746 results about "Cognitive network" patented technology

The invention provides an AI-based big data distributed computing task automatic optimization method and system, and relates to the technical field of AI. The method comprises the following steps: constructing a multi-modal spatial-temporal feature sensing network to extract spatial-temporal features of tasks and resources, and training a hierarchical hybrid decision network to perform global task scheduling and local resource allocation. The global strategy network adopts a layered deep reinforcement learning architecture, and generates a decision by combining Monte Carlo tree search and priority experience playback; and the local execution network optimizes local deployment based on the graph neural network and multi-agent collaborative learning. In addition, a distributed anomaly detection network is deployed to monitor the performance in real time, self-adaptive adjustment and optimization are achieved through enhanced transfer learning, and finally the perception network is updated through knowledge distillation to achieve model evolution. According to the method, the execution efficiency and the resource utilization rate of the big data distributed computing task can be effectively improved, and the system operation cost is reduced.

The invention provides a building dynamic structure health monitoring method. The method comprises the steps that original data of a multi-mode sensor is acquired and preprocessed; constructing topology sensing network representation reflecting the spatial distribution and structure connection relation of the sensors; extracting multiple spatio-temporal scale features; local-to-global structure state mapping is realized by using a recurrence plot evolution predictor; integrating structural dynamics knowledge into the neural network model to perform physical constraint correction; and constructing a multi-dimensional health index set and outputting damage probability evaluation. According to the invention, accurate mapping from the local dense monitoring area to the global structure state under the non-uniformly distributed sensor network is realized, and the accuracy and reliability of structure health monitoring are improved.

The invention provides a data knowledge graph construction method and system driven by a complex ecological smart brain, and relates to the technical field of complex ecology, and the method comprises the steps: collecting scene data through a depth feature sensing network, and generating an initial graph structure; entity similarity is calculated based on the map feature fingerprints, related entity pairs are screened, multi-hop path reasoning is carried out, and entity association confidence indexes are generated; generating a multi-modal knowledge representation vector based on multi-modal feature extraction and a cross-modal attention mechanism; the method comprises the following steps: identifying influence characteristics, calculating a propagation probability in combination with a problem evolution diagram and causal reasoning, performing comparative analysis on the propagation probability and a map characteristic fingerprint to generate an optimization target, generating an error correction strategy by using an Adam optimizer, dynamically adjusting the reasoning depth through a double-DQN network, forming an optimization closed loop, and constructing a data knowledge map. According to the method, the accuracy and integrity of the knowledge graph can be effectively improved, the reasoning ability is enhanced, and the method can better adapt to knowledge discovery and application in a complex ecological scene.

The invention relates to the technical field of wireless communication, discloses an air-sea cross-domain network reinforcement learning routing algorithm based on an SDN (Software Defined Network), and mainly aims to solve the problems of poor dynamic environment adaptability, insufficient multi-task QoS (Quality of Service) demand guarantee capability and the like caused by a static rule or local optimization of an existing air-sea cross-domain routing algorithm. Global network state perception and resource centralized scheduling are realized through an SDN controller, and a dynamic routing decision model driven by deep reinforcement learning (DRL) is constructed. A network state is sensed in real time through an SDN controller, a multi-target reward function is designed, and a cross-domain path selection strategy is dynamically optimized. According to the method, the limitation of a traditional routing protocol in a cross-domain heterogeneous network is broken through, the end-to-end transmission efficiency, the resource utilization rate and the multi-task differentiated service quality guarantee capability are remarkably improved, and the method can be widely applied to the scenes of marine environment monitoring, emergency rescue, cross-domain cooperative detection and the like.

The invention relates to the technical field of equipment life management, in particular to a life prediction method and system based on an application analysis spindle, and the method comprises the steps: building a modal sensing network, collecting multi-source operation data in the operation process of the spindle, and generating a multi-source operation characteristic matrix; based on the multi-source operation characteristic matrix, establishing multi-application characteristic mapping, and generating a multi-scene characteristic vector for describing an equipment operation state; performing multi-dimensional coupling fatigue analysis on the multi-scene feature vector, and outputting a fatigue damage accumulated value of the main shaft; and according to the fatigue damage accumulated value, obtaining and combining historical motion data of the main shaft, establishing a different-parameter life prediction model, and outputting a residual life prediction value based on the different-parameter life prediction model. According to the invention, the problems of single data source, insufficient prediction precision under complex working conditions, difficult modeling of nonlinear fatigue damage and lack of dynamic feedback and correction capability in a traditional main shaft life prediction method are effectively solved.

The present invention relates to the technical field of environmental perception in confined spaces, and discloses a multi-modal coupled perception method for target recognition and region segmentation in a confined space. The method comprises: firstly, constructing a 3D point cloud data processing network, a 2D point cloud data processing network, and an image data processing network; next, constructing a feature fusion device, and outputting a bird's-eye view incorporating multi-sensor information; then constructing a multi-scale feature fusion extraction network and a network output head, outputting a target recognition and region segmentation result, and designing a loss function to train a network weight; and finally, inputting 3D point cloud data, 2D point cloud data, and camera image data into a network model, performing inference to obtain a target recognition and region segmentation prediction result, and performing visual rendering on the prediction result. In the present invention, in view of the characteristics of a millimeter-wave radar, a multi-modal coupled perception network is constructed, so that effective information in mass point cloud data of the millimeter-wave radar can be deeply mined, thereby effectively improving the detection accuracy of target recognition.

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 discloses a multi-modal network state detection method based on large model alignment, which is used for processing multi-modal data of a communication network. The method comprises the steps that multi-modal data such as a time sequence and a text are converted into a unified semantic space through blocking and embedding, multi-modal features are aligned through cross-modal reprogramming by utilizing a text prototype library and an attention mechanism, and an injection task instruction and domain knowledge are enhanced by setting a dynamic prompt prefix so as to activate the LLM reasoning ability; then, LLM trunk model parameters are frozen, only an input and output adaptation layer is trained, and a lightweight optimal adaptation model is constructed; and generating a network state prediction result based on the adaptation layer. According to the method, dynamic alignment of multi-modal network data is achieved, the generated joint semantic representation can be further used for LLM reasoning, the purpose of more accurately sensing the network state is achieved, network state reasoning under few-sample and zero-sample scenes is supported, and the method is suitable for a heterogeneous network environment.

The invention discloses a multi-class network security threat perception and active and passive cooperative response processing system and method, and the system comprises a multi-class threat perception module which is used for perceiving a plurality of security threats existing in a network environment, and transmitting an obtained security event to a threat information association module; the threat information association module is used for analyzing and integrating various security events and extracting threat information from the security events; the attack graph-based threat path analysis module is used for mining vulnerability information in a network system, describing a network topology structure and an operation state, constructing an attack graph and obtaining active defense nodes in combination with a currently occurring security event and an analysis result of the attack graph; and the strategy generation module generates a corresponding response processing strategy according to the threat information and the property and the emergency degree of the security event, and generates an active defense strategy in combination with the active defense node, thereby realizing active defense and advanced deployment and control of potential threats. According to the invention, network security threats can be timely and effectively found and coped with.

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 smart park security and protection system and a control method thereof, and relates to the technical field of smart security and protection, the system comprises the following components: a diversified sensing module which integrates a video monitoring sensor, a face recognition sensor and a behavior analysis sensor and constructs an omnibearing sensing network. According to the technical scheme, all-dimensional and multi-angle monitoring and recognition of the park are achieved, the information acquisition capacity of the security and protection system is remarkably improved through the technological innovation, the comprehensiveness and accuracy of data are ensured, the diversified sensing module integrates multiple sensors of video monitoring, infrared detection, face recognition and behavior analysis, each sensor performs own functions and complements each other, and the monitoring and recognition efficiency is improved. According to the system, blind areas and errors possibly generated by a single sensor are effectively eliminated, the multi-angle sensing network enables the system to more accurately capture abnormal conditions in the park, and a solid foundation is provided for subsequent intelligent analysis and early warning.

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 discloses a multi-modal sentiment analysis method and system based on multi-dimensional perception, and relates to the technical field of sentiment analysis, and the method comprises the steps: inputting to-be-recognized multi-modal sentiment data into a trained multi-modal sentiment analysis model, the multi-modal sentiment analysis model comprises a feature extraction network, a transmembrane interaction network, a multi-dimensional sensing network and a sentiment prediction network; performing feature extraction on the to-be-recognized multi-modal emotion data through the feature extraction network, and constructing aligned text features, aligned audio features and aligned video features; inputting the aligned text features, the aligned audio features and the aligned video features into a cross-membrane interactive network for feature interaction, and determining interactive fusion features; carrying out feature extraction on the interactive fusion feature along a plurality of feature dimensions in sequence based on a multi-dimensional sensing network to generate a mixed multi-modal feature; and performing emotion scoring on the mixed multi-modal features by using an emotion prediction network, and outputting a target emotion score. Based on the above scheme, improvement of sentiment analysis precision is facilitated.

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 provides an automatic perception model method and system for illegal access in a network security isolation area, and belongs to the technical field of computer systems based on specific calculation models.The method comprises the steps that firstly, a network topological graph matrix of the security isolation area is constructed, an equipment asset list is established, and then distributed flow collection nodes are deployed to obtain real-time network data; a deep packet detection technology is used for extracting features to establish an equipment behavior baseline library, a multi-target risk assessment function is used for carrying out risk grade division on equipment, a multi-layer perceptron and a time sequence anomaly detection algorithm are used for identifying abnormal communication, and an equipment fingerprint identification mechanism based on physical layer characteristics is established to verify the legality of the identity of the equipment. A security isolation intelligent sensing network model is utilized to analyze network behaviors, a multi-dimensional abnormal scoring system is constructed to calculate risk scores, a response mechanism based on a rule engine is realized, a federal learning technology can be selectively adopted to optimize the model, and an all-dimensional and multi-level illegal access automatic sensing protection system is formed.

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 discloses a remote sensing cross-modal retrieval method based on a deep ternary fusion sensing network. According to the method, a trained remote sensing cross-modal retrieval model is used for carrying out cross-modal retrieval on multi-modal remote sensing data; the remote sensing cross-modal retrieval model takes multi-modal remote sensing data as the input of the self-supervised modal enhancement module for preprocessing, and inputs the multi-modal remote sensing data into the ternary feature expression module for independently capturing and extracting single-modal image features, text features and audio features, and the features are used as the input of the fusion sensing module so as to fuse and generate multi-modal feature embedding; and embedding according to the multi-modal features to obtain a remote sensing cross-modal retrieval result of the to-be-processed multi-modal remote sensing data. According to the method, a ternary feature expression strategy is utilized, a sensing mechanism and a self-supervised modal enhancement technology are fused, the key problems of model modal expandability, scarcity and high cost of remote sensing annotation data and the like are solved, and the precision and efficiency of a remote sensing data cross-modal retrieval task are remarkably enhanced.

The invention discloses an intelligent safety management method for a gas operating enterprise. According to the invention, through deep fusion of Internet of Things perception and intelligent decision technologies, the full-period management capability of safety risk prevention and control is significantly improved. On the aspect of beforehand prevention, the global sensing network and the dynamic risk assessment system form double defense lines, hidden danger signals such as pipe network pressure abnormity and equipment aging are captured in real time, the risk evolution trend is pre-judged in combination with meteorological environment changes, and accurate deployment of protection resources is guided. The autonomous inspection robot cluster breaks through the space-time limitation of manual inspection, continuously obtains high-precision detection data under complex terrains and severe weather, and ensures that hidden danger recognition is free of dead angles. The user side safety enabling system converts professional protection knowledge into visual interaction guidance, so that residents can check hidden dangers of gas appliances by themselves, and an enterprise-user linkage basic protection net is constructed.

The invention relates to the technical field of cultural tourism guide, in particular to a marine culture intelligent guide system based on artificial intelligence and the Internet of Things, which integrates multi-source data acquisition, context semantic understanding, user portrait construction, personalized content recommendation and adaptive content presentation, and the system acquires user voice, position, facial image and environment information, so as to realize the intelligent navigation of the marine culture. Accurate data acquisition is realized; the system combines semantic understanding and user portraits, dynamically generates interest models, recommends personalized marine culture contents to users, ensures effective information transmission through a self-adaptive presentation module, optimizes resource allocation through a distributed processing module, guarantees stable operation of the system, constructs a user-environment-cultural relic three-dimensional perception network, improves the acquisition precision by 65%, and improves the real-time performance of the system. The data dimension is obviously expanded, a comprehensive data basis is provided for intelligent navigation, and efficient and personalized cultural tourism experience is achieved.

The invention discloses a travel emergency state judgment method and device based on semantic analysis and a storage medium, and relates to the technical field of classification models. Comprising the following steps: extracting speech characteristic parameters such as speech speed, volume, speech signal spectrum entropy and the like by collecting speech data of an independent user with known emotion and target intention, and calculating a speech judgment coefficient; and based on the voice text data, performing voice recognition by using a knowledge perception network model, and generating a danger feature comprehensive vector. And using the vector to train a neural network model, taking the danger feature comprehensive vector as input, and predicting a corresponding emotion and a target intention. And monitoring voice data of the independent user in real time, obtaining a corresponding danger feature comprehensive vector, calculating a comprehensive emergency state judgment index by combining the voice judgment coefficient and the environment data, comparing the index with a risk threshold value, and outputting an emergency state instruction. The method can effectively predict the emergency situation of the user in travel, and provides support for safety emergency.

The invention relates to a multi-source sensing fusion and dynamic resource allocation method based on vehicle infrastructure cooperation, and belongs to the technical field of mobile communication. The single-vehicle environment sensing is to complete the detection of the surrounding environment by using the sensing data of the single-vehicle environment sensing. However, the problem that heterogeneous data fusion efficiency is low due to the fact that the sensing range of single-vehicle sensing is limited exists. In order to solve the problem, the method comprises the following steps: firstly, constructing a vehicle-road double-layer cooperative sensing network, and improving the accuracy of shelter detection through a multi-source sensor space-time alignment and confidence weighting mechanism; secondly, designing an improved discrete dogvessel swarm optimization algorithm, balancing global exploration and local development by using a chain collaborative search strategy, and improving the perception fusion quality; and finally, developing a mixed action space depth deterministic strategy gradient algorithm, dynamically coordinating the resources of the intelligent network connection vehicle and the road side unit, and reducing the sensing time delay. The scheme is suitable for environmental perception optimization and edge computing resource scheduling of the vehicle in a complex traffic scene, and aims to improve perception fusion efficiency.

The invention provides a network quality optimization method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining multi-dimensional real-time network operation data; based on the multi-dimensional real-time network operation data, training through a network quality evaluation model to obtain a real-time network quality evaluation result output by the network quality evaluation model; and dynamically performing network quality optimization based on the real-time network quality evaluation result. According to the network quality optimization method provided by the invention, the change trend of the network state is accurately perceived by acquiring the multi-dimensional real-time network operation data and utilizing the network quality evaluation model, so that the method can adapt to a complex and changeable network environment to realize accurate evaluation of the network quality, and the network quality is optimized based on the real-time network quality evaluation result. The network quality can be dynamically optimized, the efficient utilization and stability of network resources are ensured, and adaptive and intelligent network management is realized, so that the network operation cost is effectively reduced, and the network investment is saved.

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 provides a self-adaptive collaborative acoustic environment active treatment system and method. The system comprises a multi-dimensional environment sensing network module, an intelligent voiceprint recognition and sound field prediction module, an active noise control module, a central intelligent collaborative regulation, diagnosis and self-learning module and a passive acoustic intervention module. The multi-dimensional environment sensing network module collects multi-dimensional sensing data in real time; the intelligent voiceprint recognition and sound field prediction module outputs a voiceprint recognition result and a noise source space coordinate and predicts a sound field evolution trend; the active noise control module outputs a residual noise signal; the central intelligent cooperative regulation, diagnosis and self-learning module outputs an active and passive cooperative control instruction set; the passive acoustic intervention module controls broadband noise to block or change its propagation path. According to the invention, through a dual-channel self-learning mechanism of the deviation diagnosis capability, continuous evolution of system performance can be realized according to a deviation autonomous optimization acoustic model and a control strategy, and accurate, efficient and prospective active treatment is carried out on a complex noise environment.

Implementations are directed to receiving graph data representative of a process-aware AAG that is representative of potential lateral movement of adversaries within a computer network, receiving risk profile data representative of a risk profile of an enterprise with respect to two or more risk aspects, generating, by a process-aware risk assessment module, a risk assessment based on the process-aware AAG and the risk profile, and generating, by a mitigation simulator module, a mitigation list based on the process-aware AAG, the risk profile, and the risk assessment, the mitigation list comprising a prioritized list of two or more facts of the process-aware AAG. Other implementations of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

The invention provides a multi-scene fusion perception system and method of a humanoid robot olfaction sensor, and relates to the technical field of intelligent sensors, and the system comprises an olfaction perception optimization module which is used for obtaining an olfaction perception optimization network; the first matrix module is used for monitoring a gas pipeline to obtain a first matrix; the second matrix module is used for performing interference compensation according to a real-time environment; the third matrix module is used for correcting according to the state of the sensor; the leakage detection module is used for detecting pipeline leakage; and the suppression verification module is used for carrying out false alarm suppression verification on the pipeline leakage detection result to obtain a pipeline early warning signal. The gas leakage detection method and device can solve the technical problem that the gas leakage detection precision is limited due to the fact that the gas leakage detection device is prone to environmental interference and high in false alarm rate in the prior art, and can adjust the multi-node olfaction sensing network in real time through historical data, conduct multi-level correction on monitoring data and introduce false alarm suppression verification, thereby improving the detection accuracy of gas leakage. The accuracy of gas leakage detection in a multi-scene environment is improved.