2627 results about "Geo information" patented technology

The invention belongs to the technical field of earthquake disaster scene recognition, and discloses an earthquake disaster scene recognition method based on deep learning. The method comprises the following specific steps: S1, data acquisition and preprocessing; S1.1, multi-source heterogeneous data acquisition and establishment of a comprehensive database containing seismic waveform data, surface deformation data, building structure data, geographic information data and historical disaster record data; through fusion of a 3D convolutional network, a graph attention mechanism, a space-time LSTM and an adaptive cross-modal attention fusion technology, combined modeling of a seismic waveform space-time evolution law, an earth surface deformation space distribution characteristic, a building group topology vulnerability and disaster chain time sequence association is realized, the characterization capability of a complex nonlinear disaster mode is effectively improved, and the method has the advantages of high adaptability and high reliability. And disaster assessment response time is shortened to a sub-second level through mixed precision quantification and edge computing deployment, and high recognition accuracy is still kept in a scene with strong noise and data missing in combination with a multi-task classifier and a physical constraint verification mechanism.

The invention discloses a geographic information big data-oriented multi-source data processing system, and relates to the technical field of data acquisition and sensors, and the system comprises a data acquisition module which accesses a remote sensing satellite, an unmanned aerial vehicle, an Internet of Things sensor and social media in real time through a multi-source heterogeneous interface, and carries out the adaptive analysis of a data format and metadata marking; the distributed storage module is used for performing partition storage on the geographic information data based on a space-time database and an object storage architecture, and establishing dynamic space-time index and version control; the data fusion module is used for realizing coordinate system conversion, time sequence calibration and semantic knowledge graph matching based on a multi-source data alignment method of dynamic weight distribution; and an intelligent analysis module and a security control module. According to the method, core pain points such as data splitting, low storage efficiency, extensive analysis and compliance risks in the geographic information field are systematically solved, and a full-stack type technical base is provided for scenes such as smart cities, emergency disaster relief and environment monitoring.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention discloses a thunder and lightning monitoring and early warning method and system based on multi-source data fusion, and relates to the technical field of thunder and lightning early warning, and the method comprises the steps: extracting electric field time domain and frequency domain features, magnetic field change features, lightning activity modes and meteorological change features through obtaining atmospheric electric field, magnetic field, lightning activity and meteorological environment data in real time; and constructing multi-source feature data. A time sequence analysis and Bayesian fusion technology is adopted to calculate a correlation weight between data sources, and a fusion feature vector is generated. And establishing a weighted regression model based on the vector, calculating thunder and lightning occurrence probability through dynamic weight distribution, and generating a risk distribution map in combination with geographic information. The method has a closed-loop feedback optimization mechanism, model parameters and weights can be adaptively adjusted according to prediction errors and early warning accuracy, the accuracy, timeliness and environmental adaptability of lightning early warning are improved, and the method is widely applied to the fields of electric power, aviation, buildings and the like.

The invention discloses a data security dynamic evaluation system and a protection method, belongs to the technical field of information security, and is used for solving the problem of terminal equipment access control and behavior risk dynamic collaborative protection. The method comprises the following steps: generating fingerprints through terminal equipment features, verifying authorization, inputting access feature slices into a time sequence model by authorized terminal equipment, generating a trust score based on cosine similarity of a behavior sequence and a prediction sequence, and constructing a Markov model to dynamically select an authentication mode according to the trust score; calculating a risk index by combining network and geographic information, and distributing the risk index to a real or virtual environment; and the unauthorized terminal equipment distributes the virtual environment after registration. Differentiated monitoring is carried out, a time sequence library is established in the virtual environment, and doubt scores are generated through sequence matching; the state deviation of the real environment is calculated through a hidden Markov model, and the risk score is generated by fusing the multi-dimensional features. And based on the result management authority, the suspicion terminal equipment isolates and shrinks the authority, the compliance terminal equipment authenticates and migrates, and the access is regulated and controlled according to the minimum privilege principle and the time window mechanism.

The invention discloses a digital twin substation multi-source data visualization decision-making method, which comprises the following steps: constructing a three-dimensional holographic digital twin model of substation equipment, integrating BIM data and GIS geographic information, and carrying out lightweight processing; multi-source heterogeneous data, including equipment state data, environment sensing data and video monitoring data, of the transformer substation are collected in real time. Through the technologies of multi-source data fusion, dynamic digital twin modeling, AI aid decision making and the like, holographic data integration is realized, data islands are broken, and unified analysis and visualization of multi-dimensional data such as equipment states, environments, videos and the like are realized. And real-time dynamic mapping is carried out: high-precision digital twin bodies are constructed, the operation state of a physical substation is synchronized, and the fault positioning and prediction capability is improved. And intelligent decision support: in combination with machine learning and an expert knowledge base, fault root cause analysis, risk assessment and optimization operation and maintenance schemes are provided, and manual intervention requirements are reduced.

The invention discloses a power construction unmanned aerial vehicle path planning method and system, and relates to the technical field of space calculation, and the method comprises the following steps: determining a target region needing power construction and a preset construction point coordinate; obtaining a topographic map of the target area based on the geographic information database, constructing a three-dimensional map model, and marking the preset construction point coordinates; generating an initial construction path of the unmanned aerial vehicle; the method comprises the following steps: acquiring meteorological data in real time through meteorological data of a meteorological station in a target area, establishing a meteorological obstacle three-dimensional model, and dividing a preset radius threshold with a meteorological obstacle as a circle center into non-flying areas according to the severity level of the meteorological obstacle; adjusting the initial construction path based on the non-flying area; the cruise height is adjusted according to the meteorological data and topographic relief, a relief tracking path is generated, and the constant relative height is kept; and combining the battery capacity, the flight speed and the task priority, dynamically distributing inspection road sections, and generating a corresponding power construction unmanned aerial vehicle flight path.

The invention discloses a pollution space analysis method and system based on atmospheric pollution concentration data, and particularly relates to the technical field of data analysis. A high-precision pollutant concentration spatial distribution model is constructed by combining pollutant diffusion characteristics and geographic information, and regional pollution differences are revealed; identifying a pollution source emission mode and analyzing the evolution trends of pollutants in different time and space ranges by matching spatial and temporal change analysis with a pollution source emission list; the influence of real-time meteorological data on pollution diffusion is analyzed, and a pollutant spatial distribution model is adjusted, so that the prediction precision is improved; pollution concentration distribution, pollution source emission and meteorological influence are combined, pollution hot spot areas are accurately identified, future pollution evolution conditions are analyzed and predicted based on historical trends, scientific and reasonable pollution treatment suggestions are provided, the limitation of traditional interpolation and statistical regression models is broken through, dynamic and accurate modeling of pollutant concentration is achieved, and the method is suitable for large-scale popularization and application. And the scientificity of pollution source tracing and treatment decision making is improved.

The invention relates to the technical field of data processing, and discloses an automatic noise monitoring system and method based on multi-sensor data fusion. According to the system, a calibration module carries out time synchronization processing on noise data collected by multiple sensors; the extraction module adopts a singular value decomposition algorithm to extract frequency domain and time domain features; the separation module analyzes and separates traffic, construction and industrial noise sources through attention independent components; the construction module generates noise space propagation characteristics in combination with geographic information data; the classification module identifies the type of a noise source through a space-time convolutional network, locates coordinates and allocates responsibility weight. The technical problem that the existing noise monitoring technology cannot realize multi-source noise intelligent identification and pollution source accurate traceability is solved.

The invention relates to the technical field of unmanned aerial vehicle-unmanned ship data fusion, and discloses an unmanned aerial vehicle-unmanned ship multi-source heterogeneous big data fusion system and method, and the system comprises a task scheduling module which is used for executing the path planning and task distribution of operation tasks; the control and data acquisition module is used for controlling the unmanned aerial vehicle and the unmanned ship to execute a synchronous measurement task; the space-time reference unification module is used for performing dynamic synchronization and consistency alignment processing; the multi-source data fusion module is used for carrying out fusion processing on the aligned heterogeneous sensing information data; the self-adaptive robust enhancement module is used for monitoring the noise level and the change condition of the signal-to-noise ratio of the sensor in the fusion process; and the intelligent analysis and visualization module is used for executing environment modeling, target identification and geographic information expression processing. According to the method, the fusion weight self-adjusting mechanism driven by the maximum entropy is introduced, so that the internal structure can be intelligently adjusted when the data quality fluctuates. According to the technical effect, the target that the weight is dynamically adjusted along with signal noise is achieved.

The invention provides an unmanned aerial vehicle inspection route determination method and device, equipment and a medium. The method for determining the inspection route of the unmanned aerial vehicle comprises the steps that a, basic geographic information, environment state data and task requirements of a target area are obtained, the geographic information of the target area is obtained through remote sensing data, the environment state data is collected in real time through a sensor, and the task requirements comprise a starting point and an ending point of an inspection route and a specific inspection task; according to the unmanned aerial vehicle inspection route determination method and device, the equipment and the medium, by introducing the environmental factor correction model and the adaptive optimization algorithm, the problem that dynamic environmental changes are not fully considered in traditional route planning is solved. The environment factor correction model can pre-process the path in real time according to external factors such as wind speed, air temperature, atmospheric pressure and the like, so that a more accurate environment assessment map is generated, and more comprehensive reference data is provided for path planning.

The invention discloses a group aircraft collaborative perception decision control method and system in a complex high-dynamic environment, and relates to the technical field of cluster aircrafts, and the method comprises the steps: obtaining the performance parameters of a target group aircraft, building a task scene model in combination with the geographic information of a target task area, and building a target optimization function. Iteratively optimizing an aircraft and task allocation scheme through a graph neural network algorithm, outputting a task allocation result of each aircraft, and then according to a flight path constraint condition, analyzing a matching degree between a flight cost generated in a flight path and a corresponding task demand; and generating a globally optimal task allocation and flight path cooperation scheme. Task distribution and path planning are dynamically adjusted by sensing task requirements and environment changes in real time and combining performance parameters of all aircrafts, and compared with a traditional static method, the method can quickly respond to dynamic changes of task scenes, and task distribution reasonability and path planning effectiveness are ensured.

The invention discloses a method and system for intelligently constructing a two-dimensional hydrodynamic model based on a large language model, and relates to the technical field of artificial intelligence and hydraulic numerical simulation, and the system comprises an intelligent region block division module which is used for analyzing geographic information data through the large language model LLM, recognizing topographic features and engineering facilities, and dividing region blocks; the self-adaptive grid generation module is used for setting different grid types and dynamically setting grid density according to different partitions; the terrain interpolation optimization module is used for selecting an interpolation algorithm to calculate a grid unit terrain and solve an optimal grid terrain based on a water level-reservoir capacity relationship verification result, and checking and marking an elevation abnormal mutation grid unit; and the two-dimensional hydrodynamic model construction module is used for outputting the grid and the time sequence data to a configuration file according to the format requirement of the target two-dimensional hydrodynamic model, and constructing the two-dimensional hydrodynamic model. According to the method, the problems of dependence on artificial experience, long time consumption of grid division and tedious adjustment are effectively solved, and integrated intelligent modeling is realized.

The invention discloses an urban flood toughness evaluation method, apparatus and device, a medium and a computer program product. The method comprises the steps of obtaining hydro meteorological data of a to-be-evaluated region and geographic information data of an urban spatial form element layer; setting boundary conditions of a plurality of flood scenes according to the hydrometeorological data, constructing a multi-scene urban flood disaster simulation model in combination with geographic information data, and respectively generating flood inundation depth data of urban spatial form element layers of the plurality of flood scenes; and calculating a first toughness index, a second toughness index and a third toughness index of each flood scene so as to calculate a flood toughness comprehensive evaluation result of each flood scene of the to-be-evaluated region. According to the method, an evaluation system of multiple composite flood disaster scenes is established, different disaster factors and interaction thereof are comprehensively considered, and the effect of key space parameters in urban flood toughness is fully considered, so that the capability of coping with flood disasters in cities can be more comprehensively and accurately evaluated.

The invention discloses an urban road mobile source intelligent monitoring method and system based on multi-source data coupling, and relates to the technical field of environment monitoring and intelligent traffic. Utilizing a machine learning algorithm to construct a pollutant emission, carbon emission and energy consumption prediction model; acquiring vehicle inventory data in the city, and calculating the pollutant emission, energy consumption and carbon emission of the whole city; the method comprises the following steps of: constructing a dynamic distribution diagram of automobile emission in a city by using real-time position data of vehicles and urban road network information, introducing an atmospheric diffusion model, simulating pollutant migration by combining urban geographic information and environmental data, comparing and verifying a simulated migration result with actually measured data of a national control site, and optimizing parameters of the atmospheric diffusion model; and storing the result into a real-time database, and displaying the pollutant distribution, emission, energy consumption and carbon emission of the urban road network in real time through a visual interface. According to the invention, the temporal-spatial resolution and prediction precision of data are effectively improved.

The invention relates to the technical field of water and fertilizer control, in particular to a water and fertilizer integrated control system and method based on the Internet of Things. The method comprises the following steps of obtaining environment measured data, historical environment data and regional geographic information of a farmland region, performing spatial modeling and time sequence learning on the farmland region, and constructing a farmland digital twinborn model; simulating the state of an area without sensors by using a farmland digital twinborn model to obtain virtual sensing node data; collecting multispectral image data of crops, and extracting physiological feature data of the crops; and constructing a crop physiological state model by utilizing the environment measured data, the virtual sensing node data and the crop physiological feature data, and generating crop physiological state evaluation data. Through multi-source data fusion and three-dimensional visualization integration, data-driven accurate regulation and control and whole-process intelligent management of the water and fertilizer integrated system are realized, and the intelligent level of irrigation and fertilization and the resource utilization efficiency are comprehensively improved.

The invention belongs to the technical field of disaster early warning systems, and particularly relates to a dynamic early warning method and system for urban inland inundation and secondary disasters based on multi-source data fusion, and the method comprises a data collection module which is used for collecting the related data of urban inland inundation and secondary disasters from multiple sources in real time, the data includes but is not limited to meteorological data, rainfall data, ground water level data, urban drainage system state data, historical waterlogging event data and geographic information data; the data preprocessing module is used for carrying out cleaning, denoising, missing value filling and normalization processing on the data; the data fusion module is used for fusing data from different data sources and analyzing the data through a multi-source data fusion algorithm to generate a comprehensive urban inland inundation risk assessment model; through multi-source data fusion, accurate prediction and precision of urban inland inundation and secondary disaster occurrence are improved, so that the system can identify disaster risks earlier and more accurately.

The invention relates to the technical field of map data acquisition, in particular to a surveying and mapping geographic information map data acquisition method and system, and the method comprises the steps: building a space-time reference network: generating an underground-earth surface space-time reference network for connecting the underground with the earth surface through acousto-optic joint calibration; generating a multi-source correction parameter: fusing the underground inertial navigation data, the surface atmospheric refraction parameter and the equipment temperature drift coefficient, and calculating a multi-source correction parameter set for coordinate system conversion and error compensation; space-time evolution element collection: calibrating a mobile collection device in real time by using the multi-source correction parameter set, synchronously obtaining underground pipeline point cloud, an earth surface building image and a construction machinery track, and generating a space-time evolution element body with a space-time version label; and dynamic topology evolution modeling: analyzing an element change sequence in the spatio-temporal evolution element body, and constructing a dynamic evolution topology model. According to the method, differential analysis and a conflict backtracking strategy are combined, so that the adaptive updating and decision support capability of the map data in a construction disturbance scene is improved.

The invention belongs to the technical field of water conservancy project monitoring, and discloses a flood storage and detention area risk intelligent sensing and multi-stage progressive early warning system, which comprises a flood storage and detention area sensing module for collecting multi-source heterogeneous data covering a flood storage and detention area, constructing a unified space-time coding framework, mapping the multi-source heterogeneous data to a standardized geographic information grid, and performing early warning on the risk of the flood storage and detention area. A confidence regulation and control mechanism is adopted to form a dynamic storage and stagnation pressure map; the linkage instability propagation module is used for constructing a flood diversion driving situation flow field based on the dynamic storage stagnation pressure map, continuously tracking topological disturbance characteristics in the flood diversion driving situation flow field and identifying a linkage instability propagation chain and an instability propagation path; the risk phase change identification module dynamically tracks the derivative of the instability propagation tension gradient along with time according to the instability propagation chain and the instability propagation path, and combines the gravity center migration trend and the edge activation frequency in the flood diversion driving situation flow field; and a powerful support is provided for scientific decision-making in a complex flood environment.

The invention relates to the technical field of power system management, in particular to a Beidou-based power grid emergency wireless command system, which comprises a Beidou positioning communication terminal, a multi-source data fusion terminal, an intelligent decision-making terminal, a heterogeneous communication terminal and a visual command terminal. The Beidou positioning communication module accurately obtains the position of the power equipment and transmits an encryption instruction; the multi-source data fusion module integrates power, weather and geographic information, and provides support for decision making through data cleaning and space-time correlation analysis; the intelligent decision-making module generates a multi-target optimization decision by applying an improved multi-target genetic algorithm based on a digital twinborn dynamic plan library and a deep reinforcement learning algorithm; the heterogeneous communication module integrates various communication units, realizes mode dynamic switching and bandwidth aggregation, and ensures stable communication. And the visual command terminal displays the power grid situation and efficiently issues an instruction by means of augmented reality and multi-level authority management. The power grid emergency response and command efficiency is greatly improved, and power supply stability is powerfully guaranteed.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an intelligent charging and endurance optimization method for an unmanned aerial vehicle nest, and the method comprises the steps: collecting flight, battery and environment data through an unmanned aerial vehicle, transmitting the data to a nest, and precisely evaluating the remaining endurance of the unmanned aerial vehicle through the nest in combination with historical tasks and geographic information; performing dynamic task planning based on reinforcement learning, and generating an optimal charging strategy by using a dynamic planning algorithm; by means of a multi-mode sensor and a self-adaptive charging control model, intelligent regulation and control of the charging process are achieved; and through solar auxiliary power supply and multi-machine cooperative charging, the endurance is enhanced. The problems of low charging efficiency, poor endurance management, insufficient system reliability and the like in a traditional method are effectively solved, the utilization rate of nest charging resources is remarkably improved, the service life of a battery is prolonged, the endurance of the unmanned aerial vehicle and the system stability are enhanced, and the method has important significance in promoting efficient application of the unmanned aerial vehicle in multiple fields.

The invention discloses a substation route inspection planning method based on deep learning. The method comprises the following steps: acquiring substation equipment state data and environment monitoring data; performing multi-dimensional feature analysis on the equipment state data based on a preset depth feature extraction model to generate an equipment health degree evaluation vector; based on the environment monitoring data and the equipment health degree evaluation vector, multiple candidate paths are generated through a reinforcement learning algorithm, an initial inspection path is obtained through screening in the multiple candidate paths, and the initial inspection path is dynamically optimized by fusing geographic information in real time; according to unmanned aerial vehicle positioning data and equipment state feedback, if it is detected that the health degree is lower than a threshold value or the environmental risk exceeds the limit, local path re-planning is triggered, and navigation to high-risk equipment is preferentially carried out. The method has the following advantages and effects that the intelligence, accuracy and safety of routing inspection path planning can be improved, and it is ensured that the unmanned aerial vehicle can efficiently and reliably execute the substation equipment routing inspection task.

The invention belongs to the technical field of geographic information data processing, and discloses a graph attention-based road slope surface displacement time sequence prediction method, which comprises the following steps of: obtaining surface displacement time sequence data of a plurality of monitoring stations and associated environmental influence data, and constructing a weighted adjacency matrix based on geographic positions of the monitoring stations, defining an initial spatial topological relation; carrying out feature fusion on the displacement data and the environment data, and constructing an attribute-enhanced feature matrix; respectively inputting the weighted adjacency matrix and the feature matrix into a graph convolutional network and a graph attention network for parallel processing; extracting structured spatial features by the GCN through a fixed topological structure, and generating a first feature representation; the GAT adaptively allocates a dynamic weight by using an attention mechanism, extracts a non-uniform spatial dependency feature, and generates a second feature representation; and fusing the two feature representations, inputting a time sequence modeling module to analyze time dependence, and finally outputting a surface displacement prediction result at a future moment. According to the method, the accuracy of surface displacement prediction is remarkably improved.

A water pollution risk early warning and tracing method based on multi-source data fusion belongs to the technical field of water pollution monitoring and early warning, and comprises the following steps: step 1, constructing a multi-source heterogeneous data acquisition network and realizing real-time data transmission; 2, performing multi-source data fusion and feature enhancement processing based on space-time reference; 3, constructing a water pollution risk dynamic early warning system based on a WOA-LSSVM model; fourthly, reverse positioning of the pollution source is completed on the basis of a CNN-GRU-SE Attention model; and 5, carrying out development and emergency response on a multi-dimensional visual decision support system, and positioning a pollution source. Multi-source information such as water quality sensor data, unmanned aerial vehicle image data and geographic information data is fused, and an intelligent monitoring network is constructed; through multi-source data fusion and an intelligent algorithm, water pollution risk early warning accuracy and traceability efficiency are effectively improved, and the method has the advantages of high response speed, wide monitoring range, accurate positioning and the like, can be widely applied to the fields of urban water supply, drainage basin management and the like, and meets the requirements of water environment safety guarantee.

The invention relates to the technical field of communication engineering, in particular to a communication optical cable line intelligent inspection fault point rapid positioning method and device, and the method comprises the steps: obtaining optical cable line state data, constructing an intelligent fault detection model, recognizing an abnormal signal through wavelet transform and a threshold determination method, training an LSTM neural network in combination with historical fault data, and pre-judging a fault type. When a fault is detected, an unmanned aerial vehicle inspection unit is triggered, an inspection path is planned based on an improved Dijkstra algorithm according to the fault type and suspected fault area geographic information, an optical cable line is scanned and detected through a laser radar, physical fault features are recognized through a YOLOv5 algorithm, and geographic coordinates of a fault point are calibrated in combination with an inertial navigation system; and fusing sensor data and a detection result, evaluating a fault confidence level by adopting a DS evidence theory, and generating an alarm work order. Therefore, the problems of long positioning time consumption, high omission ratio, difficulty in distinguishing fault types and the like in the prior art are solved.

The invention provides a ship trajectory prediction method based on a graph attention mechanism and an electronic chart, and relates to the technical field of intelligent ships. By fusing dynamic AIS trajectory data and static navigation channel geographic information, high-precision modeling and prediction of the future motion trajectory of the ship are realized. According to the method, a traditional time sequence-based trajectory modeling mode is expanded into multi-modal joint modeling, channel structure vector representation is introduced for the first time, structural information of a channel environment is extracted on the basis of a graph neural network, and deep fusion is performed on the structural information and historical trajectory data of a ship; and constructing a trajectory expression mode containing time, space and environment triplex semantics at the same time. In the aspect of model architecture, a graph attention mechanism is adopted to enhance the node representation capability in a channel sub-graph, and meanwhile, a time sequence modeling module is introduced to model a trajectory evolution rule, so that the prediction precision and generalization capability of the model in a complex water area environment are effectively improved, and high-quality modeling, interpretable prediction and intelligent support of the ship trajectory are realized.

The invention relates to the technical field of machine vision, and discloses a surveying and mapping geographic information analysis method and system based on machine vision and a medium. The method comprises the following steps: preprocessing multi-source remote sensing data to obtain a standardized surveying and mapping data set, and extracting ground feature edge and texture information to obtain a multi-scale spatial feature and a semantic segmentation result; carrying out multi-source data integration to obtain a unified geographic element representation diagram, and carrying out geographic element identification on the unified geographic element representation diagram to obtain a surveying and mapping geographic element set; performing Bayesian inference of spatial uncertainty quantization and parameter uncertainty quantization on the surveying and mapping geographic element set to obtain a comprehensive uncertainty evaluation result; and constructing vectorized geographic information based on the surveying and mapping geographic element set and the comprehensive uncertainty evaluation result. According to the method and the device, the edge information of the geographic elements is effectively reserved, the identification precision of the geographic elements is improved, the geographic information expression is more structured, and automatic updating is realized.

The invention discloses a fire risk accurate prediction method and system based on a multi-source data fusion knowledge graph, and belongs to the technical field of machine learning, and the method comprises the following steps: accessing a multi-source heterogeneous data set, integrating sensor data, geographic information, historical data and external data, and obtaining a multi-source heterogeneous data set; a full-size forest three-dimensional model is constructed by means of FDS and SolidWorks, combustion simulation is carried out, environmental parameters are set carefully to ensure that the result is accurate, and a foundation is laid for construction of a multi-source heterogeneous data set; the method comprises the steps of data acquisition, data preprocessing, noise data cleaning, space-time alignment and feature engineering, multi-source heterogeneous data acquisition and preprocessing are carried out, and forest fire related data covering structured, semi-structured and non-structured types are acquired from a multi-source heterogeneous data set. The accuracy and timeliness of fire early warning are remarkably improved, effective fusion of forest fire multi-source heterogeneous data can be achieved, and transparency and traceability in the data fusion process are ensured.

The invention discloses a lighting data management system and method for street lamp networking, relates to the field of lighting data management, and aims to solve the problem of waste of lighting resources. The technical scheme is characterized in that all street lamp nodes are networked according to physical positions, and a geographic information database is constructed; collecting motion data of a moving target and environment illumination intensity data; when the moving target is detected to be a pedestrian or a vehicle, switching the current street lamp node and the adjacent street lamp node to a normal mode; predicting the trajectory of the moving target, and calculating the number of street lamps needing to be turned on in advance according to the moving target speed and the safety response time; when the moving target approaches the intersection, the street lamp nodes of the intersection branches are converted from an energy-saving mode to a normal mode; and when the moving target leaves the detection range of the sensor, the street lamp nodes of the branch paths which are not actually passed are recovered to the energy-saving mode, so that on-demand illumination is achieved, and the effect of reducing the energy consumption of the street lamp is achieved.

The invention relates to the technical field of smart city data visualization, in particular to a visual smart city display method, which comprises the following steps: S1, collecting traffic flow data, energy consumption data, environment monitoring data and city three-dimensional geographic information data in real time; s2, constructing a reference three-dimensional model with a space coordinate system; s3, mapping the data into a dynamic data layer with a timestamp; s4, superposing the dynamic data layer to the reference three-dimensional model according to space-time coordinates to generate a fused dynamic scene; s5, receiving deduction parameters input by a user, and calculating a corresponding evolution path in real time; and S6, converting the evolution path into a dynamic visualization effect, and outputting the dynamic visualization effect to a display terminal. According to the invention, through integrated processing of multi-source dynamic data fusion, space-time path deduction and visual output, continuous expression and dynamic evolution display of the operation state of the smart city are realized.