371 results about "Emergency plan" patented technology

The invention discloses a community intelligent monitoring and emergency linkage method and system fusing BIM spatial semantics, and the method comprises the steps: constructing a BIM scene map, and obtaining the attributes and mutual relationships of components and spatial regions in a BIM model; mapping a dynamic target detected in video monitoring into the BIM model, and obtaining spatial semantic information of the dynamic target; based on BIM spatial semantic information of a dynamic target, a target-environment interaction graph is constructed, a graph neural network model is used for training and reasoning, and specific complex events related to spatial contexts are recognized; taking the BIM model as a space-time reference, fusing multi-source heterogeneous data, and reconstructing by adopting a graph-based event association algorithm to form a complete event chain containing an atomic event sequence and an association relationship; and when an emergency event or an event chain is detected to indicate an emergency state, combining BIM preset information and real-time sensor data, dynamically generating an optimal emergency plan, and performing visual commanding and dispatching through a BIM three-dimensional scene and augmented reality.

The invention relates to the technical field of dispatching and emergency response, in particular to a supply chain multi-node real-time collaborative dispatching and emergency response system and method, and the system comprises a distributed data collection module which is used for obtaining the inventory data, logistics state and equipment operation parameters of each node in real time; the digital twin modeling engine is used for constructing a dynamic virtual mapping model of the supply chain network; the collaborative decision center generates a multi-objective optimization scheduling scheme based on a reinforcement learning algorithm; the emergency response trigger is used for automatically starting a graded emergency plan through abnormal mode recognition; according to the method, second-level response is realized through millisecond-level data synchronization and edge calculation, so that decision timeliness is improved, cross-node cooperation efficiency is improved by adopting multi-agent game and federated learning, and the punctuality rate of orders and the toughness index of the network are improved through multi-target Pareto optimization on the premise of controllable cost.

The invention relates to the technical field of offshore area monitoring, in particular to an offshore area pollutant diffusion real-time tracking and traceability monitoring system. The method comprises the following steps: a data perception and fusion unit collects and preprocesses data, carries out space-time calibration and multi-source fusion, and outputs a standardized fusion space-time sequence; the pollutant diffusion simulation and prediction unit calculates a flow velocity field, simulates a diffusion process and predicts a trend; the pollutant traceability positioning unit tracks a pollution path and inverts pollution source parameters; the emergency decision support unit determines a risk level and generates an emergency plan. And through a space-time alignment algorithm and a multi-source fusion mechanism, the data consistency and reliability are improved, a high-quality data basis is provided for subsequent processing, and the monitoring accuracy is guaranteed.

The invention provides an intelligent early warning system fusing Internet of Things sensing data and a domain knowledge graph, aiming at the problems of data islands, high false alarm rate, response lag and the like of a traditional park early warning system, and is suitable for park safety prevention and control in industries such as chemical industry, logistics, manufacturing and the like. The knowledge graph is an ideal tool for modeling connection between objective objects in the real world, the data island problem can be effectively solved by constructing the knowledge graph oriented to the smart park safety management field and fusing an intelligent reasoning algorithm, and the accuracy and timeliness of park risk early warning are remarkably improved. Specifically, a whole set of pre-warning system is designed from bottom to top in three aspects of multi-modal knowledge graph modeling, a three-level pre-warning inference engine and a self-adaptive optimization mechanism, and the park pre-warning requirements which meet current intellectualization and manpower cost saving are constructed. The multi-modal knowledge graph relates to six types of ontology concepts, comprises different data types, and comprises an equipment topological relation, environmental parameter association, an emergency plan, risk analysis, attack behavior simulation and an asset attribute model. The third-level early warning reasoning comprises rule reasoning, sub-graph matching reasoning and link prediction reasoning. The self-adaptive optimization technology aims at constructing a feedback learning mechanism, incorporating each early warning processing result into a knowledge graph, and continuously optimizing the object relation weight. In an early warning analog simulation experiment, the scheme of the invention realizes the effects of reducing the false alarm rate by 42% and improving the emergency response speed by 60%, and the feasibility and effectiveness of the scheme are proved.

The invention provides a method and a system for intelligently generating and dynamically adjusting a public emergency plan, which are oriented to the field of public safety emergency. The method comprises the following steps: fusing multi-source heterogeneous data to construct an extensible domain knowledge graph, establishing a standardized emergency instruction library, and carrying out multi-dimensional information labeling; automatic extraction of disaster elements is realized based on an entity recognition model of deep learning; analyzing association rules among the emergency entities through a semantic relationship mining technology; key information such as a disaster chain and resource distribution is rapidly obtained by using a map reasoning mechanism; carrying out cross-department resource collaborative allocation by adopting a multi-objective optimization algorithm to generate an optimal disposal scheme; a high-dynamic adjustment mechanism is constructed, and an emergency plan is dynamically optimized based on situation evolution prediction and real-time monitoring data; and finally, automatic generation and versioning management of the plan are realized. Through multi-mechanism cooperation of knowledge modeling, intelligent element analysis, semantic reasoning, dynamic optimization and predictive adjustment, the emergency plan generation efficiency, situation adaptability and resource allocation rationality are remarkably improved, and support is provided for quick response and scientific decision under emergencies.

The invention relates to the field of bill auditing, and particularly provides a financial bill auditing decision-making method and system, a terminal and a medium, and the method comprises the steps: collecting multi-mode finance and tax data including texts, images and structured data, and carrying out the preprocessing and alignment; carrying out feature extraction on the preprocessed data by using a multi-modal large model, carrying out feature fusion by using a dynamic weight distribution algorithm based on the credibility of each modal, the service priority and historical feedback, and obtaining a risk probability through a risk identification neural network; the risk probability is compared with a preset threshold value and rule in an auditing rule base, automatic passing is triggered, after risk abnormity is recorded, passing is conducted, and auditing actions such as manual auditing or starting of a high-risk emergency plan are pushed; and finally, adjusting model weight parameters according to manual feedback information to realize system self-optimization. The auditing decision-making efficiency is improved, and the accuracy and the service adaptability are improved.

The invention discloses a hospital project intelligent management and collaborative operation optimization system based on an AI algorithm technology, and the system comprises a multi-modal data collection module which is used for obtaining hospital operation data, medical equipment data, patient vital sign data and medical staff operation records in real time; the data fusion engine, the dynamic resource scheduling module, the risk prediction unit and the collaborative work platform provide intelligent task distribution, conflict detection and emergency plan generation functions; the self-adaptive optimization controller is used for continuously updating system parameters through an online learning mechanism; the method breaks through the limitation of traditional single-mode data processing, constructs a cross-department data association model through a graph neural network, integrates heterogeneous data sources such as radiation images, inspection reports and equipment logs, realizes dynamic mapping of diagnosis and treatment paths and resource states, introduces a space-time attention mechanism, and improves the diagnosis and treatment efficiency. Equipment operation cycle characteristics and patient movement tracks are subjected to combined modeling, so that the sensing precision of the system is improved to a minute level.

The invention discloses an emergency plan generation method and device based on artificial intelligence, and a medium, belongs to the technical field of artificial intelligence, and is used for solving the technical problems of sensing one-sided lag, poor static plan adaptability, insufficient situation deduction and decision support and low whole process recovery efficiency caused by information islands in an existing emergency processing system. The method comprises the following steps: acquiring multi-source event situation data, identifying an emergency event type, and constructing a dynamic event situation map; matching a basic plan based on the dynamic event situation map, and generating a plurality of preliminary emergency plans based on a preset constraint condition; simulating and deducing each preliminary emergency plan in the digital twin scene model, and determining a comprehensive evaluation value of each preliminary emergency plan; determining an optimal emergency plan based on the comprehensive evaluation value, and sending the optimal emergency plan to an execution end; and capturing key data in the execution process of the optimal emergency plan, generating an event handling process report, and optimizing the optimal emergency plan based on the event handling process report.

The invention belongs to the technical field of extreme weather emergency management, and particularly relates to an extreme weather event prediction and emergency response system. The problems of multi-source data splitting, disaster chain prediction missing, static plan stiffness and lack of closed-loop evolution in the prior art are solved. A global disaster map is constructed through real-time fusion of multi-source data, a physical mechanism and machine learning are coupled to realize refined deduction of extreme weather and secondary disasters, a self-adaptive emergency scheme is generated based on a dynamic resource library and an optimization algorithm, and disaster feedback data is utilized to drive model and strategy iteration, so that the disaster situation feedback data can be used for driving the model and strategy iteration. And a'prediction-decision-feedback-evolution 'intelligent closed loop is formed. The method has the advantages that information islands are eliminated, the disaster chain modeling bottleneck is broken through, resource scheduling is dynamically optimized, a self-evolution mechanism is established, and the extreme weather response efficiency is improved.

The invention discloses a multi-parameter fusion pipe network overflow prediction system and method, relates to the technical field of pipe network overflow prediction, and aims at overflow risks caused by equipment faults, unstable communication and extreme weather in a combined drainage system. A system scheme integrating five steps of predictive maintenance, reliable real-time control, digital twinning check, layered emergency plan and cloud edge cooperation is provided; in the first step, equipment health is monitored and faults are predicted to ensure that key pump stations and gates are available; 2, generating a fault-tolerant scheduling instruction by adopting reliable optimization and multi-agent reinforcement learning; 3, pre-judging and correcting the deviation by utilizing digital twinning check before execution; 4, dealing with a single-point fault and communication paralysis by using a layered distributed emergency mechanism; 5, cross-regional parallel simulation and self-adaptive scheduling are realized through cloud edge collaboration and federated learning; overflow can be reduced, environmental pollution and waterlogging loss under the rainstorm condition can be reduced, and efficient collaborative management of the drainage system is achieved.

The invention discloses a dam leakage monitoring system and method based on optical fiber sensing and time-space fusion. The dam leakage monitoring system comprises an optical fiber sensing system, a data processing and analyzing system, a dam leakage data management system and a real-time monitoring platform. According to the invention, the distributed optical fiber realizes continuous strain and temperature monitoring of the whole length of a dam, a leakage area can be roughly positioned, an FBG sensor is deployed at a key position, local tiny deformation is accurately monitored, a cooperative mode is formed by combining wide-area screening of the distributed optical fiber and key diagnosis of the FBG, and the monitoring efficiency is improved; the all-fiber passive design is adopted, the environmental adaptability is high, the anti-interference performance and the long-term stability are high, frequent maintenance is not needed after the fiber is embedded into the structure, and the operation and maintenance cost is reduced; finally, the leakage risk is displayed through a trend curve of a visual data platform, alarm pushing and emergency plan functions are automatically triggered, the closed-loop time of monitoring, decision making and response is shortened, and the real-time performance and decision-making force are improved.

The invention belongs to the technical field of public safety, and particularly relates to a map-based focused person management and control large model visualization system and method, and the system comprises a multi-source data collection layer which is used for constructing a multi-modal data set comprising identities, tracks and biological characteristics; the large model analysis layer comprises a space-time trajectory prediction model, a risk integral calculation engine, a group event early warning model and data processing; the visual interaction layer dynamically renders personnel tracks, risk thermodynamic diagrams and police resource distribution based on a three-dimensional map engine, and supports hierarchical early warning display and emergency plan linkage; the monitoring and tracking layer is matched with the tracking and early warning layer, frequently-going / non-frequently-going areas are divided through an improved space-time ST-DBSCAN algorithm, dangerous behaviors of management and control personnel are monitored and tracked, and a differential early warning mechanism is triggered. The multi-modal data fusion technology is combined, real-time loading of ten thousand people-level track points is achieved, data collaborative training of public security organizations in multiple places is supported, and dangerous behaviors of management and control personnel can be monitored and tracked.

The invention discloses a flood control and emergency rescue plan generation method, which comprises the steps of S1, collecting flood control and emergency rescue original data, performing preprocessing and feature extraction, and labeling and confirming extracted feature data to serve as a training set; s2, performing LoRA fine tuning of a low-rank decomposition matrix on the basis of a pre-trained large language model, and performing large language model training operation through the training set; s3, constructing a flood control and emergency rescue field ontology model, learning distributed representation of the knowledge graph by adopting a graph neural network technology, and dynamically updating the knowledge graph and executing reasoning query according to real-time dangerous case information by establishing a dynamic reasoning mechanism; and S4, constructing a multi-modal information fusion framework which is used for carrying out information fusion processing on the knowledge graph reasoning result and then guiding the big language model to generate an emergency plan as a control signal. According to the method, the technical bottlenecks of low plan generation quality, difficulty in knowledge updating, insufficient multi-source information fusion and the like of a traditional method in a complex dangerous case scene are effectively solved.

The invention belongs to the technical field of gas monitoring, and discloses a limited space gas monitoring method, system and device based on the Internet of Things and a medium, and the method comprises the steps: obtaining gas sensing data transmitted by a pre-constructed gas sensing node network through an optimal transmission path; performing gas concentration prediction on the gas sensing data by using a long short-term memory neural network and a random forest algorithm, comparing a prediction result with a preset early warning threshold value, and triggering an alarm of a corresponding level; and determining a prediction error based on a prediction result, dynamically optimizing an early warning threshold based on the prediction error, and continuously optimizing the plan through a closed-loop feedback mechanism. By introducing the gas monitoring sensor, the wireless Internet of Things ad hoc network technology, the edge computing and the cloud collaborative architecture, the toxic gas real-time monitoring with high sensitivity, low power consumption, flexible deployment and intelligent analysis is realized.

The invention discloses a dam seepage intelligent monitoring and risk early warning method, system and device based on an expert large model and a storage medium, and belongs to the field of hydraulic engineering monitoring, and the method comprises the steps: collecting multi-source heterogeneous data from a dam seepage monitoring system, and marking structural domain labels according to data sources and structural attributes; executing corresponding feature fusion processing on different types of data according to the structural domain labels, and constructing a structured feature vector group in a unified format; inputting the feature vector group into a structure-driven expert large model, activating a bound expert path according to a structural domain label, and executing multi-task reasoning; the multi-task reasoning result is input into a dam seepage virtual twin system, a simulation module is driven to conduct seepage diffusion evolution simulation, and a space risk map is generated; and generating an early warning signal and an emergency plan text according to the risk map and the multi-task reasoning result.

The invention discloses an electric power field operation management system, which relates to the technical field of electric power and comprises the following process modules: S1, planning; s1-1, an operation plan and task management module; s1-2, a power industry core combined scale block; s2, preparing; s2-1, a personnel and qualification management module; s2-2, a tool and material management module; s2-3, a risk assessment and scheme management module; s3, executing; s3-1, a field execution and monitoring module; s3-2, an emergency processing module; s4, checking and accepting; s4-1, an acceptance check and filing module; s4-2, a performance and data analysis module; s4-3, a system management module; and S5, summarizing. According to the electric power field operation management system, through systematicness and integrity of emergency plan management, a process covers full life cycle management of an emergency plan from formulation, auditing, issuing to updating, and meanwhile, links such as emergency response starting, disposal, resource management, information communication and recovery summarization are included; and a complete closed loop of prevention, preparation, response and recovery is formed.

The invention provides a disaster evolution path deduction method and system based on a multi-modal space-time atlas, and the method comprises the steps: obtaining the attribute data of a shopping mall, and constructing a deduction static atlas based on the attribute data of the shopping mall; the deduction static map initializes a state based on a set instruction, updates based on physical simulation, and outputs a deduction dynamic map; generating a comprehensive risk matrix based on the deduction static atlas and the deduction dynamic atlas, and clustering the comprehensive risk matrix to obtain a classification region cluster; performing physical prediction and digital prediction according to the classification region cluster and the deduction dynamic map, respectively obtaining physical determination and judgment data and digital determination and judgment data, and obtaining spreading probability data through the physical determination and judgment data and the digital determination and judgment data; according to the method, re-deduction is carried out through an intervention instruction, a deduction method capable of carrying out step-by-step deduction and backtracking deduction is provided, a deduction dynamic map is fused, and the deduction accuracy is improved in a physical and digital cooperation mode, so that making of a better emergency plan is assisted.

The invention relates to an intelligent generation method for a subway emergency disposal scheme. The method comprises the following steps: S1, constructing an emergency plan knowledge graph; s2, performing automatic accident analysis and information extraction; and S3, generating an emergency disposal scheme. The invention further relates to a subway emergency disposal scheme intelligent generation system and a computer readable storage medium. The method has the advantages of high efficiency, accuracy, intelligence and the like, intelligent generation of the disposal scheme is realized by performing automatic analysis on accident data and combining a knowledge graph RAG technology, and the scheme generation speed is remarkably improved. The semantic reasoning ability of the knowledge graph and the deep analysis ability of the large model are utilized to ensure the high matching degree of the generation scheme and the actual event. The knowledge extraction and relation recognition technology of a large model is combined, automatic generation and logic relation reasoning of the emergency task unit are achieved, and the intelligent level is improved.

The invention discloses a hydropower station on-duty emergency processing method, system, equipment and medium based on a knowledge graph, and belongs to the technical field of hydropower station emergency processing, and the method comprises the steps: collecting the operation parameter data and historical fault information of a hydropower station, and carrying out the digital processing; based on a natural language processing and artificial intelligence training technology, constructing a knowledge graph including fault types, cause analysis and emergency disposal measures; establishing an intelligent fault diagnosis algorithm based on a knowledge graph, and carrying out fault identification and positioning on the operation data of the hydropower station; performing classification and priority ranking on the faults according to the identified fault result, and matching a corresponding processing flow; and according to the fault classification information, an emergency plan is started, a control instruction is executed to complete emergency processing operation, and recording is carried out. According to the invention, real-time sensing of the operation state of the hydropower station, accurate fault identification and priority classification are realized, and the response speed and processing accuracy of the hydropower station to emergencies are effectively improved.

The invention discloses a property asset digital operation and intelligent security linkage cooperative processing system and an implementation method, and relates to the technical field of Internet of Things, and the system comprises a protocol adaptive gateway layer which solves the problem of compatibility of multi-manufacturer equipment, and outputs a standardized data stream based on a dynamic protocol sniffing and fingerprint feature library, SDK-free access control, fire protection, monitoring and IoT equipment; the method comprises the following steps: constructing a millimeter-level precision three-dimensional space model by a digital twinborn middle table layer, fusing a security event and operation data, and ensuring that the operation is compliant and audible through blood relationship tracking; the intelligent decision execution layer combines a rule engine and machine learning to generate a linkage instruction, drives a work order to be automatically distributed and positions an equipment fault responsible party; and the edge cooperative computing node locally realizes video millisecond-level analysis, and starts localized linkage according to an emergency plan when the network is disconnected, so as to ensure that the core security function is not interrupted.

The invention discloses a security risk intelligent response method and system. The response method comprises the following steps: acquiring video streams, equipment states and environment sensor data in real time through a multi-source interface; processing through a feature fusion engine to generate a threat matrix containing threat type judgment and confidence; matching an emergency plan library based on a dynamic weighting algorithm; an equipment control instruction queue is generated according to a matching result, conflict detection is executed, and multiple instructions of the same equipment are sequenced according to fire control, access control locking and video tracking; and finally, acquiring an equipment execution state in real time and feeding back the state to a plan library to form closed-loop control, thereby solving the problems of high false alarm rate and response lag in traditional security and protection. Historical and real-time data are fused through a dynamic decision model, the limitation of a static plan is broken through, and the disposal efficiency is improved; instruction conflicts are eliminated through a conflict resolution mechanism; and a self-healing system and model correction are combined to form double-closed-loop optimization.

The invention provides an emergency plan generation method and device and electronic equipment, and the method comprises the steps: obtaining abnormal event description information for a target place, converting the abnormal event description information into a description text vector, carrying out the vector retrieval of a preset vector library according to the description text vector, and obtaining a target text vector, extracting a target entity relationship triple from the abnormal event description information, performing graph retrieval on a preset knowledge graph according to the target entity relationship triple to obtain a target knowledge sub-graph, and performing fusion according to a first slice text corresponding to the target text vector and a second slice text corresponding to the target knowledge sub-graph to obtain a target knowledge sub-graph; and a target big language model part in the multi-modal big model is adopted, and an emergency plan for the target place is generated according to the target slice text. According to the method, a two-channel retrieval mechanism of the knowledge graph and the vector library is adopted, related knowledge is retrieved, and the generation accuracy of the emergency plan is improved.

The invention provides a comprehensive emergency plan information management method and system, and relates to the technical field of plan information processing, and the method comprises the steps: constructing a first knowledge graph according to plan information corresponding to a plurality of historical emergency plans contained in a historical comprehensive emergency plan information base; determining an initial emergency plan for the to-be-planed event according to the first knowledge graph; constructing a second knowledge graph according to the content of the initial emergency plan; in response to occurrence of the to-be-planed event, determining conflict nodes of a second knowledge graph according to field information collected by the edge server; determining a supplementary plan in the first knowledge graph according to the basic information of the to-be-planed event and the conflict node; fusing the initial emergency plan according to the supplementary plan to obtain a target plan; and recording the determination of the initial emergency plan of the to-be-planed event and the generation of the target plan in a full cycle. According to the application, the intelligence, precision and dynamic response capability of comprehensive emergency plan management are integrally improved.

The invention relates to the field of industrial automatic control systems, and discloses a mine hoisting cage positioning information and emergency plan linkage control system, which comprises a conventional dynamic control unit and an absolute safety anchor point unit, the anchor point unit latches a continuous positioning value of a conventional unit when a binary anchor point sensor is triggered, and the absolute safety anchor point unit locks the continuous positioning value of the conventional unit when the binary anchor point sensor is triggered. The method comprises the following steps of: acquiring a theoretical reference value, comparing the theoretical reference value with the theoretical reference value to generate a drift calibration value, sending the drift calibration value to a conventional unit, and correcting a positioning reference by the conventional unit by using the drift calibration value. The online self-calibration of a control system is realized by using a safety anchor point, so that the safety unit is converted into an active calibrator; the accumulated drift of the high-precision sensor can be avoided during operation.

The invention provides an edge calculation and AR collaborative intelligent first-aid method and system based on trauma grading, and relates to the technical field of trauma first-aid, and the method comprises the steps: collecting trauma field data through a multi-modal sensor, constructing a dynamic digital twinborn model, and calling a medical knowledge graph to carry out trauma grading evaluation. And generating multiple groups of emergency plans based on a biological plasticity calculation principle, performing parallel deduction to select an optimal plan, and continuously collecting, feeding back and optimizing the plan through the neural morphology adaptive sensing network. Accurate grading and intelligent decision making of the emergency treatment process are achieved, and the emergency treatment efficiency and accuracy are improved.

The invention discloses an intelligent fire early warning method and system for power equipment based on a fire extinguishing patch, and relates to the technical field of intelligent early warning of fire extinguishing devices, and the method comprises the steps: enabling a fire extinguishing device to be connected to an intelligent collection terminal based on a power line carrier communication technology, and building an Internet of Things collection object and configuring an active reporting task; operating parameters of the fire extinguishing device are collected in real time; feature extraction and fault identification are carried out according to the operation parameters in combination with electrical operation environment data of the distribution box; performing multi-level intelligent grading and research and judgment on the fault identification result to obtain an early warning result; triggering an emergency plan according to an early warning result, generating a digital chemical order, and distributing the digital chemical order to a corresponding transformer area manager; through high-frequency real-time data acquisition, rough classification-fine classification fault identification and rule driving dynamic fusion, and a comprehensive early warning method of multi-stage intelligent grading and digital chemical engineering single triggering, the problems of poor fire monitoring real-time performance, low fault identification precision and low emergency response efficiency of power equipment are solved.

The invention discloses a basement ventilation and smoke exhaust system and method based on digital management, and the method comprises the steps: constructing a digital twin architecture comprising a terminal layer, an edge layer and a cloud layer, and carrying out the real-time mapping and interaction of a physical system for basement ventilation and smoke exhaust and a virtual model; carrying out integrated monitoring on the terminal layer by utilizing multiple sensors, and collecting relevant data parameters of ventilation and smoke exhaust of the basement; and a high-integration-level edge computing gateway is adopted to preprocess the collected data. The regional controller adopts a dynamic Bayesian network to fuse the preprocessed multi-source data, and constructs a local environment state model; the edge layer utilizes a deep reinforcement learning algorithm and a digital twinborn model to simulate multiple scenes to carry out state real-time prediction, and generates an emergency plan library; and the cloud layer central server operates a double-track decision engine and performs adaptive adjustment in combination with a deterministic rule base and an adaptive deep reinforcement learning model, and the technical scheme of the invention realizes efficient and accurate ventilation and smoke exhaust cooperative control.

The invention discloses a smart city monitoring and simulation deduction method based on a digital twinborn model, and belongs to the field of electric digital data processing.The smart city monitoring and simulation deduction method based on the digital twinborn model comprises the following steps that dynamic data are continuously accessed, static data are obtained at the same time, a digital twinborn body with a timestamp is constructed, and the digital twinborn body is constructed; forming a virtual mirror image of the physical state of the city; on the basis of real-time indexes in a digital twinborn body, hydrological model parameters are corrected in real time, the short-term rainfall prediction precision is improved by fusing an LSTM model, and a twinborn driving model matched with the current reality is generated; compared with the prior art, the method has the advantages that dynamic data and static data of a city are collected in real time, a digital twinborn model for analyzing city drainage is constructed, the ponding risk level can be rapidly evaluated, differentiated emergency plans are automatically generated according to different risk levels, and the method has the advantages of being high in prediction precision and high in response speed.

The invention discloses an urban inland inundation simulation method and an urban inland inundation emergency scheme selection method, and belongs to the technical field of intelligent water conservancy, and the method comprises the steps: obtaining historical social media data related to urban inland inundation; obtaining a first waterlogging feature set based on historical water level data collected by a water level monitor; obtaining a second waterlogging feature set based on the historical social media data and the historical water level data; the second waterlogging feature set and the first waterlogging feature set are adopted to perform parameter calibration on the urban waterlogging model under different rainfall capacities, and a parameter corresponding relation is obtained; obtaining target rainfall data, real-time social media data and real-time water level data; determining a target parameter of the urban waterlogging model based on the target rainfall data and a parameter corresponding relation; and substituting the target parameter into the urban inland inundation model to obtain a first urban inland inundation simulation result. The method can effectively improve the accuracy degree of urban inland inundation simulation.

The invention relates to the field of Internet of Things application, and discloses an intelligent property management system and method based on Internet of Things application, and the system comprises an equipment state sensing module, an implicit work order generation engine, a work order-resource dynamic matching module, and an emergency logic reconstruction module. Through an innovative state-work order mapping mechanism, real-time perception and dynamic analysis of the equipment state are realized, the work order can be automatically generated at the initial stage of potential abnormity of the equipment, and the problem of traditional fault lag is avoided. Resource configuration is optimized through intelligent scheduling, the shortest response path is calculated through a graph algorithm, and the maintenance efficiency and the response speed are improved. In addition, the dynamic emergency plan reconstruction module flexibly deals with superposition of complex events, and the adaptability and processing capacity of emergency response are enhanced.