types comprising gas concentration, temperature, humidity, wind speed, ground pressure, water level and vibration information; each type of data is provided with a corresponding timestamp and a spatial position identifier. According to the method, time resampling and space mapping standardization of multi-source data are realized, so that the time-space consistency of data fusion is remarkably improved, and the accuracy of disaster prediction model input is ensured. Meanwhile, a dynamic feature matrix is constructed and a high-precision position weight mechanism is introduced, so that the sensitivity of the model to key areas and key parameters is enhanced, the real-time performance and accuracy of mine disaster prediction are effectively improved, and the risk of missing report and false report of an early warning system is remarkably reduced.

The invention discloses an intelligent operation abnormity monitoring and positioning method for a polypropylene cable, and relates to the technical field of intelligent operation and maintenance of a power system, and the method comprises the steps: collecting multi-source physical signals in the operation process of the cable, and constructing a multi-dimensional feature matrix fusing multiple physical quantities through multi-scale time window division and space mapping processing; extracting space-time coupling characteristics among nodes by using a graph attention embedding network, and training a running state recognition model in combination with a label perception contrast learning mechanism; constructing a cable topological graph based on an identification result, introducing an improved Bayesian space reasoning network, and calculating abnormal probability distribution of each node; a weighted abnormal heat map is further generated, an abnormal propagation path is extracted through an abnormal state flow model and a directional propagation scoring algorithm, and abnormal node positioning and trend evolution prediction are achieved; the method has the advantages of high spatial resolution, high identification precision and good online adaptability, and is suitable for intelligent state perception and abnormity early warning of the polypropylene cable in a complex operation environment.

The invention discloses a multi-sensor fusion heat pump full life cycle AI maintenance early warning system, and relates to the technical field of new energy utilization, and the early warning system comprises a data collection module which obtains operation parameters in a heat pump full life cycle based on a sensor array, and constructs a data set after preprocessing the parameters; the operation parameters comprise temperature, pressure, flow and micro vibration; the data fusion module is used for extracting trend correlation characteristics and parameter coupling characteristics from temperature, pressure and flow parameters by adopting a dynamic sliding window adaptive to a working condition, and preserving core nonlinear information through KPCA dimension reduction; the micro-vibration signal extraction comprises frequency domain and time domain features. According to the method, features are extracted through a working condition adaptive dynamic sliding window, then through cross-space mapping and a life cycle-working condition double-attention mechanism, the analysis and early warning module depends on a core feature mapping library and a two-dimensional dynamic baseline, through instantaneous and accumulated deviation double judgment, abnormal accurate recognition and stage division are achieved, and early warning perspectiveness is high.

The invention discloses a weld quality intelligent diagnosis system based on deep learning, and relates to the technical field of welding quality detection, and the weld quality intelligent diagnosis system comprises an image quality evaluation module, a feature alignment module, a deviation detection module, a path reconstruction module, a prior enhancement module and a defect identification module, identifying an area of which the signal-to-noise ratio is lower than a preset threshold value, and constructing a noise interference distribution diagram; and the feature alignment module executes a deformable convolution feature alignment operation with a confidence factor adjustment mechanism based on the noise interference distribution diagram to generate an initial space mapping result. Through mechanisms such as image quality perception, robust alignment, deviation detection, self-adaptive reconstruction and prior enhancement, a closed-loop weld joint intelligent diagnosis process is constructed, false alignment errors are effectively inhibited, the multi-modal fusion stability and the defect recognition precision are improved, and the reliability and the intelligent level of the system under complex working conditions are enhanced.

The invention provides a dynamic game difficulty self-adaptive adjustment method and system based on user behavior feedback, and relates to the technical field of game design, the method comprises the following steps: carrying out dynamic weighting calculation based on a difficulty adaptation index, generating a dynamic difficulty correction coefficient in combination with user real-time physiological feedback data, and adjusting the dynamic game difficulty according to the dynamic difficulty correction coefficient; the dynamic difficulty correction coefficient comprises a scene complexity adjustment parameter and an interaction response threshold adjustment parameter; inputting the user feedback parameter set into an image feature weight distributor, dynamically adjusting weight distribution of image retrieval feature vectors according to user attention distribution data and operation delay parameters, and generating an optimized scene element retrieval strategy; and performing feature space mapping on the dynamic difficulty correction coefficient and the optimized scene element retrieval strategy, and generating a final game difficulty control instruction set through nonlinear superposition. Game design can be optimized, and game adaptability and flexibility are enhanced.

The invention discloses a power distribution equipment on-line monitoring system based on multi-modal data fusion, and relates to the field of power distribution equipment management, and the system comprises a sensing module which is used for collecting an electrical signal, a thermal infrared signal, a mechanical vibration signal and an acoustic signal generated in the operation process of power distribution equipment through a multi-type sensing channel, converting the collected various signals into processable equipment state original data to form an equipment operation state original data set; according to the invention, through accurate acquisition of multi-dimensional signals, combination of space mapping and time delay compensation, data quality is optimized, the reliability of original information is ensured, key features are extracted according to working conditions, subtle state changes are captured by means of temperature field analysis and a variable-resolution spectrum technology, the comprehensiveness, accuracy and response timeliness of equipment operation monitoring are effectively improved, and the real-time performance of equipment operation monitoring is improved. Misjudgment and missed judgment are reduced, and fault risks are avoided in advance.

The invention relates to a PCB production line process parameter intelligent matching method based on feature space mapping, and the method comprises the steps: collecting and fusing the material attributes, structure parameters, sizes and historical process records of a plurality of batches of PCB products, carrying out the normalization preprocessing, removing abnormal data, and constructing a high-quality feature matrix; after multi-dimensional feature expression is realized by utilizing a multi-scale embedded network, a mapping relation between features and process parameters is dynamically learned on the basis of an adaptive space mapping network in combination with a soft constraint multi-objective loss function, and gradient cutting, step length adjustment and a disturbance elasticity pool mechanism are introduced in a parameter recommendation process to guarantee convergence and stability. After the parameters are implemented, feedback data are collected in real time, periodic iteration distillation optimization and loss function recalibration are carried out, and finally a fine-tuning parameter recommendation scheme marked with conflict indexes, confidence intervals and weight suggestions is output for an engineer. According to the scheme, the intelligence, the traceability and the field adaptation capability of parameter recommendation are improved.

The invention discloses a visual text coding method and system based on shared semantics and a composite external space. The method comprises the steps that after image sizes are unified, candidate areas are detected, and text characters are subjected to index embedding; local visual features are extracted and flattened by using multilayer convolution and nonlinear activation; respectively carrying out gating calculation from left to right and from right to left by adopting a two-way circulation network; k external spaces are defined, each space contains visual and text mapping matrixes, and local visual features and text features are mapped to the corresponding external spaces; calculating the matching degree and the regular constraint of the vision and the text in each space; performing weighted fusion on each external space mapping, and outputting an aggregation feature under dimension-level attention; pooling or summarizing the aggregated features to generate a final cross-modal global situation; according to the method, accurate and dynamic cross-modal semantic alignment is established between the visual image and the text description, and efficient and accurate retrieval and matching are realized.

The invention discloses a network space map surveying and mapping method and system based on multi-source data fusion, and the method comprises the steps: obtaining a multi-source data set in a unified format based on network flow data, equipment information data and geographic position data; obtaining a network asset entity and an incidence relation graph thereof through entity identification and correlation analysis based on the multi-source data set with the uniform format; obtaining a network space three-dimensional map model through three-dimensional space mapping and visual rendering based on the network asset entity and the association relationship map thereof; based on the network space three-dimensional map model, performing dynamic updating according to the accessed real-time data flow to obtain real-time network space situation data; and obtaining a network security risk assessment result through anomaly detection and threat identification based on the real-time network space situation data. According to the invention, visual display and security situation awareness of the network space are realized, and a brand new decision support tool is provided for network security management.

The invention belongs to the technical field of urban low-carbon planning, and relates to a low-carbon community evaluation index library dynamic construction method and system. The method comprises the steps of knowledge graph mode layer construction, knowledge extraction, semantic relation enhancement and fusion and graph database storage. Defining the types of the entities and the semantic relationship between the entities to obtain a mode layer with a hierarchical topological structure; knowledge extraction: mapping the entity relationship in the mode layer into a knowledge extraction template; after an extraction result is subjected to subgraph structured organization, vector space mapping is carried out, semantic association strength among indexes is calculated, and entity alignment is carried out by adopting a knowledge fusion mechanism driven by a large language model; and importing the knowledge graph subjected to semantic relationship enhancement into a graph database through a query language. According to the method, efficient construction and expansion of the cross-domain index library can be realized; the adaptability of the index system is enhanced; and in combination with a knowledge fusion feedback mechanism, the mode layer is dynamically adjusted, so that the data processing efficiency and accuracy are improved.

The invention belongs to the technical field of three-dimensional scene reconstruction, and particularly provides a shielding object three-dimensional reconstruction method and system based on millimeter wave radar and vision fusion. The method comprises the following steps: acquiring radar point cloud data and visible light image data of a target object; performing space-time alignment on the radar point cloud data and the visible light image data, and establishing a space mapping relation of multi-modal data; generating a three-dimensional geometric structure of the shielded part through a geometric reconstruction model based on three-dimensional space information penetrating through the shielding object in the radar point cloud data; based on the context features of the visible light image data, generating surface textures of the occluded area through a texture completion model; and fusing the three-dimensional geometric structure and the surface texture, and outputting a three-dimensional reconstruction model containing complete information of the occlusion region. Geometric consistency and texture authenticity of virtual-real fusion are guaranteed through space-time alignment and multi-modal data mapping, and the reliability of three-dimensional modeling in a complex shielding scene is remarkably improved.

The invention relates to the technical field of lubricant analysis, in particular to a gearbox lubricating oil product state intelligent recognition system and method, and the system comprises a lubricating oil state parameter collection module, a multi-dimensional feature fitting degree classification module, a spectrum pollutant dynamic detection module, a state change trend storage module and a lubricating oil abnormal deviation early warning module. According to the method, the lubricating oil state features are accurately extracted by constructing a multi-dimensional feature matrix and high-dimensional space mapping, the fitting degree weight and the data scale are combined to adjust, optimize and classify, the state recognition precision is improved, the pollution change is recognized through trend analysis in dynamic spectrum pollutant detection, and the monitoring sensitivity is improved. A data storage strategy driven by parameter change ensures the integrity of high-value data and reduces invalid storage, an offset calculation method is combined with data trend comparison to improve the recognition accuracy of an abnormal state, and misinformation and missing report are reduced.

The invention discloses a fusion semantic vector space mapping-based translation ambiguity term accurate matching method, which comprises the following steps of: S1, obtaining source language ambiguity terms, context texts and a target language candidate translation list, and extracting domain tags and term matching features to form a multi-modal data set; s2, using improved XLM-R model coding to generate term-level, sentence-level and translation-level semantic vectors; s3, training a dynamic mapping matrix based on a bilingual parallel corpus, and aligning source side vectors to a shared semantic space; s4, fusing the source-side basic vector and the multi-dimensional features through a double-channel attention fusion network, and generating source-side and translation-side comprehensive semantic vectors; s5, introducing term-context attention weight to correct cosine similarity; and S6, outputting an optimal translation through normalized sorting and part-of-speech secondary judgment. According to the method, multi-field ambiguous term accurate matching is realized, the term translation precision and efficiency in professional fields are improved, and the requirements of high reliability of term translation in the fields of medicine, machinery, computers and the like are met.

The invention relates to a multi-dimensional high-resolution turbine blade heat-flow coupling field synchronous measurement method and system, and the method comprises the steps: obtaining a target spot image of known space distribution in a measurement space, and building a three-dimensional space mapping function; carrying out particle matching on the velocity field measurement original image based on a three-dimensional space mapping function, and reconstructing a three-dimensional particle trajectory by adopting a self-adaptive grid algorithm combined with machine learning to obtain a high-resolution three-dimensional velocity vector field; acquiring a temperature field measurement original image, performing three-dimensional space registration according to a three-dimensional space mapping function, further calculating voxel-level temperature distribution, and inverting a three-dimensional temperature field; and carrying out coupling analysis on the three-dimensional velocity vector field and the three-dimensional temperature field, and outputting a three-dimensional heat-flow coupling cloud picture. According to the invention, multi-dimensional and high-precision synchronous measurement of the temperature field and the velocity field in the complex flow field of the turbine blade is realized, the spatial resolution and dynamic response capabilities are remarkably improved, and a reliable experimental basis is provided for analysis and optimization of the cooling performance of the blade.

The invention provides a psychological counseling model training method and device, electronic equipment and a storage medium, and the method comprises the following steps: 1, multi-modal data collection and preprocessing: collecting voice, micro-expression and text interaction data of a visitor, and generating a multi-modal aligned original data set through a noise suppression and timestamp synchronization technology; step 2, cross-modal feature space mapping: based on cross-modal comparative learning, mapping voice, micro-expression and text features of unstructured data to a unified semantic embedding space; according to the method, voice, micro-expression and text features are mapped to a unified semantic embedding space through cross-modal contrast learning, an alignment error is constrained by using a cosine similarity threshold value, the problem of insufficient semantic consistency of multi-modal data is solved, and meanwhile, according to the real-time multi-modal features of visitors, the semantic consistency of the multi-modal data is improved. And a dynamic weight adjustment mechanism is adopted to select an adaptive psychological genre guide strategy, a staged consultation target is generated, and personalized consultation guide is realized.

The invention relates to the technical field of software and hardware compatibility testing, in particular to a domestic software and hardware compatibility intelligent evaluation system and method, and the system comprises a compatibility analysis engine, a compatibility detection task execution engine, a software behavior analysis module, a fault positioning module, a knowledge graph module, a performance optimization suggestion module and a visual interface. The software behavior analysis module constructs a high-dimensional feature space of a software running state through deep learning, and extracts abnormal features based on a topological space mapping technology; the fault positioning module constructs a dynamic causal network based on a probability graph model, and identifies a root node through a chaos sensitivity analysis algorithm; the knowledge graph module constructs a software and hardware dependency relationship model and provides priori knowledge for fault location; and the performance optimization suggestion module generates an optimization scheme based on the root positioning result, so that the functions of automatically executing a compatibility test, accurately positioning the root of the compatibility problem and providing targeted optimization suggestions are realized, and the adaptation efficiency of domestic software and hardware is greatly improved.

The invention relates to a BIM-based water conservancy project safety early warning method and device, equipment and a medium. The method comprises the following steps: acquiring multi-source data such as an osmotic pressure gradient field based on a predefined hydraulic engineering monitoring point space coordinate list, and generating a three-dimensional osmotic field matrix after noise reduction, standardization and interpolation processing; physical characteristics of the matrix are extracted by using a pre-trained space-time convolution model, and abnormal parameter labels are determined through threshold comparison and deviation calculation; associating the exception to a BIM component node through a spatial topology matching algorithm; and finally, performing visual early warning based on the BIM abnormal coordinate vector, and generating a structured alarm instruction set by combining risk levels and abnormal type matching treatment measures. According to the method, through multi-source data fusion modeling, feature analysis and extraction, BIM space mapping, three-dimensional visualization decision making and the like, the precision, efficiency and intuition of hydraulic engineering safety early warning are improved, and a reliable technical scheme is provided for guaranteeing safe and stable operation of hydraulic engineering facilities.

The invention belongs to the technical field of virtual power plants, and particularly relates to a virtual power plant resource aggregation method for a dynamic peak regulation demand of a power grid, which comprises the following steps of: acquiring multi-source data, preprocessing the multi-source data, and then verifying the data quality; aiming at different resource types including temperature control load, energy storage and charging piles, respectively constructing refined models, setting constraint conditions of the refined models, and solving a resource operation feasible region by applying multi-dimensional space mapping and linear programming; establishing a target function and a constraint condition by taking the lowest cost and the minimum energy abandoning as targets; solving a target function by using a dung beetle optimization algorithm, and screening an optimal resource aggregation scheme by using an entropy weight method; and based on the optimal resource aggregation scheme, dividing peak, valley and normal periods, constructing a four-dimensional peak regulation index, determining a weight by using an analytic hierarchy process, and screening an optimal resource combination in each period to execute scheduling. The method can guarantee the accuracy and high efficiency of the peak regulation demand response of the power grid, and assists in improving the stability of the power system and the renewable energy consumption level.

The invention discloses a generative odor real-time synthesis method and system based on cross-modal potential space mapping, and belongs to the technical field of artificial intelligence and olfaction calculation. The method comprises the following steps: acquiring a multi-modal input stream of a current scene, and extracting an emotion semantic feature vector by using a deep neural network; mapping the semantic features into target odor chemical feature vectors by using nonlinear projection through a pre-constructed vision-smell joint embedding space; constructing a convex optimization model based on olfactory perception, and calculating a basic liquid optimal mixing proportionality coefficient matrix capable of fitting the target vector; the matrix is converted into a micro-fluidic driving signal, and the target smell is synthesized in situ in the micro-fluidic chip. The invention further discloses a self-adaptive cleaning logic and olfactory fatigue compensation mechanism based on scene mutation detection. The method solves the problems that in the prior art, label matching is dependent, new smell cannot be synthesized, and dynamic transition is lacked, and olfactory replicating and real-time generation of abstract semantic scenes are achieved.

The invention discloses a boundary grid node-oriented shipborne unmanned aerial vehicle delivery scheduling method and system, and relates to the technical field of shipborne unmanned aerial vehicle path optimization, and the method comprises the steps: carrying out the systematic grid division of a boundary region based on a geographic coordinate system and marine environment constraints, and constructing a boundary grid node set; mapping attribution is carried out based on boundary grid nodes, and path feasibility is evaluated; and according to an evaluation result, screening and scheduling candidate tasks, generating a corresponding time window, constructing an optimal path, and completing dynamic scheduling and path planning of multiple tasks through time, space and platform constraint linkage. By constructing a task space mapping structure, an airspace schedulability scoring mechanism and a path multi-dimensional cost evaluation model, dynamic, efficient and safe scheduling of the shipborne unmanned aerial vehicle in a boundary sea area is realized, the task matching precision, the path feasibility and the system robustness are remarkably improved, and the method has high intelligent cooperation capability and environmental adaptability.

The invention discloses an adaptive multi-angle elbow airtightness detection method, which comprises the following steps: arranging a flexible pressure sensor array and a micro inertial measurement unit on the inner wall of an elbow, collecting multi-point sealing pressure and attitude signals in real time, fusing geometric parameters and a spatial mapping relation, and completing denoising, normalization and multi-curvature region mapping of pressure data; a distributed pressure-attitude database is combined, a function model associated with sealing pressure and pipeline geometric parameters is established, inflation, exhaust and deformation of each node are dynamically adjusted through an intelligent closed-loop regulation and control algorithm, collaborative optimization of multi-node pressure and a fitting state is achieved, and the sealing performance of the pipeline is improved. According to the method, the accuracy and the self-adaptive adjusting capacity of sealing pressure distribution under a complex bent pipe structure are effectively improved, the tiny leakage real-time positioning and self-learning optimization capacity is achieved, and the method is suitable for sealing quality guarantee of various types of curved surface pipelines.