349 results about "Cognitive network" patented technology

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 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 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 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 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 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 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 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 sterilization equipment control, in particular to an intelligent control method and device for sterilization equipment and electronic equipment. The method comprises the following steps: deploying a sensing network in a disinfection space and executing environment calibration to generate sensing reference data; synchronously acquiring space environment data through a sensing network; constructing a target recognition model based on the sensing reference data and the space environment data so as to perform dynamic target recognition and contact judgment; determining environment state parameters according to dynamic target recognition and contact judgment results; executing risk partitioning according to the environment state parameters to determine a region type and a risk score; and establishing a killing strategy rule base and executing killing operation according to the area type and the risk score. According to the invention, a differential disinfection strategy is formulated and executed based on dynamic environment perception and partition recognition.

The invention relates to the technical field of traffic engineering management, and particularly discloses a traffic engineering quality safety dynamic supervision and management system and method, and the method comprises the steps: constructing a multi-dimensional sensing network which comprises an entity, an environment and a behavior sensing layer, and collecting data; a three-dimensional model is built based on the BIM technology, and standardized data and model millisecond mapping are fused and visualized; an LSTM and random forest hybrid algorithm is adopted to establish a three-level early warning model, and risks are identified and pushed in a graded manner; matching the disposal flow according to the early warning level, tracking the progress by the mobile terminal, and checking the closed loop in a grading manner; and key node data are stored through a multi-level block structure, an alliance chain permission, a consensus mechanism and an intelligent contract, so that reverse tracing is realized. According to the invention, real-time acquisition, dynamic modeling, intelligent early warning, co-processing and credible tracing of quality safety data in the traffic engineering construction process are realized, the quality safety risk is reduced, and the management efficiency and supervision transparency are improved.

The invention discloses a cross-device multi-target tracking method and system. The method comprises the following steps: converting vehicle driving data collected by a camera and a radar into trajectory data under a global coordinate system; dividing all the sensing devices into a plurality of adjacent device pairs; checking the time dimension and the space dimension of the upstream track and the downstream track which are respectively formed by the adjacent equipment pair in sequence; and if the upstream track and the downstream track meet verification, splicing the track sequence according to a timestamp sequence to perform fusion of the global track, otherwise, temporarily storing the track as an isolated track independent of the global track. The system is used for implementing the method. The method has the advantages that feature extraction is not needed, the calculation complexity is low, and the real-time processing requirement of a large-scale sensing network can be met. Spatial similarity calculation methods are respectively designed for devices of the same type and different types, the cooperation requirements of mainstream sensing devices in an intelligent traffic scene are covered, and the compatibility is high.

The invention relates to the technical field of data mining, in particular to a mining analysis method and system for vehicle wire harness fault data, and the method comprises the following steps: extracting a maintenance timestamp and a path segment index to construct a two-dimensional coding matrix, carrying out the segment division of a vehicle operation mileage, generating a total number of segment faults and a probability sequence, and carrying out the mining analysis. And executing logic comparison according to the serial numbers of the node major loop and the branch line, configuring a path weight parameter, executing tensor operation on the probability sequence and the weight parameter, obtaining an aggregation degree parameter, and constructing a mining analysis result. According to the method, a data multi-dimensional space topological structure is remodeled, dynamic recession mapping is calculated to capture a deep derivation relation, a probability sequence and a weight parameter are input into a sensing network to execute feature extraction, an aggregation degree index is obtained, and according to feature collection operation, space associated conduction paths of wire harness state degradation at different positions are deeply mined. And a fault data mining analysis system with a network deep topology feature analysis capability is formed.

The invention discloses an air-sea cross-domain integrated sensing network and a construction method thereof, relates to the technical field of communication, and adopts an underwater communication neighbor sensing protocol to realize sensing between underwater nodes through the underwater-oriented high-reliability and low-overhead underwater communication neighbor sensing protocol. Secondly, sensing between air-sea cross-domain nodes is realized through self-defined protocol integration on an overwater wireless communication platform, and the shortest link can be adaptively selected for data transmission through overwater topology sensing to underwater topology sensing, so that a traditional air-sea information island is broken through, deep fusion and sharing of cross-domain data are realized, and the data transmission efficiency is improved. A global coverage and real-time cooperation intelligent sensing system is constructed, and the problem that the existing air network, the water surface network and the underwater network are independent of one another, and integrated network sensing of the water target to the underwater target cannot be achieved is solved.

The invention discloses a method, a system, equipment and a medium for improving reliability of high-frequency data acquisition in a multi-element load scene, and relates to the technical field of power system communication and data acquisition, comprising the following steps: broadcasting beacons to a station area and receiving terminal response, constructing dynamic network topology and channel state information, executing dynamic channel allocation based on the received information, and sending the dynamic network topology and channel state information to the station area. The method comprises the following steps: acquiring minute-level data packets, performing fragment coding on the acquired minute-level data packets, selecting an optimal path for each terminal for transmission in combination with a network state, collecting fragments at a receiving side, checking integrity, decoding and restoring when conditions are met, and otherwise, starting a local retransmission mechanism until the data packets are successfully recovered. According to the method, the reliability guarantee system is constructed by dynamically sensing the network state, cooperatively optimizing channel allocation and transmission paths and combining fountain code coding and a precise retransmission mechanism, and the success rate and the integrity of high-frequency data acquisition are remarkably improved in a multi-element load scene.

The invention discloses an urban inland inundation prediction method and system based on space attention and depth fusion, and relates to the technical field of urban inland inundation prediction, and the method comprises the steps: laying monitoring points in a waterlogging-prone risk area, and constructing a dense perception network; constructing a dynamic space attention network, quantifying rainfall-ponding space correlation, calculating a dynamic influence weight through a learnable weight matrix, and generating an attention weight matrix to represent the influence of rainfall points on ponding points; training a long-short-term memory network model, and fusing the feature representation of the spatial correlation information and a historical ponding depth sequence to carry out ponding depth time sequence prediction; and based on topographic feature extraction, physical guide diffusion and point time sequence prediction data conversion, expanding topographic-guided point-surface data, and generating a surface region ponding risk map. Through fusion of a spatial attention mechanism and deep learning, rainfall-ponding spatial heterogeneity association is effectively captured, the accuracy and timeliness of urban inland inundation prediction are improved, and scientific decision support is provided for urban flood control and disaster reduction.

The invention discloses a hierarchical image rain removal method based on enhanced rain stripe perception. According to the method, for the problem of image quality degradation in a rainy day environment, an enhanced rain stripe perception feature enhancement module E-RAFEM is designed, two core components of a learnable direction filter and rain stripe texture modeling are integrated, and the directivity and linear texture features of rain stripes are accurately modeled. An encoder-decoder backbone network based on Transform is adopted, and E-RAFEM modules are embedded in the first three encoding levels, so that a hierarchical rain stripe processing mechanism is formed. A progressive multi-scale fusion mechanism is introduced, multi-scale features are captured through convolution kernels with different expansion rates, and gradual integration is carried out in a progressive mode to avoid information loss. According to the enhanced rain stripe sensing network ERA-Net provided by the invention, high-quality recovery of images under various complex rainy day conditions is realized through accurate rain stripe feature modeling and hierarchical processing strategies.

In one embodiment, a device obtains a training dataset to train a machine learning model to predict a quality of experience metric for an online application accessible via a computer network. The device identifies a range of modifications to a telemetry metric in the training dataset that would be realistic with respect to the computer network and the online application. The device receives feedback from a user interface regarding the range of modifications to the telemetry metric. The device augments the training dataset with additional values of the telemetry metric in accordance with the feedback from the user interface.

The invention relates to the technical field of fermentation systems, and particularly discloses a partition stirring dry type anaerobic intelligent fermentation system which comprises a dynamic reconstruction fermentation unit, a bionic stirring device, a multi-dimensional sensing network, a cognitive control center, an intelligent energy network and a digital twinborn interaction system. The dynamic reconstruction unit and the bionic stirring device improve the fermentation effect; the multi-dimensional sensing network and the cognitive control center realize accurate monitoring and regional optimization; the intelligent energy network improves the energy efficiency and reduces the cost and emission; the digital twin interaction system is combined with AR and block chain evidence storage, so that operation and maintenance are facilitated, and data are guaranteed; the regional function dynamic allocation and optimization algorithm enhances the adaptability and anti-interference performance of the system, and assists the fermentation system to become efficient, intelligent and green.

The invention provides a multispectral visual perception electroslag remelting defect online identification method, which comprises the following steps: constructing a cross-modal perception network through a multispectral camera and an array sensor, and collecting slag bath morphology, multiphase flow parameters and slag system component data; feature fusion is carried out on the multi-source data, a multi-physics field coupling model is established, and a defect inoculation mechanism is analyzed; a hierarchical identification network is constructed based on the fusion features, and defect type determination is realized in combination with a dynamic threshold algorithm; a cross-modal sensing network is constructed to synchronously collect multi-source data, a multi-physics field coupling model is combined to analyze a defect formation mechanism, and a hierarchical identification network is adopted to realize dynamic threshold judgment, so that the technical problem that a traditional method cannot track multiphase flow dynamic and slag system component changes in real time is effectively solved, and the method is suitable for large-scale popularization and application. The method has the capability of capturing multiphase flow dynamic characteristics of the molten pool and real-time evolution of slag system components at the same time, and the identification accuracy of slag inclusion type defects is remarkably improved.

The invention provides a saliency-enhanced adaptive collaborative sensing method for surface defect detection. The saliency-enhanced adaptive collaborative sensing method comprises the following steps: S100, constructing a surface defect data set; s200, constructing a saliency enhanced adaptive collaborative awareness network; s300, training the saliency enhanced adaptive collaborative awareness network; s400, performing defect segmentation by using the trained network model; different from the existing scheme adopting the traditional convolution or single path feature extraction, the method disclosed by the invention has the advantages that through shallow saliency enhancement, high-level multi-scale semantic modeling and an edge-region collaboration mechanism in a decoding stage, the edge-region collaboration mechanism in the decoding stage is utilized to adapt to the conditions of edge blurring, large scale difference, random distribution, complex illumination conditions and the like of surface defects; defect features can be effectively expressed at different scales and different structure levels, so that the accuracy and stability of overall segmentation are improved.

The invention relates to the technical field of network scheduling, and discloses an agricultural heterogeneous perception and intelligent network scheduling method and system, and the method comprises the following steps: S01, a system initialization stage; step S02, a resource scheduling and strategy learning stage; step S03, a sensing data fusion and processing stage; s04, a security mechanism and life cycle management stage: adopting an end-to-end encryption and identity authentication mechanism in a data transmission process, and collecting feedback parameters for updating a scheduling strategy and releasing resources after a task is completed; the system comprises a multi-mode agricultural sensing terminal, intelligent gateway equipment, a 5G and LPWAN hybrid access network, a resource scheduling module and a scheduling strategy learning engine. According to the method, the multi-dimensional state characteristics such as the network congestion level, the edge node load, the data timeliness and the task priority are sensed in real time through the deep reinforcement learning model, and the optimal allocation decision of communication and computing resources is dynamically generated.

The invention relates to the technical field of financial industry big data management, in particular to a financial knowledge graph updating and compliance verification method based on multi-source heterogeneous data, and the method comprises the steps: building a global sensing network for the financial field, collecting multi-source heterogeneous financial data in real time, and carrying out the normalization processing; performing named entity recognition and relation extraction on the normalized financial data by using a pre-trained large language model, and constructing a financial knowledge graph; when a new supervision rule is released, detecting whether there is a conflict between the new supervision rule and the old supervision rule, and if there is a conflict, carrying out lossless dynamic updating on the financial knowledge graph; and when a financial service request is received, taking a service main body and a service type corresponding to the current financial service request as anchor points, extracting a local sub-graph from the financial knowledge graph, reasoning the local sub-graph by using the graph neural network, and finally outputting an interpretable compliance audit report.

An information processing method, a communication device, and a storage medium are provided. The method includes: an AMF receives request information sent by UE via a base station, wherein the request information is configured to request for sensing service capability information of a network side.

The invention discloses a cable partial discharge accurate positioning method and system based on conjoint analysis, and relates to the technical field of cable discharge positioning, and the method comprises the steps: constructing a multi-source sensing network which is composed of a UHF sensor, an ultrasonic sensor, and a transient voltage / current sensor; obtaining a geometric path, a shielding layer and a dielectric distribution parameter of the cable, executing digital twin modeling of the cable, and establishing a propagation time difference and interference compensation model; accessing a time synchronization network, and reading an acquisition signal set; multi-source signal partial discharge positioning identification is executed, and a fuzzy partial discharge area is established; constructing an error objective function by using the observation time difference and the prediction time difference; and game search is carried out to establish partial discharge positioning. The technical problems that in the prior art, cable information collection is limited, cable structures and medium parameters are difficult to comprehensively analyze, and cable discharge positioning accuracy and reliability are insufficient are solved, and the technical effect of improving cable partial discharge positioning accuracy and running state monitoring reliability is achieved.

The application discloses a cloud multimodal consistency constraint key abnormal event identification method and system. For the multimodal time series data generated by cameras, environmental sensors and other devices in smart city, industrial internet of things and other scenes, a completely unsupervised event-level abnormal understanding framework is proposed: through a multimodal consistency constraint mechanism, a cooperative abnormal period is accurately positioned; the abnormal period is clustered online to form an event cluster; based on Z-score significance analysis, the most abnormal feature dimension in each cluster is extracted and mapped into a human-readable semantic description; combined with the three indexes of the number of affected devices, duration and state deviation, the keyness of the event is quantified, and a ranked list is output. The application does not need to predefine event types or label data, can discover, name and sort known and unknown abnormal events at the same time, significantly improves the discovery and response efficiency of key abnormal events, and is suitable for lightweight cloud deployment of large-scale sensing networks.

The invention provides a low-temperature refrigerator intelligent starting method and system based on a swimming pool compressor. The method comprises the following steps: acquiring multi-dimensional monitoring data of key components around a swimming pool and inside a compressor in real time through a multi-dimensional sensing network, and uploading the multi-dimensional monitoring data to an edge computing node; through a lightweight neural network model deployed in the edge computing node, carrying out real-time preprocessing on the multi-dimensional monitoring data and constructing to obtain a multi-dimensional strategy for the swimming pool compressor, and uploading the multi-dimensional strategy to a cloud end; a multi-dimensional strategy is simulated and operated through a virtual swimming pool compressor synchronously maintained by a cloud end and a hybrid simulation algorithm based on digital twinning and fuzzy logic, so that a simulation execution effect is obtained; and optimizing a multi-dimensional strategy based on the simulation execution effect, and issuing the multi-dimensional strategy to an Internet of Things management and control end to realize data closed-loop and cooperative control of the swimming pool compressor. Low-temperature cold machine starting and comprehensive management of the compressor can be achieved, and the starting reliability and the operation energy efficiency of the compressor in the low-temperature and high-humidity environment are improved.

The invention relates to the technical field of branch box state monitoring, and discloses a low-voltage branch box state monitoring and early warning method based on the Internet of Things, and the method comprises the following steps: S1, deploying multiple types of sensors in and around a low-voltage branch box, and constructing a full-dimensional sensing network; s2, installing an edge calculation module in the branch box, and carrying out the real-time preprocessing of the sensor data in S1; and S3, constructing a local-cloud hierarchical communication network. According to the invention, multiple types of sensors are deployed inside and around a low-voltage branch box, a full-dimension sensing network is constructed, the full-dimension sensing network comprises an electrical parameter sensor, a mechanical vibration sensor, a harmful gas sensor, an environment sensor and a switch state sensor, a local-cloud hierarchical communication network is constructed, and fused equipment state vectors are transmitted to a cloud platform. And the data transmission reliability is ensured, and scene decision suggestions are given, so that the method has the advantages of multi-source data fusion, hierarchical communication, graded early warning and the like.

The embodiment of the invention provides a network configuration change method and device and a storage medium, the method is applied to an operating system providing networking service, the operating system comprises a control plane and a data plane, and the method comprises the following steps: acquiring network configuration change information corresponding to a target network through the control plane; generating a plurality of sequentially executed network configuration change tasks corresponding to the network configuration change information in the control plane; in response to execution of the target network configuration change task in the control plane, network connectivity detection of the target network is started in the data plane, and the target network configuration change task is a network configuration change task configured with a network connectivity detection trigger node; and determining the network state of the target network after the network configuration change according to the network connectivity detection result. According to the scheme, through linkage of the control plane and the data plane, network connectivity detection of the data plane is automatically triggered, network abnormity can be sensed in time, and the stability of a target network is improved.

The invention discloses a building fire intelligent detection and positioning method based on multi-source data fusion, and the method comprises the steps: collecting active and passive differentiated data through a double-sensing-layer architecture, and constructing a full-coverage information complementary sensing network; establishing a dynamic game mechanism based on the two types of differentiated data, judging the credibility of a data source in real time, and generating a high-credibility fusion feature set; and performing parallel research and judgment through multi-head deep learning research and judgment based on the fused feature set, and outputting a fire information research and judgment result. And generating a training sample with a label by using a fire information research and judgment result to optimize dynamic game and multi-head deep learning research and judgment parameters, and correcting historical misjudgment. The invention aims to solve the technical problems that an existing fire detection system is high in false alarm rate, poor in positioning precision, incapable of identifying the type of a fire source, lack of adaptive ability, high in deployment cost in a complex environment, and has a monitoring blind area and the like.