706 results about "Smart city" patented technology

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention discloses a single-view large-scale outdoor scene three-dimensional reconstruction method based on three-dimensional Gaussian splashing, and the method comprises the steps: collecting a pseudo aerial image, and constructing a panoramic multi-mode supervision end-to-end single-view three-dimensional reconstruction model; meanwhile, panoramic consistency supervision, semantic constraint depth regularization and a radial weighted luminosity loss and Gaussian cutting mechanism are introduced, so that the defect of insufficient geometric constraint of traditional single-view three-dimensional reconstruction is effectively overcome, and high-efficiency and high-fidelity three-dimensional modeling of a large-scale outdoor scene under single image input is realized; the method is suitable for various actual scenes such as smart city construction, automatic driving simulation, virtual reality / augmented reality, digital twinning and the like.

The invention relates to the technical field of smart city traffic, and discloses a smart city traffic dynamic optimization system and method based on digital twinning. The system obtains urban traffic network multi-dimensional data from a plurality of heterogeneous data sources through a traffic multi-dimensional data acquisition module and integrates the urban traffic network multi-dimensional data into a traffic related data warehouse; a traffic digital twinning model construction module extracts features from the data warehouse to generate a traffic related feature matrix, and a digital twinning traffic dynamic model is constructed according to the traffic related feature matrix to output a theoretical traffic state value; the traffic flow map construction module determines a dimension link map of each dimension and constructs a traffic flow link map; the traffic core feature screening module screens a traffic core feature sequence based on the map; the traffic multi-dimensional optimization analysis module performs multi-dimensional difference analysis on the theoretical traffic state value and real-time actually measured traffic data, and generates a region-level difference coefficient matrix in combination with the core feature sequence; and the traffic event association positioning module can realize accurate management and dynamic optimization of urban traffic.

The invention discloses a method for evaluating evolution characteristics and evolution driving factors of a space-time pattern of a park green land, and particularly relates to the technical field of crossing of smart cities and landscape ecology, and the method comprises the steps: obtaining multi-period remote sensing image data and planning maps, and constructing a multi-source database; interpreting the image based on a random forest supervised classification algorithm to generate a park green space distribution map, and optimizing the precision; adopting a landscape ecology theory to select a landscape pattern index to quantify space-time evolution characteristics; constructing a driving evaluation index system, and analyzing a driving mechanism by means of a multiple regression model and a space measurement model; and generating a three-level spatial pattern optimization strategy based on the result and outputting a visual decision map. According to the method, the spatial-temporal pattern evolution characteristics and driving factors of the urban park green land can be comprehensively analyzed, and a scientific basis and decision support are provided for planning, management and protection of the urban park green land.

The invention relates to the technical field of adaptive routing optimization, and particularly discloses a topology self-recognition and routing optimization method and system for a concentrator and a collector, and the method comprises the steps: carrying out the topology modeling of a multi-layer heterogeneous network; executing high-speed link state monitoring based on the initial topological graph; global loop detection and link optimization are completed based on the link state matrix; a weighted robust multi-dimensional path selection method is adopted to configure a main path and a standby path for each node; and monitoring the path state in real time and dynamically switching. In the prior art, a static route, a single protocol path or a fixed main and standby path is mainly adopted, and especially in a cross-protocol, cross-level and dynamic multi-node environment in a smart city, rapid path switching and high-reliability route redundancy guarantee cannot be realized. Due to the fact that the multi-dimensional path selection method and the cross-level and cross-protocol self-adaptive switching strategy are adopted, the problems of routing interruption and performance reduction are avoided, and the reliability of the whole network is improved.

The invention discloses a real-time reservation and scheduling system for shared parking, and relates to the technical field of intelligent traffic and smart city management, and the system comprises a demand analysis module which is used for extracting parking lot scene types, time requirements and priority information from user parking request data, generating standardized demand description through dynamic weight distribution, and sending the standardized demand description to a user; the process decomposition module is used for decomposing a parking scheduling process into demand receiving, resource matching and path planning business units according to parking lot scene types and time requirements in the user demand analysis result, and determining a business unit set; according to the real-time reservation and scheduling system for shared parking, through dynamic weight distribution and real-time response optimization, efficient resource distribution and path planning are ensured, and the parking scheduling efficiency and the user experience are remarkably improved.

The invention discloses a steam pipe network decoupling steady-state optimization method and device, a medium and a program product, and relates to the technical field of smart city guarantee. The method comprises the following steps: determining node pressure distribution data and flow distribution data when a mass and momentum balance condition is satisfied according to fluid distribution data of a pipe network simulation model corresponding to a physical structure and an actual working condition of a steam pipe network; and determining node enthalpy data meeting an energy balance condition. Determining a condensation phase change node according to the node pressure distribution data and the node enthalpy data, associating the steam enthalpy of the condensation phase change node to a saturation state under the current pressure, and updating a current fluid distribution parameter according to the determined condensation water volume, and taking the parameters of the pipe network simulation model finally reaching the dynamic balance state as the operation state parameters of the steam pipe network under the target operation condition. The problem that the steam pipe network parameters cannot be accurately determined in the prior art can be solved, and the running state parameters of the steam pipe network can be accurately determined.

The invention discloses a smart city management system fusing artificial intelligence and big data, and relates to the technical field of smart city management, and the system comprises a data collection and feature extraction module which is used for obtaining and processing multi-source city data, extracting road network topological features, functional area distribution features and people flow dynamic features through a graph neural network, and obtaining a data collection and feature extraction module; generating and storing a structured feature vector; the cross-domain knowledge migration model construction module is used for constructing a city simulator, generating virtual data through a generative adversarial network and reinforcement learning, bridging a source domain and a target domain based on the virtual data, and realizing migration of source domain knowledge to the target domain by utilizing graph matching and an optimal transmission theory; and the model optimization and alignment module is used for aligning feature distribution of a source domain and a target domain through confrontation training of a domain discriminator and a gradient inversion layer, dynamically adjusting data weight, realizing seamless migration from virtual distribution to real distribution of the target domain, and realizing seamless knowledge migration from a mature urban area to a new urban area.

The invention provides a smart city construction energy intelligent management scheduling regulation and control system, and relates to the field of energy intelligent management, comprising the following steps: the system detects energy physical parameters in real time through an event chain generation module, maps data into event nodes by using an event feature extraction chip, and sends the event nodes to a cloud server; an event chain topology is constructed through a causal association engine, and a nonlinear priority is generated; the responsibility collaboration module creates a scheduling instruction and a responsibility node based on the priority, calculates a responsibility weight through a reverse causal tracing algorithm, triggers cross-system permission degradation when the weight exceeds a threshold, and migrates the operation permission to an associated system; the paradox enhancement module injects controllable disturbance when the event chain conflicts or the responsibility nodes continuously exceed the limit, the disturbance effect is evaluated through the paradox index, the event chain topology is fed back and updated, and dynamic optimization and hidden risk prevention and control of energy dispatching are achieved.

The invention relates to the field of smart cities, in particular to an urban landscape semantic segmentation method and system based on a large model, and the method comprises the steps: obtaining street view image data of a target geographic region; inputting the streetscape image data into a preset image semantic segmentation model, analyzing the streetscape image data through the image semantic segmentation model, and identifying various predefined environmental element categories contained in each image; calculating an urban landscape quantitative index based on the recognition result of the environmental element category; associating the calculated value of the urban landscape quantitative index to a corresponding road section or node in the road network spatial data corresponding to the target geographic area; generating a road network space visualization layer representing the value of the index value in a color grading manner based on the result after association; and according to a preset index threshold value, identifying the spatial road sections or nodes meeting the early warning conditions in the visual layer and generating prompt information. And the scientificity and comparability of an analysis result are ensured.

The invention provides a smart city water supply pipe network emergency supervision Internet of Things large model system and method, and relates to the field of city water pipe monitoring, and the system comprises a government supervision management platform, a government supervision sensing network platform, a government supervision object platform, a water affair company sensing network platform, and a smart water affair equipment object platform. The government supervision and management platform is configured to determine time sequence flow corresponding to the multiple water pipe node groups based on water flow characteristics of the multiple water pipe nodes in the water supply network; based on the time sequence flow, determining water and soil loss coefficients corresponding to the one or more target areas; based on the water and soil loss coefficient, temporary control parameters are generated through a parameter generation model; and on the basis of the temporary control parameters, the valve opening degrees of the corresponding valves in the one or more target areas are controlled. The system can determine the control parameters of the adjusting device according to the water and soil loss coefficient of the target area, and realizes informatization and intelligence of urban water pipe emergency monitoring.

The invention relates to the technical field of smart city and emergency management, in particular to a cross-domain data fusion early warning method for urban inland inundation. The method comprises the following steps: acquiring original multi-source heterogeneous data; performing spatial registration and rasterization processing on the original multi-source heterogeneous data to obtain an urban grid attribute table; constructing an urban bearing bottom plate according to the urban grid attribute table to obtain a multi-dimensional static bearing capacity map; collecting urban real-time state data flow, and performing influence factor superposition analysis on the multi-dimensional static bearing capacity map to obtain a comprehensive influence coefficient distribution map; and extracting a reference drainage capacity value of each grid in the multi-dimensional static bearing capacity map, and performing drainage capacity dynamic correction in combination with the comprehensive influence coefficient distribution map to obtain an instantaneous drainage efficiency matrix. According to the method, the problem that the traditional method cannot dynamically quantify the influence of the urban operation state on the drainage system is solved, and finally accurate tracing and cascade influence early warning of the waterlogging risk are realized.

The invention discloses a multi-scene micro garbage real-time detection method and a multi-scene micro garbage real-time detection system, and aims to solve the defects of an existing garbage detection method in the aspects of small target identification, environmental adaptability and system integration. According to the method, on the basis of an improved YOLOv8 architecture, a Transform module and an efficient channel attention mechanism are fused, and the tiny target feature extraction and multi-scale information fusion capability is enhanced. The system adopts dynamic resolution reasoning, INT8 quantitative compression and time-space domain post-processing, so that the detection precision and the reasoning efficiency are remarkably improved. Experiments prove that the system effectively improves the small target recall rate and environmental adaptability in a garbage detection task, comprises eight corresponding modules, realizes full-process automatic detection, has relatively high real-time performance and automation level, is suitable for various application scenes such as smart city environment monitoring, and has important practical application value.

The invention discloses a smart city gas pipeline safety intelligent monitoring method based on digital twinning, and relates to the technical field of gas pipeline monitoring, and the method comprises the steps: building a gas pipe network digital twinning body of a target city gas pipeline based on digital twinning technology simulation; performing pipeline state calculation on a monitoring blind area pipeline section in the gas pipe network by using an interpolation prediction algorithm and a fluid simulation technology to generate a global monitoring data sequence network; performing pipeline leakage risk prediction on the plurality of pipeline sections according to the global monitoring data sequence network, outputting a plurality of pipeline leakage risk indexes, and constructing a pipeline leakage risk thermodynamic diagram; and identifying and marking a high-risk leakage area as a priority inspection area in the pipeline leakage risk thermodynamic diagram, generating an inspection work order based on the priority inspection area, and assigning the inspection work order to an inspection personnel terminal for manual inspection of pipeline leakage. The technical problems of low efficiency and poor early warning effect caused by blindness of gas pipeline inspection in the prior art are solved.

The invention discloses a smart city data capitalization-oriented data right confirmation management method and system, and belongs to the technical field of data resource management. The method comprises the following steps: receiving data assets uploaded by a user and carrying out multi-modal preprocessing on data contents to generate a data fingerprint with a unique identifier; counting the proportion of the unconfirmed right data fragments; judging an infringement risk level of the data assets in combination with a risk early warning model; if the unconfirmed right proportion exceeds the dynamically calculated threshold value range or the infringement risk level reaches a preset threshold value, executing an audit interception operation and returning a right confirmation abnormity prompt to the user, and requesting to supplement ownership proof; and embedding a multi-modal fusion watermark into the data assets passing the auditing, and generating a cross-domain verifiable right confirmation voucher. According to the method, the data features are extracted through multi-modal preprocessing to generate the unique data fingerprint, the ownership registration state of the data fingerprint is inquired in combination with the block chain technology, the ownership-confirmed and unconfirmed data fragments are accurately identified, and the problem that the data ownership is difficult to accurately and efficiently identify is solved.

The invention discloses a comprehensive pipe gallery operation and maintenance system and method based on an AI large model, and relates to the technical field of public safety operation and maintenance and smart city management. The method comprises the following steps: acquiring continuous time sequence data generated by a distributed environment sensor in a pipe gallery and a monitoring image sequence of a corresponding time period; calculating a change feature vector of the continuous time series data in a preset sliding time window to form a trend vector, and mapping the trend vector into a textualized trend abstract; inputting the textualized trend abstract into a pre-trained language model, and outputting a first anomaly inference result with confidence; performing differential calculation on the monitoring image sequence of the same time window to obtain a differential image sequence, and inputting the obtained differential image sequence into a pre-trained visual semantization model to generate visual change description; according to the method, the core problem that the slowly-changing or distributed small-amplitude abnormity is difficult to find in time due to the lack of cross-time and cross-space fusion in the prior art is solved.

The invention discloses a dynamic planning method and system for smart city traffic, and relates to the technical field of smart cities and intelligent traffic, and the method comprises the steps: collecting multi-source traffic data, and carrying out the fusion preprocessing; constructing a dynamic weight model to calculate a priority weight; a signal lamp timing scheme is generated based on the weight and is adjusted through a rolling optimization mechanism; outputting an optimization strategy by combining historical and real-time feedback evaluation effects; and a manager is notified through the acousto-optic prompt device and a report is generated. According to the invention, through the dynamic weight model and the rolling optimization mechanism, the overall delay time of the intersection is reduced, the traffic capacity of the traffic flow is improved, the Kalman filter is introduced to fuse multi-source data, and the data processing precision is improved; the LSTM network is adopted to predict the future traffic flow, a scientific basis is provided for a signal lamp timing scheme, and the workload of traffic management personnel is reduced through the acousto-optic prompt device and the automatic report generation module.

The invention relates to the field of smart city maintenance, and discloses a smart city-based guardrail obstacle avoidance maintenance method and system, and the method comprises the steps: carrying out the dynamic recognition of a guardrail maintenance robot, a road cleaning robot and a guardrail target structure in a shared road shoulder operation region through a multi-source heterogeneous sensing unit; acquiring a guardrail state data stream sequence subjected to time synchronization calibration, constructing a multi-dimensional time sequence feature vector of an equipment cooperative detection state, and detecting a high-risk precision attenuation candidate event by identifying a deviation degree between track distribution abnormality and a predicted detection time sequence; dividing a guardrail detection section and an influence range corresponding to the high-risk precision attenuation candidate event as candidate intervention areas, constructing a local collaborative detection map, performing intervention judgment on a collaborative detection evolution path, and generating a dynamic obstacle avoidance adjustment and detection precision enhancement result; the method has the advantage of improving the detection accuracy.

The invention provides an SDN (Software Defined Network) dynamic control system based on a cloud native architecture, and belongs to the technical field of SDN and cloud native. The system adopts a three-layer decoupling architecture of a service layer, a control layer and a data layer, wherein the service layer consists of a core network, a cloud platform and edge nodes and is used for issuing service requirements and executing a joint resource scheduling strategy; the control layer comprises four core modules, namely a micro-servitization SDN control module, a cross-domain collaborative engine module, an end-to-end service quality guarantee module and an edge collaborative optimization module, and is responsible for unified analysis, decision and scheduling to realize dynamic updating of a network strategy and multi-cluster collaborative optimization; the data layer is composed of an SDN switch and a router, and is used for executing a flow table rule and collecting network situation data. Through deep fusion of the SDN and the cloud native technology, the problems of upgrade interruption, low cross-domain collaboration efficiency and edge resource limitation of a traditional network architecture are solved, and the method can be widely applied to hybrid cloud management, industrial Internet of Things and intelligent city network scenes.

The invention relates to the technical field of equipment management, and discloses a data security evidence storage method and system for smart city construction. The method comprises the following steps: acquiring operation state data of target equipment and performing encryption preprocessing to construct a security data set, further identifying data exception and determining a potential emergency list with relevance; equipment is intelligently grouped according to event priorities, a resource allocation scheme is generated and dynamically adjusted, and when the scheme does not meet the evidence storage requirement, standby resources are automatically obtained, and configuration is expanded; calculating and verifying a data storage path with the shortest delay by using a path selection model fusing bandwidth and priority constraints on the basis of an expansion scheme; and finally, carrying out full-link log recording on the evidence storage process along the path, implementing integrity verification based on a cryptographic hash chain, and carrying out closed-loop evaluation on a resource adjustment effect based on a verification result to generate a confirmation result. According to the invention, the timeliness and reliability of equipment data evidence storage in a complex environment of a smart city are improved.

The invention discloses a smart city public facility supervision method based on the Internet of Things, and the method comprises the steps: carrying out the multispectral collection and space marking of a surface block of a to-be-detected circuit board, and achieving the standardized preprocessing and physical feature extraction of multi-label high-dimensional pixel data; according to the method, material spectral features and surface texture data are combined, a clustering analysis and supervision discrimination model is used for accurately discriminating the type of a block material, material attributes and multi-spectral features are fused, and pixel-level thickness prediction and dynamic adaptive correction are realized through a machine learning model. The system can automatically monitor drifting of the material and the mapping relation, and performs real-time optimization on the mapping model based on periodic calibration data.

The invention discloses a comprehensive quality evaluation method and system for Beidou deformation monitoring observation data in a complex environment, and relates to the technical field of satellite navigation positioning, and the method comprises the steps: carrying out the comprehensive evaluation of an elevating angle, a signal-to-noise ratio, an epoch difference and an environment model of the observation data of a Beidou single station, and determining the initial quality of the data; based on the double-difference observation values among the multiple stations, the data quality evaluation precision is further improved by combining a double-difference ambiguity resolving method; according to the method, ionosphere delay, three-difference errors and inter-epoch difference errors are used for data fusion, a more accurate quality evaluation result is obtained, and finally, the data are optimized through comprehensive analysis based on the quality evaluation results of various types of data so as to ensure accurate positioning and deformation monitoring. The technology aims at improving the accuracy and reliability of data quality evaluation by comprehensively analyzing influence factors of Beidou observation data in a complex environment, and provides support for geological disaster monitoring, smart city construction and other high-precision application scenes.

The invention relates to the technical field of Internet of Things, intelligent monitoring and sensor fusion, and discloses an intelligent multi-sensor data fusion processing system and method, and the system comprises a data collection preprocessing module, a feature and confidence evaluation module, a core data fusion module, a real-time decision early warning module and a performance feedback optimization module. The system preprocesses data through adaptive Kalman filtering, dynamically calculates the confidence coefficient of each sensor by using error entropy, further performs multi-stage data fusion by using weighted fusion and a D-S evidence theory, and finally outputs accurate state estimation and intelligent decision. Meanwhile, internal parameters are adaptively optimized through a closed-loop feedback mechanism. The system effectively solves the problems of data isolation, low precision, slow response and poor adaptability of a traditional sensor system, remarkably improves the precision, reliability, real-time performance and self-adaptability of Internet of Things monitoring, and is suitable for industrial equipment monitoring, smart cities and other scenes.

The invention relates to the technical field of heterogeneous data fusion processing, in particular to a heterogeneous data fusion processing platform for multi-industry information system integration, which comprises an industry knowledge graph module for acquiring business data of each industry, extracting a core entity relationship of each industry and constructing a field knowledge graph of at least two industries; when the system is used, the limitation of fixed weighted average is broken through by constructing a lightweight domain knowledge graph for each industry, extracting a core entity relationship and dynamically adjusting the fusion weight of each data feature, so that the purpose of dynamically adapting to the semantic feature difference of different industries is achieved; the method can be applied to cross-department data fusion of smart cities conveniently, semantic conflict intensity is accurately quantified by using a dual-channel neural network, adaptive strategy grading processing is combined, efficient resolution is performed through unit conversion during low conflicts, semantic integrity is guaranteed by using field isolation fusion during high conflicts, and conflict resolution efficiency and accuracy can be improved.

The invention relates to the technical field of heterogeneous data analysis, and discloses a data joint debugging method and system based on a heterogeneous monitoring system, and the method comprises the steps: obtaining the original data of each data source in the heterogeneous monitoring system; each data source is used as a node in the graph, edges among the nodes represent association relationships among different data sources, and a knowledge graph of the heterogeneous monitoring system is constructed; by analyzing the dependency relationship between the edges of the nodes and adopting a graph time sequence conjoint analysis method, carrying out delay correlation analysis on the feature data of each node, so that the original data of each node is correlated; and triggering a joint debugging mechanism according to the attention weight and the data feature of the node to complete multi-source data collaborative joint debugging of the heterogeneous monitoring system. The time sequence consistency of multi-source monitoring data, the timeliness of abnormal response and the intelligent level of a joint debugging strategy are remarkably improved, and the method has good adaptability and expandability and can be widely applied to complex monitoring scenes such as industrial Internet of Things and smart cities.

The invention discloses an urban communication abnormity monitoring and early warning system based on multi-modal data fusion, and the system comprises a multi-modal data collection module which is used for collecting multi-modal data; the data preprocessing and aligning module is used for preprocessing the multi-modal data; the multi-scale double-tower representation modeling module is used for constructing a global embedded vector sequence and a local embedded vector sequence; the nonlinear contrast mapping module is used for constructing a projection vector for contrast learning; the abnormal score generation module is used for generating an abnormal probability score; the abnormity positioning and influence evaluation module is used for positioning a link, a node and a transmission area where communication abnormity occurs; and the graded early warning and linkage control module is used for triggering a multi-stage early warning mechanism. According to the invention, high-precision detection, positioning and early warning of urban communication abnormal events are realized, and the method is suitable for real-time monitoring and emergency response scenes of communication networks in smart cities.

The invention discloses a smart city-oriented cloud resource adaptive scheduling method and system, and particularly relates to the technical field of cloud resource adaptive scheduling, and the method comprises the steps: precisely recognizing a high-risk task through calculating a task mobility misjudgment coefficient, and marking the high-risk task for deep risk control; calculating a node resource pseudo available coefficient for candidate nodes to be migrated by the high-risk task; a strategy degradation risk analysis model is constructed based on the coefficients to quantify and output a global strategy degradation trend, a'task misplacement-resource misplacement 'closed-loop fault mode is effectively revealed, and a quantifiable basis is provided for risk early warning and strategy correction; and on the basis of the evaluated high degradation risk degree, constructing a scheduling objective function to realize real-time optimization of task-node matching.

The invention belongs to the technical field of data security management, and discloses a smart city data processing method and platform based on artificial intelligence, and a storage medium. Comprising the following steps: monitoring real-time data between network entities in real time to obtain a real-time data stream; constructing a dynamic data blood relationship map by analyzing an instantaneous behavior mode and a content derivation relationship of the real-time data stream; constructing a node symbiont by analyzing the data difference degree between different nodes in the dynamic data blood relationship map; calculating an interaction risk of the node symbiont and an external entity, and constructing a minimum access control rule according to the interaction risk; and constructing a life cycle management model, dynamically managing an access control strategy, and managing and controlling real-time data. According to the method, the optimization effect from passive response to active perception is realized on the data stream security management level, the collaborative effect of the authority structure and system resource allocation is improved, and the intelligence and efficiency of the overall data security management are improved.

The embodiment of the invention provides a smart city fire-fighting automatic fire extinguishing system and method based on an Internet of Things large model and a medium, and belongs to the technical field of city fire fighting. The system comprises an emergency supervision management platform and an emergency supervision sensing network platform, wherein the emergency supervision management platform is configured to obtain emergency monitoring data; based on the emergency monitoring data and the regional feature data, determining emergency prevention data of the prevention region; determining emergency control parameters based on the emergency prevention data; based on the emergency control parameters, fire extinguishing control signals are sent to a fire extinguishing device installed in the prevention area through the emergency supervision sensing network platform, so that the fire extinguishing device is driven to execute emergency fire extinguishing according to the emergency control parameters. The system can improve disaster response speed and resource utilization efficiency, so that fire extinguishing efficiency is improved, and disaster loss is reduced.

The invention discloses a distributed MIMO (Multiple Input Multiple Output) integrated system and method based on a reconfigurable notch chaotic optoelectronic oscillator, belongs to the field of microwave photons, and solves the problems of spectrum resource conflict, difficulty in multi-node synchronization, discrete inductance functions and the like. According to the system, a plurality of independent chaotic OEOs are combined with an optical frequency comb to drive an FIR filtering effect, MIMO chaotic signals with reconfigurable notch frequency points and quantity are generated, and dynamic allocation of spectrum resources is realized. The notch frequency band is used as a communication channel embedded modulation signal, and supports multi-spectrum sharing with 2.4 G, 5G, Wi-Fi and the like; a non-notch frequency band is reserved as a radar signal, and the range resolution is improved by using the broadband characteristic of the chaotic waveform. The star topology optical network expands the coverage area of a distributed system, and a centralized processing architecture reduces the complexity of multi-node collaboration. The invention provides an integrated solution of high-precision environment perception and high-reliability communication for smart cities, Internet of Vehicles and the like, and is suitable for dense urban areas with resource shortage and dynamic multi-service environments.