16 results about "Urban system" patented technology

A dynamic evaluation method for an extreme rainstorm waterlogging disaster risk for disaster prevention and reduction. The method comprises: investigating and surveying urban system data and disaster prevention and reduction data, using GIS technology to divide disaster-bearing objects into refined risk units on the scale of urban buildings and road networks, and determining the spatial distribution of the disaster-bearing objects; on the basis of an extreme rainstorm waterlogging scene, simulating the disaster influence of a dynamic change process of a flood ponding depth on the disaster-bearing objects; developing refined dynamic evaluation on a waterlogging risk by combining the two methods of waterlogging process simulation and an indicator system; using a spatial complex network and a shortest path plan to calculate a traffic capacity and emergency service accessibility of a road network system; and on this basis, taking into comprehensive consideration the rational allocation of disaster prevention emergency drainage and emergency rescue services to a high-risk area, and proposing dynamic evaluation technology for a waterlogging risk that integrates a disaster evolution process and a disaster prevention response process, and ultimately realizing the dynamic evaluation of the waterlogging risk of each disaster-bearing unit during the waterlogging disaster evolution.

The invention relates to the technical field of data analysis, in particular to a city physical examination accurate analysis method and system based on AI multi-modal data collaboration, and the method comprises the following steps: constructing a self-adaptive fractal space-time grid, and mapping a city multi-modal data source into a space-time coding vector with a fractal dimension; establishing a physical field driven cooperative resonance network, and extracting an abnormal cooperative mode exceeding a normal resonance threshold; calculating a modal entropy chain value according to the real-time metabolic rate of the urban system, and generating an optimal weight matrix through a non-equilibrium thermodynamic model; and fusing the abnormal cooperation mode and the dynamic weight matrix, and generating a space-time causal map for displaying a fault source and a propagation path by using a causal discovery algorithm. According to the method, the multi-modal data collaboration and causal analysis technology is utilized, the emergency response capability of an urban system in the case of sudden failures is improved, the stability of urban operation is guaranteed, and the space-time causal atlas provides comprehensive visual information for the failure propagation process.

The invention provides an urban carbon emission optimization model algorithm aiming at the urgent demand of green and low-carbon transformation of an urban industrial structure. According to the algorithm, a multi-agent model comprising a public management mechanism agent and an enterprise agent is constructed based on a complex adaptive system theory. The core lies in that an evolution path of urban carbon emission and industrial upgrading is dynamically deduced by simulating an adjacent learning effect between enterprises and a regulation and control mechanism of a public management mechanism based on a pre-decision. The enterprise agent decides the output value speed increase and the carbon emission intensity change rate according to the self state, the surrounding enterprise state and the public management mechanism predetermined target; and the government agent formulates and adjusts industry and emission reduction policies according to the overall goal of the city and enterprise pre-decision. According to the invention, through dynamic coupling of the microscopic enterprise decision and the macroscopic city system, low-cost and high-efficiency simulation and optimization of the city carbon emission path under different target scenes are realized, and a scientific quantitative analysis tool is provided for city carbon emission.

The invention discloses an urban system simulation model coupling method, device and product based on a disaster chain. The method comprises the following steps: acquiring urban related data and water disaster related data; classifying the city related data according to different city system types to obtain first modeling data of different types of city systems; classifying the water disaster related data to obtain water disaster related second modeling data influencing the operation of different types of urban systems; according to the first modeling data and / or the second modeling data of the different types of city systems, building different types of city system models; carrying out disaster chain analysis on the related data of the water disasters, and determining disaster chains of various types of water disasters; coupling the urban system model and the disaster chains of various types of water disasters to generate an urban system disaster simulation model fusing the disaster chains of the composite water disasters; according to the method, urban system simulation model coupling is carried out by considering the disaster chain of the composite flood, and the accuracy of predicting the composite flood in cities can be improved.

This invention relates to the field of data analysis technology, specifically to a precise analysis method and system for urban health checks based on AI-driven multimodal data collaboration. The method includes the following steps: constructing an adaptive fractal spatiotemporal grid to map urban multimodal data sources into spatiotemporal encoded vectors with fractal dimensions; establishing a physics-driven co-resonance network to extract abnormal co-resonance patterns exceeding normal resonance thresholds; calculating modal entropy chain values ​​based on the real-time metabolic rate of the urban system and generating an optimal weight matrix using a non-equilibrium thermodynamic model; and fusing abnormal co-resonance patterns and the dynamic weight matrix to generate a spatiotemporal causal graph displaying the origin and propagation path of faults using a causal discovery algorithm. This invention, utilizing multimodal data collaboration and causal analysis technology, enhances the emergency response capability of urban systems in the face of sudden faults, ensuring the stability of urban operations. The spatiotemporal causal graph provides comprehensive visualization information for the fault propagation process.

This invention relates to the field of smart city safety and emergency management technology, and provides a method and system for urban data fusion event early warning. The method includes: constructing a physical environment time-series dataset, an urban facility asset dataset, and an API call behavior log dataset; constructing a dynamic digital twin graph based on the aforementioned datasets; using the dynamic digital twin graph and the physical environment time-series dataset, applying a sliding time window anomaly detection mechanism to locate initial risk triggering nodes, performing graph traversal search to construct a set of potential risk propagation paths, and using a chain probability model to quantitatively evaluate and generate a set of early warning events; based on high-risk events, calling a natural language generation function to match the optimal emergency plan from an emergency plan knowledge base, visualizing the early warning scenario in conjunction with the natural language early warning message, and parsing the optimal emergency plan to generate scheduling instructions. This invention constructs an event early warning and response mechanism for urban systems, applicable to smart city early warning and emergency management.

The present application discloses an artificial intelligence-driven urban system computational simulation platform method and related devices, relating to the fields of artificial intelligence and digital simulation technology. The method comprises: obtaining basic urban data to generate multiple initial strategies, then sampling to obtain a first reference strategy; deducing the first reference strategy through a strategy calculation model to obtain a first benchmark deduction result, and adding the result to a strategy recommendation set; screening at least two initial strategies through a target agent model to obtain a second reference strategy; deducing each second reference strategy through a strategy calculation model to obtain a second benchmark deduction result, and updating the strategy recommendation set based on the second reference strategy and the second benchmark deduction result to recommend strategies to users. The present application can implement urban system strategy recommendations and meet user control needs.

The invention discloses an urban system toughness dynamic measurement method and device, equipment, a medium and a product, and relates to the field of urban disaster management and risk assessment, and the method comprises the steps: obtaining toughness index data and disaster environment data of a target city; determining an initial rainstorm impact index according to the disaster environment data; constructing a toughness index system according to the toughness index data and the initial rainstorm impact index; calculating a weighted comprehensive index of each toughness dimension according to each toughness index value under each toughness dimension to obtain a plurality of toughness attribute parameter values; according to the toughness attribute parameter values, a parameterized gamma family recovery function model is adopted to simulate system performance changes after the target city is subjected to disaster impact, and a dynamic performance recovery curve is generated; and calculating a dynamic toughness quantized value and recovery time of the target city based on the dynamic performance recovery curve. According to the method, dynamic simulation of the whole process of'impact-response-recovery 'can be carried out on the city, and the accuracy of city toughness measurement is improved.

The invention provides a graph representation learning method and device considering graph node attribute missing and graph structure incompleteness, and a storage medium, and belongs to the technical field of graph neural networks. The problem that an existing method takes a single node as a basic unit, the mode of deducing the full view of information in a graph does not conform to the urban regional association theory, and a regional interaction back mechanism cannot be effectively and reasonably described is solved. According to the method, a node pair is taken as a basic unit, a node attribute-structure cyclic consistency constraint is introduced, joint distribution of node attributes and edges in the graph is modeled, and missing information in the graph is complemented by using the distribution, so that the method is more matched with the complementation of the missing information of an urban area; the application background of the method is data-driven city system dynamics modeling and is used for predicting the city development situation.

The invention relates to the technical field of flood control, in particular to an urban flood influence degree evaluation method and electronic equipment. The evaluation method comprises the following steps: establishing a three-dimensional model of an object area, and dividing the three-dimensional model into a plurality of unit areas in a horizontal plane; according to the three-dimensional model, the circulation range of water in the object area at different heights is determined; determining a flood source input port in the three-dimensional model, and determining the flood flow of the flood source input port; on the basis of the flood flow, determining hydrodynamic data of each unit area through a continuity equation in the circulation range; and determining the propagation condition of the flood in the object area according to the hydrodynamic data of each unit area, and determining the influence degree of the flood on the object area according to the propagation condition. According to the method, more accurate flood influence evaluation can be carried out on a complex urban system, the flood control and disaster reduction capability of a city can be improved, reference can be provided for urban planning and infrastructure design, and further optimization of flood control means is facilitated.

The invention belongs to the technical field of city safety and emergency management, and particularly discloses and provides a BIM and big data fusion-based city toughness evaluation and planning decision-making system, which comprises the following steps of: deducing a predictive buffer depletion curve and judging a toughness phase change critical point of a city system, and providing a specific time window of possible collapse of the system in the future; key hub buffering is positioned through inversion, and an initial link playing a decisive role in a chain failure chain is locked; physical assets in different fields are standardized into a buffer list by constructing a city toughness evaluation model, and a dynamic consumption vector is generated, so that the integral and dynamic measurement of the city comprehensive buffer capability is realized; by generating a buffer center type intervention strategy set, an action route map is provided for solving the identified key risk, a specific scheme for enhancing the capacity or reducing the coupling relation is automatically generated around a key hub buffer, and the complexity and fuzziness of decision making are greatly reduced.

The application discloses a kind of urban system resilience dynamic measurement method, device, equipment, medium and product, it is related to urban disaster management and risk assessment field, the method comprises: obtaining the resilience index data and disaster environment data of target city;According to disaster environment data, determine initial storm impact index;According to resilience index data and initial storm impact index, construct resilience index system;According to each resilience index value under each resilience dimension, the weighted comprehensive index of each resilience dimension is calculated respectively, obtain multiple resilience attribute parameter values;According to each resilience attribute parameter value, using parameterized gamma family recovery function model simulates the system performance change of target city after suffering disaster impact, generates dynamic performance recovery curve;Based on dynamic performance recovery curve, the dynamic resilience quantitative value and recovery time of target city are calculated.The application can carry out dynamic simulation to the whole process of "impact-response-recovery" of city, improve the accuracy of city resilience measurement.

The application discloses a kind of urban system simulation model coupling method, equipment and product based on disaster chain, which comprises the following steps: collecting city related data, water disaster related data;City related data is classified according to different city system types, and the first modeling data of different types of city system is obtained;Water disaster related data is classified, and the second modeling data related to water disaster affecting the operation of different types of city system is obtained;According to the first modeling data and / or second modeling data of different types of city system, different types of city system model are constructed;Disaster chain analysis is carried out on water disaster related data, and the disaster chain of each type of water disaster is determined;Coupling is carried out on city system model and the disaster chain of each type of water disaster, and the city system disaster simulation model of the disaster chain of fusion composite water disaster is generated;The application considers the disaster chain of composite water disaster to carry out city system simulation model coupling, which can improve the accuracy of composite water disaster prediction faced by city.

This invention provides a method for dynamic urban renewal based on deep reinforcement learning, relating to the field of urban renewal. The method includes: step S1, data acquisition; step S2, state prediction; step S3, hierarchical decision-making; step S4, execution feedback; and step S5, iterative optimization. Hierarchical decision-making, based on the urban state data in step S1 and the predicted state in step S2, implements hierarchical update schemes for macro-level, meso-level, and micro-level intelligent agents. This method transforms urban renewal from a one-time static planning process to dynamic adaptive decision-making that evolves with the urban system, thereby achieving dynamic adaptation of infrastructure and public services and avoiding negative impacts such as environmental damage, facility idleness, resource waste, and increased costs caused by mismatch.

The application provides a kind of urban system earthquake disaster resilience dynamic measurement method, system, equipment and medium, belong to urban risk management and disaster prevention planning technical field.The method includes: constructing the earthquake disaster resilience multilayer network model comprising multiple network layers corresponding to different urban subsystems to represent the dependence relationship within the system and across systems;Based on the topology and real-time state data of the model, the comprehensive resilience index is dynamically calculated, which is obtained by integrating the three-dimensional index set of function synergy degree, structural stability and resource potential;Based on the comprehensive resilience index, the resilience critical threshold is determined using the prediction model and threshold determination technology;Finally, by comparing the real-time or predicted resilience index with the critical threshold, the intelligent early warning of resilience level is realized, and the decision support is generated based on the network model analysis.The application realizes the transformation of urban earthquake disaster resilience from static evaluation to dynamic real-time monitoring, and solves the problem of existing method system isolation and model simplification.

This invention discloses a method for predicting carbon emissions in urban systems based on input-output analysis and Bayesian neural networks. The method includes: determining the carbon emissions of each sector in the city; calculating a carbon emission input-output table; identifying key carbon-emitting sectors and major energy consumption factors; calculating the economic share and energy consumption factors of these sectors; establishing an urban carbon emission prediction model considering economic development and energy consumption; designing high, medium, and low future scenarios based on historical data of key carbon-emitting sectors and energy consumption factors, as well as the city's future development plan; solving the coupled model; quantifying the impact of different scenarios on urban carbon emissions; and determining under what development model the city's carbon emissions can meet predetermined requirements. This invention introduces a reasonable and scientific analytical prediction model into the urban energy and economic system, providing more effective technical support for the generation of decision-making solutions.