3274 results about "Geo information" patented technology

The present application relates to the technical field of power digital twin, and discloses a digital twin system for a power grid. The system comprises: a data access module, which accesses multi-source heterogeneous data of a power grid; a modeling module, which is used for receiving the data output by the data access module and establishing digital twin models in multiple service scenes; a fusion module, which is used for performing association and fusion of the digital twin models in the multiple service scenes; a computing module, which is used for carrying out multi-scene collaborative simulation and derivation by using the digital twin models; and an optimization module, which is used for optimizing the digital twin models, wherein the multi-source heterogeneous data comprises real-time monitoring data, historical operation data, meteorological environment data, device model data, and geographic information data. The problem in the prior art of low fusion efficiency of multi-source heterogeneous data of a power grid is solved. A standardized data access interface is constructed, and association analysis, knowledge graph technology, etc. are applied, so that seamless integration of the multi-source heterogeneous data of the power grid is achieved, thereby improving the data fusion efficiency.

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 provides an AI algorithm-based scenic spot car intelligent path establishing and planning method and system, and relates to the technical field of path planning, and the method comprises the steps: collecting scenic spot road geographic information data, and constructing a weighted road network model; collecting real-time positioning of the trolley and sensor data; using the reinforcement learning model to generate a real-time optimal path planning strategy; calculating an initial recommendation path; road condition changes are monitored, and a path is dynamically adjusted; and outputting the updated optimal path scheme and carrying out navigation. According to the method, intelligent and real-time path planning of the vehicles in the scenic area can be realized.

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 relates to the technical field of environment monitoring, in particular to an underground water pollution real-time monitoring method and system, and the method comprises the following steps: analyzing the underground water flow direction and original pollution records in geographic information, combining the geologic structure and hydrological characteristics of a monitoring area, and through the water flow propagation characteristics; and marking the position of the pollution source and matching the pollution range to generate a matching result of the pollution source and the range. According to the invention, the underground water flow direction, the pollution record and the geological and hydrological characteristics are analyzed, the position and the range of a pollution source are accurately marked, the identification precision and the monitoring pertinence are improved, monitoring points are deployed at key positions, diffusion information is extracted in real time, an early warning threshold value is dynamically generated, early warning is issued in time, and emergency response is started, so that the pollution control efficiency is improved; rapid development of isolation and water quality treatment is ensured, closed-loop adjustment and optimization of monitoring parameters are realized, so that the method dynamically adapts to environmental changes, and comprehensive control of groundwater pollution is realized.

The invention relates to the technical field of data processing, and discloses a double-end fault positioning method based on signal enhancement, which is used for improving the efficiency of double-end fault positioning based on signal enhancement. The method comprises the following steps: acquiring and preprocessing electrical signals at two ends of a power transmission line to obtain preprocessed synchronous electrical signals; performing adaptive enhancement processing on the preprocessed synchronous electrical signal to obtain an enhanced fault feature signal; performing time-frequency analysis and feature extraction on the enhanced fault feature signal to obtain a fault feature vector and a key parameter; performing machine learning analysis on the fault feature vectors and the key parameters to obtain a fault type identification result and initial fault position estimation; performing multi-algorithm fusion analysis on the preliminary fault position estimation to obtain an accurate fault position coordinate; and performing geographic information mapping and field verification on the accurate fault position coordinates to obtain a fault positioning result and system optimization data.

The invention discloses a landslide monitoring and early warning method and system based on a digital twinborn technology, and belongs to the technical field of geological disaster monitoring and early warning, and the method specifically comprises the steps: collecting the geographic information data of a landslide in real time; constructing a dynamically updated multi-source spatial database; the method comprises the following steps: constructing a landslide simulation model, performing mesh generation on the landslide simulation model, introducing a stress field and seepage field numerical coupling algorithm on the basis of a numerical analysis method, establishing a landslide mass hydraulic coupling model, performing solution on the basis of the landslide mass hydraulic coupling model, and accurately analyzing the existing state of the landslide mass. Acquiring the safety coefficient of the landslide mass in real time by adopting a strength reduction method; and establishing a disaster analysis model, if the safety coefficient reaches a preset alarm threshold, deducing a landslide disaster forming range, calculating a disaster risk index, evaluating a risk level according to the disaster risk index, and if the risk level is higher than a preset level, carrying out early warning on the landslide disaster. According to the invention, accurate landslide monitoring and intelligent safety early warning are realized.

The invention relates to the technical field of geographic information surveying and mapping, in particular to a city-level surveying and mapping geographic information standard system fusion construction method and system, and the method comprises the following steps: obtaining surveying and mapping data space coordinates, extracting terrain boundaries, ground feature distribution and road intersection features, calculating boundary point spacing, and calling a database to divide a topological structure. And detecting precision, spatial offset and overlapping conditions, and generating a topological correlation result. According to the method, by deeply analyzing the space coordinates, the time sequence, the geographic boundary and the classification information of the surveying and mapping data, the organizability and logicality of the data are improved, the tracking and updating capabilities of the data are enhanced through the refinement of the topological structure, the dynamic monitoring of the ground feature change is realized, the regional conflict is reduced through the optimization of the boundary adjustment mechanism, and the accuracy and the reliability of the surveying and mapping data are improved. The accuracy and adaptability of boundary data are improved, the consistency and application value of surveying and mapping data are enhanced through accurate matching of classification information and adaptive weight adjustment of data fusion, and accurate fusion of the data in a multi-source heterogeneous environment is ensured.

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 relates to the field of business intelligence and artificial intelligence, and discloses an AI analysis technology-based store site selection accurate data comprehensive measurement and calculation system, which comprises a data acquisition module, a data preprocessing module, an AI analysis and optimization module, a financial prediction and risk assessment module and a report generation module. The data acquisition module acquires geographic information, human traffic, rent cost, competitor information and the like of candidate positions from a multi-dimensional data source; the data preprocessing module preprocesses the data; the AI analysis and optimization module generates a recommendation scheme of a candidate position by using multi-objective optimization, linear programming and a machine learning algorithm; the financial prediction and risk assessment module carries out financial income prediction and risk analysis on each position; and the report generation module displays the analysis result through a visual tool and generates a decision support report. According to the method, the optimal store site selection scheme can be dynamically and accurately recommended, and merchants are helped to make scientific decisions and adapt to market changes.

The invention relates to the technical field of data evaluation processing, in particular to a rural waterlogging area waterlogging risk grade evaluation and early warning method based on multi-source data. The method comprises the following steps: acquiring geographic information, hydrological and flood data and flood control and drainage engineering data of a rural waterlogging area, and performing multi-source data fusion to obtain rural flood multi-source data; and performing river flood overflow calculation on the rural flood multi-source data to obtain river flood overflow data. According to the invention, through a data processing technology, a mode identification technology and a deep learning technology, rural multi-source feature data is subjected to fusion analysis, and risk level evaluation of a waterlogging area and a waterlogging sheet in a rural area is realized; a flood real-time forecasting and early warning model is constructed, when rainstorm occurs or a meteorological department issues rainstorm early warning, the rural flood flooding range is quickly predicted, and a risk avoiding transfer route is planned.

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 land investigation data updating method, a land investigation data updating system, a product and a medium, and relates to the field of land investigation. Superposing vector boundary data of the preset survey range on the basic geographic information layer and importing historical land information in the preset survey range to obtain a comprehensive basic information layer; acquiring actual land types, actually measured areas and ownership condition information of various lands in a preset survey range; and comparing the actual land type, the actually measured area and the ownership condition information with the comprehensive basic information layer, identifying land utilization change data, generating a land survey data updating instruction according to the land utilization change data, and sending the land survey data updating instruction and the comprehensive basic information layer to a target client. By implementing the method, the land survey data updating efficiency can be improved.

The invention provides a distribution network line routing inspection path planning method based on a gas-electric hybrid unmanned aerial vehicle, which combines geographic information system data and power equipment information to construct a three-dimensional geographic information model, and determines a routing inspection sequence through a scientific and reasonable region division and scoring mechanism. Meanwhile, detailed path evaluation is carried out on the key inspection points and reachable inspection sub-paths of each sub-region, an optimal inspection sub-path is selected by adopting a comprehensive scoring model, and finally an optimal inspection total path passing through all the key inspection points is generated, so that the overall inspection time is greatly shortened, and the inspection efficiency is improved. According to the method, position coordinates and residual energy consumption information of the unmanned aerial vehicle are monitored in real time, so that continuity and safety of inspection work are ensured.

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 discloses an optical cable routing anti-intrusion method and system, and the method comprises the following steps: laying a sensor network at key nodes along an optical cable, monitoring the data of a surrounding environment in real time, constructing an environment reference model, and integrating the model to a GIS platform, thereby achieving the risk assessment of different regions. Different monitoring frequencies and sensitivities are set for a high-risk area and a low-risk area according to an evaluation result, an anomaly detection module compares real-time signal data with an environment reference model, high-credibility abnormal signals are screened out through self-adaptive threshold adjustment, and when a physical barrier module detects abnormal external force, the physical barrier module sends out the abnormal external force. Triggering the area and the adjacent area to enter a high-frequency monitoring mode to form a dense monitoring area, positioning an intrusion position through a sensor network and a GIS platform, generating a path prediction model, and dynamically adjusting the frequency, sensitivity and alarm threshold value of the monitoring area based on data analysis and GIS geographic information. The adaptability and the resource configuration efficiency of the system in different environments are 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 provides a remote identity recognition method and system based on unmanned aerial vehicle management and control requirements. Wherein the preparation state of the unmanned aerial vehicle is determined by generating an inquiry code, sending the inquiry code to all online unmanned aerial vehicles in a target airspace, and receiving an identity response signal comprising a unique identifier, a task brief description and a flight path. A multi-dimensional data fusion algorithm is combined with historical flight data, meteorological conditions and geographic information to predict an unmanned aerial vehicle behavior mode, and a behavior prediction report is formed. The block chain technology is used for cross verification, and the real identity and authorization state of the unmanned aerial vehicle are verified through the dual key encryption technology and the consensus algorithm. And according to a verification result, using machine learning to construct a temporary virtual fence and plan an optimized route, and automatically adjusting an air corridor to form a traffic management scheme. And finally, analyzing actual flight parameters by applying a self-adaptive control algorithm, and further optimizing route setting. According to the technical scheme provided by the embodiment of the invention, the safety and efficiency of air traffic management of the unmanned aerial vehicle are improved.

The invention discloses a project-level visualization display method and system for highway multi-source data fusion, and the method comprises the following steps: 1, constructing a multi-source heterogeneous data exchange and integration frame, and carrying out the seamless connection and mapping of a BIM model, GIS space geographic information and an oblique photography three-dimensional point cloud model; 2, formulating a standardized interface specification, and performing specification processing on data from different sources through a data analysis and conversion engine; 3, dynamically and synchronously updating the multi-source engineering data and the project progress in real time; 4, gathering engineering data to construct a digital twinborn model of the expressway project to realize centralized management, multi-dimensional analysis and dynamic presentation in a visual scene; 5, carding and extracting management business logic to construct a flexibly expanded professional application module so as to realize intelligent analysis of the engineering project; and step 6, performing visual immersive display on the multi-source data fusion project by adopting visual man-machine interaction. According to the invention, visual comprehensive management and traceability of each element of the project are realized.

The invention discloses a geographic scene parameterization three-dimensional modeling method and system driven by a large language model, a storage medium and a program product, belongs to the field of geographic information science, and solves the problems that a geographic 3D scene reconstruction method is time-consuming in the data acquisition and processing process, generated models and scenes lack semantic information and the like. The method comprises the following steps: constructing a progressive three-dimensional scene modeling knowledge graph of a geographic scene; a reasoning context screening mechanism based on spatial autocorrelation and information entropy is constructed based on the geographic entity space and the progressive three-dimensional scene modeling knowledge graph, and a prompt text used for LLMs reasoning is obtained; constructing an implicit association reasoning rule between entities guided by a thinking chain based on a prompt text of LLMs reasoning, and updating a progressive three-dimensional scene modeling knowledge graph; and performing parameterized geographic scene three-dimensional modeling based on a three-dimensional model generation method of a primitive model and modeling object semantics and the updated progressive three-dimensional scene modeling knowledge graph. The method is used for parameterized geographic scene three-dimensional modeling.

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 surveying and mapping geographic information data management method and system based on cloud computing, and relates to the field of geographic information, and the method comprises the steps: constructing a semantic segmentation model based on a convolutional neural network Deep Lab, and carrying out the feature extraction and semantic annotation of a surveying and mapping geographic data set through the semantic segmentation model, and obtaining a geographic element set; performing spatial division and indexing on the geographic element set by adopting an R tree spatial indexing method to obtain a hierarchically organized geographic element tree; constructing a graph neural network model based on a graph convolutional network GCN, and performing topological relation modeling on the geographic element tree by using the graph neural network model to obtain a time-space attribute graph containing geographic semantics and topological relations; and constructing a space-time prediction sequence model based on the space-time diagram neural network STGCN, and performing evolution trend prediction on the space-time attribute diagram by adopting the space-time prediction sequence model. Aiming at the problem that dynamic prediction of geographic elements is difficult to realize in the prior art, the spatial-temporal evolution trend of the geographic elements is predicted, and dynamic prediction is realized.

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 relates to the technical field of petroleum pipelines, in particular to a petroleum pipeline leakage detection method and system, and the method comprises the following steps: S1, data collection and preprocessing: collecting the detection data of a petroleum pipeline in real time through a plurality of sensors and geographic information collection equipment; s2, feature extraction: performing feature extraction on the preprocessed detection data by adopting a multi-scale feature extraction algorithm; s3, model construction: constructing a petroleum pipeline state recognition model; s4, real-time detection: inputting detection data acquired in real time into the petroleum pipeline state recognition model; s5, leakage path prediction: predicting the diffusion path and range after petroleum leakage in real time; and S6, leakage alarm: when leakage of the petroleum pipeline is detected, an alarm mechanism is triggered, and related personnel are notified to take emergency measures. According to the method, the diffusion path and range after leakage are accurately predicted, and a reliable basis is provided for emergency response.