160 results about "Urban management" patented technology

The invention provides a smart city space information management method and system based on live-action three-dimensional, and relates to the technical field of three-dimensional management, and the method comprises the steps: obtaining multi-source heterogeneous space data, and fusing the data to construct a high-precision live-action three-dimensional model; carrying out space element identification and establishing a dynamic space-time relation network in combination with a knowledge graph; intelligent reasoning and prediction are realized based on a graph neural network; performing space division by adopting an octree algorithm to construct a database; and establishing an influence evaluation model and providing an intelligent space information service. The spatial data fusion efficiency is improved, the spatial relationship analysis capability is enhanced, and the intelligent level of urban management is improved.

The invention relates to the technical field of urban space information processing, in particular to an urban functional area fine classification method based on multi-modal data collaboration. According to the method, POI data, AOI data, land utilization data, remote sensing images and other multi-source heterogeneous data are fused, a multi-dimensional feature system is constructed, and an urban functional area classification model covering a single functional area, a composite functional area, a mixed functional area and a non-functional area is provided. According to the method, firstly, block-level basic units are constructed based on an open street map (OSM), then coordinate transformation and function classification are carried out on POI and AOI data, features such as POI density, AOI area proportion and land utilization structure are extracted respectively, and finally, function type identification is carried out on urban block units according to set classification rules. Compared with the prior art, the method has the advantages that through multi-source information fusion and discrimination logic optimization, the accuracy and applicability of urban functional area recognition are remarkably improved, and the method is suitable for scenes such as urban planning, land utilization evaluation and urban management.

The invention discloses a city management AI dispatch algorithm and system based on historical mining and responsibility matching, and the method comprises the steps: building and dynamically updating a city management element evolution graph through obtaining the multi-mode description information of a city management case and the real-time state data of disposal resources; calculating potential disposal effects of different candidate dispatching schemes by using a causal inference engine, and generating a comprehensive efficiency estimation vector; on the basis, a multi-target reinforcement learning strategy is adopted to generate an optimal dispatch instruction, and system parameters are continuously optimized through online element learning during execution; cooperative processing network analysis is activated for sudden complex events, and responsibility atlas reconstruction is triggered when the matching efficiency is low. According to the method, the accuracy and efficiency of case disposal are remarkably improved, disposal timeliness optimization, resource load balancing and improvement of the first solution rate are realized, and meanwhile, the adaptive capacity and continuous optimization efficiency of the system to complex scenes are enhanced.

The invention discloses an urban risk prediction method and system based on multivariate data fusion, and relates to the technical field of urban tunnel risk prediction.The method comprises the steps that data such as the water flow velocity, pipeline structure parameters and real-time rainfall of a plurality of monitoring areas in an urban drainage tunnel are comprehensively monitored through the first step, the second step, the third step and the fourth step; the system can carry out comprehensive risk assessment and timely identify potential risks of blockage, sewage backflow and gas explosion. According to the comprehensive evaluation, the problem of single information island caused by a traditional single water level monitoring method is improved, a multi-level early warning mechanism is constructed, early warning signals of different levels are automatically generated when risks are recognized, and management personnel are prompted to take necessary countermeasures. The flexible early warning mechanism enables urban managers to respond quickly, improves the efficiency of emergency treatment, reduces possible loss, and ensures safe operation of the sewage discharge tunnel in various environments.

The invention provides an intelligent monitoring and risk early warning method for urban road settlement, and belongs to the technical field of urban management based on machine learning. The method comprises the following steps: firstly, collecting four types of multi-source space-time monitoring data, including urban road settlement data, underground environment data, pavement structure data and dynamic load and environment data; secondly, constructing a multi-source data space-time completion model, performing unsupervised completion on sparse monitoring area data, and generating space-time continuous settlement field data; thirdly, constructing a road settlement health index prediction model fusing multiple factors, inputting complementation data and original features, and outputting a health index of a 0-1 continuous interval; and finally, in combination with the health index and the road function level, four-level risk classification and dynamic early warning are realized. According to the method, the problems of space-time faults and data islands of a traditional method are solved, urban road global real-time monitoring is achieved, and early warning upgrading from qualitative judgment to quantitative grading is achieved.

The invention discloses a kitchen waste collection and transportation path optimization method based on an improved simulated annealing algorithm, belongs to the technical field of intelligent optimization and path planning, and mainly solves the technical bottlenecks of an existing kitchen waste collection and transportation path planning method in the aspects of dynamics, multi-objective optimization and algorithm efficiency. The method comprises an application layer, an algorithm layer and a data layer, and by fusing mixed neighborhood search, a dynamic punishment mechanism and a parallel computing architecture, the timeliness, the economical efficiency and the environmental protection property of path planning are remarkably improved. According to the method, real-time traffic and corruption risks can be dynamically responded, multi-target optimization weights are quantified, different city management strategies are adapted, the problems that a traditional algorithm is low in efficiency and prone to falling into local optimum are solved through multi-chain parallel acceleration and self-adaptive temperature control, and city-level large-scale node real-time scheduling requirements are supported.

The invention discloses an urban governance multi-modal large model construction method based on a target area, and relates to the technical field of smart cities. The method comprises the following steps: S1, acquiring a training set; s2, establishing an urban governance multi-modal large model; s3, training the urban governance multi-modal large model in the step S2 through the training set obtained in the step S1; a trained urban governance multi-modal large model is obtained; s4, repeatedly executing the step S3 to obtain a first number of trained urban governance multi-modal large models; and S5, evaluating the first number of trained urban governance multi-modal large models, and selecting the optimal urban governance multi-modal large model as the urban governance multi-modal large model constructed by the method. According to the urban governance multi-modal large model constructed by the urban governance multi-modal large model construction method based on the target area, violation scenes in urban management can be identified more accurately.

The invention discloses a method and a system for rapidly and continuously detecting missing border trees based on dynamic edge calculation. The method comprises the following steps: acquiring an image, a GPS coordinate and a timestamp, and constructing a spatio-temporal trajectory; preprocessing the image, identifying and classifying vegetation, municipal facilities and moving shelter targets, and outputting feature information and confidence score to obtain an image identification result; based on an image recognition result, screening a street tree close to the lane line and predicting a corresponding position, constructing a motion track through continuous multi-frame data association, and performing calibration in a coordinate system to generate a predicted attention area to form a street tree position prediction result; and judging and classifying the plant missing condition in the green belt according to the street tree position prediction result, and generating related event information. By implementing the method provided by the invention, the limitation of the prior art can be overcome, more efficient and accurate technical support can be provided for daily maintenance of urban greening, and the quality and efficiency of urban management can be improved.

The invention discloses a crowd flow dynamic monitoring method and system, and relates to the technical field of crowd flow monitoring, and the method comprises the steps: deploying a full-coverage signal collection network for key nodes in a monitoring region, and associating a unique identifier of a monitoring user RFID tag with an anonymized user file; the RFID reader captures the unique identifier of the RFID tag, the timestamp and the signal intensity to form a multi-dimensional data pair, then an initial moving sequence is generated, the position error is corrected based on the difference value of the signal intensity of the adjacent RFID readers, and an individual smooth path track is generated; and aggregating the individual smooth path tracks of all the RFID tags to generate a full-region people flow density thermodynamic diagram, identifying and marking high-frequency moving paths and retention hotspots, and generating facility layout optimization suggestions based on a predicted hotspot capacity threshold. Full-coverage accurate data acquisition, individual track accurate reduction and macroscopic analysis visual presentation are realized, and powerful support is provided for city management and resource allocation.

The invention relates to the technical field of smart city information management, in particular to a smart city information management method and system based on GIS and AI fusion, and the method comprises the steps: carrying out the data mining and statistical analysis of the basic information of each economic entity in a city, and achieving the overview of economic development situations; collecting geographic position data of an economic body and matching coordinates, and constructing a three-dimensional digital city model associated with a GIS system; a hierarchical comprehensive evaluation algorithm is adopted to calculate the economic innovation value, the contribution degree of the economic innovation value is analyzed, and a long-short memory network is utilized to carry out space-time dynamic evolution analysis and trend prediction; and intelligent semantic understanding and personalized question and answer feedback are realized by analyzing a multi-modal query request of a user. The problems that an existing system is difficult in data fusion, static state analysis lags behind, and decision support is insufficient are solved, unified perception, intelligent analysis and scientific decision support of the urban operation state are achieved, and the urban fine management and service level is remarkably improved.

The invention discloses a smart city similar complaint event recognition method based on a graph database and semantic recognition, and relates to the technical field of smart city management, and the method comprises the steps: carrying out the vectorization calculation of a complaint event text through employing a sentence-transformers / paraphrase-multilingual-Mini LM-L12-v2 model, and carrying out the model fine tuning for the field of city governance; constructing an event-complaint-address-keyword association network model based on a graph database, defining points and edges, and realizing complaint event multi-dimensional association modeling through attribute design; preprocessing the stock data, establishing an association relationship among events, complaints, geographic positions and keywords, and importing the association relationship into a graph database; and performing preprocessing operation on the newly added complaint event, and calculating similarity based on keywords and complaint person information in combination with graph database retrieval so as to realize intelligent identification of multi-person simultaneous complaint and one-person multi-complaint. According to the invention, intelligent clustering and repeated reporting identification of city complaint events can be realized.

The invention discloses a smart city resource allocation method and system based on end-cloud collaboration. The method comprises the steps of collecting urban multi-dimensional resource state data in real time through an Internet of Things sensor, and performing standardization processing; edge calculation is executed on the end side, noise is filtered, and a resource load feature vector is extracted; uploading the data to a cloud for multi-source data fusion and association analysis to obtain a resource interaction relationship quantitative index; a resource multi-dimensional situation map model is constructed, dynamic adjustment is triggered according to the change rate, and resource availability distribution is judged; simulating a resource flow path by using a graph neural network, and generating an optimal allocation scheme draft; issuing a control instruction to an end side for execution, and determining a final plan according to feedback iterative updating; and continuous correction is performed through a monitoring feedback mechanism. The system comprises corresponding modules and realizes intelligent resource allocation. According to the method, the real-time performance and the self-adaptability of resource allocation are improved, and the city management efficiency is optimized.

The invention relates to the technical field of city model construction, and discloses a smart city 3D model construction method and system, and the method comprises the steps: S1, enabling an unmanned aerial vehicle to fly over a city according to a preset flight path, dynamically calculating a proper flight height according to the height of a building and the pitch angle of a camera in the flight process, and automatically adjusting the flight height; the flight height is dynamically adjusted according to the building height and the camera pitch angle in the flight process of the unmanned aerial vehicle, and the coverage range and the resolution of image acquisition are ensured; in combination with the real-time analysis capability of the edge calculation module, data defects can be quickly identified and laser radar supplementary acquisition can be triggered, so that the data acquisition efficiency and integrity are remarkably improved; and finally, through multi-source data fusion and automatic modeling of the cloud processing platform, a high-precision and high-reality smart city 3D model is constructed, and a reliable three-dimensional digital basis is provided for city management and planning.

The invention relates to the technical field of city management, and discloses a city operation management system for resource allocation based on big data. Comprising data acquisition, data storage and preprocessing, data security protection, continuous model optimization, data analysis and prediction, multi-department collaborative linkage, resource allocation decision making, execution and feedback, and visual display and interaction, and the data security protection comprises transmission encryption, authority management and data backup and recovery. According to the urban operation management system for resource allocation based on big data, through encryption transmission, multi-level authority management and regular remote backup, by means of professional evaluation groups, an online learning technology and a model fusion strategy, an informatization platform is built, a collaborative process and a plan are formulated, department barriers are broken, a visual platform is built, and an interaction function is added; therefore, the effects of comprehensively guaranteeing the data security, optimizing the model in real time according to the new data, quickly responding to complex conditions and collaboratively working can be achieved.

The invention relates to the technical field of artificial intelligence, and discloses a smart city monitoring management method based on artificial intelligence, comprising the following steps: step A: data acquisition and preprocessing: acquiring data related to city operation from a plurality of heterogeneous data sources; step B, cross-domain data fusion: carrying out feature extraction and fusion on the preprocessed data; c, performing intelligent analysis and prediction, and applying a machine learning algorithm; and step D, decision support and response: transmitting an analysis result and prediction and early warning information to urban managers and related departments through the system. By introducing an artificial intelligence technology, a deep learning model, a transfer learning technology and a semantic web technology or ontology method, the problem of cross-domain data fusion is effectively solved. The methods can process and analyze heterogeneous data from different data sources and convert the data into a unified and comparable format, so that seamless joint and integration of cross-domain data are realized.

The invention discloses an AIoT-based urban management digital intelligent consumption field management platform, and the platform comprises a sensing detection module which is used for connecting various types of Internet of Things equipment of a consumption field, so as to carry out the multidirectional sensing detection of the consumption field; the queue generation module is used for generating a consumption management queue according to the acquired transportation task information; the vehicle scheduling module is used for checking the vehicles entering the parking lot through the sensing detection module based on the consumption management queue, determining an in-field operation area for the vehicles entering the parking lot according to the vehicle type and the cargo type, and generating a planned path; and the in-field and out-field cooperation module is used for acquiring the in-field operation information according to the intelligent sensing module and updating the consumption management queue according to the in-field operation information. The entering vehicles are regulated and controlled by distinguishing the vehicle types and the cargo types, and the vehicles can appear in the matched working areas at proper time through cooperation inside and outside the parking lot, so that the absorption and operation efficiency is greatly improved.

The invention discloses an urban environmental sanitation operation track abnormity identification and early warning method based on the Internet of Things, and relates to the technical field of Internet of Things and spatio-temporal data analysis in intelligent city management, and the method comprises the steps: collecting the positioning track data and operation state data of an environmental sanitation operation object, and constructing an operation track data set; constructing a standard trajectory reference model by adopting a spatial buffer algorithm, and identifying trajectory offset abnormal segments and operation state violation abnormal segments; on the basis of the trajectory offset abnormal segment and the operation state violation abnormal segment, no-load behavior recognition and abnormal combination processing are executed, and an abnormal event set marked with abnormal types and time periods is output; and constructing a grading early warning model according to the abnormal event set, and outputting early warning information to an operation management platform. According to the invention, visual supervision and abnormal fine early warning of the whole urban environmental sanitation operation process are realized, and the urban fine management level and the public service quality are remarkably improved.

The invention relates to the technical field of urban inland inundation monitoring and prediction, and discloses an urban inland inundation ponding point analysis and prediction algorithm and system based on a large model and a big data technology. Collecting multi-source heterogeneous data including hydrological data, real-time weather forecast data, topographic data and historical waterlogging data of key nodes of the rainwater pipe network; s2, cleaning, standardizing and fusing the multi-source heterogeneous data to form a structured input data matrix; through multi-source big data fusion and big model dynamic simulation, by means of an advanced urban inland inundation early warning and monitoring system and in combination with big data analysis and machine learning technologies, the prediction error of the inland inundation occurrence time can be controlled within 10 minutes, and meanwhile, the prediction precision of the inundation range is improved by more than 40%; accurate time and space references are provided for urban managers and emergency response teams, and the efficiency of coping with waterlogging disasters is greatly improved.

The invention discloses a charging pile metering and billing optimization control method based on an intelligent city management platform, and relates to the technical field of billing optimization, and the method comprises the steps: recognizing the delayable charging capability of each charging request, and dividing the requests into high-elasticity schedulable requests and low-elasticity supportability requests according to the capability; calculating the total schedulable power which can be provided by all the high-elasticity schedulable requests in the target adjustment time period; the total schedulable power is compared with the adjustment demand power of the power grid, the technical problem that the deviation between the declaration duration and the actual duration is too large in charging request elastic classification is solved, and the classification precision is improved.

The invention discloses an urban business center identification method and system based on big data, and relates to the technical field of urban management, and the method comprises the steps: training a gradient lifting decision tree through multi-source data based on differential privacy and a homomorphic encryption algorithm, carrying out the gradient aggregation, carrying out the collaborative modeling based on business features, and generating a joint feature weight matrix; based on the joint feature weight matrix, obtaining an optimal spatial-temporal feature weight through a quantum annealing algorithm in combination with classical simulated annealing adjustment, monitoring the stability of the optimal spatial-temporal feature weight through a dynamic adjustment mechanism, and performing dynamic adjustment; and in combination with the optimal spatial-temporal feature weight and the road network real-time state data, performing spatial-temporal kernel density calculation on the whole-city grid through an adaptive kernel function, and identifying the city business center according to a calculation result. According to the invention, the global optimization and dynamic adjustment of the feature weight are realized by combining the temporal-spatial feature weight optimization of classical simulated annealing based on the quantum annealing algorithm.

The invention belongs to the technical field of urban governance, and discloses a bimodal urban governance system, a computer device and a storage medium, and the system comprises a collection judgment module which is used for collecting early warning information and judging whether the early warning information is steady state information; if the judgment result is steady-state information, taking the early warning information as the input of a steady-state module; if the judgment result is not the steady state information, taking the early warning information as the input of a sensitive state module; the steady state module is used for outputting a corresponding disposal scheme according to the early warning information; the sensitive state module is used for determining a sensitive state object and acquiring sensitive state data according to the early warning information; according to the urban management system, intelligent distinguishing between the steady state and the sensitive state is carried out on the early warning information through the acquisition and judgment module, a dual-mode parallel processing and automatic switching mechanism of the urban management system is realized, manual decision links and time are reduced through the intelligent scheme, and the efficiency of the urban management system is improved. And the treatment efficiency is improved.

The invention discloses a computer system fault detection method and system based on artificial intelligence, and relates to the technical field of fault detection.When the system operates, a first data set, a second data set and a third data set are obtained by collecting traffic system and urban infrastructure data, and key features are extracted from the collected data; the data processing module is used for storing the data, reserving information beneficial to fault detection, transmitting the information to the data processing module, training a machine learning model by using historical data, calculating a computer equipment operation state index Jtss and comprehensively evaluating the operation state of the traffic facility, continuously monitoring the real-time state, inputting the data to the machine learning model, detecting potential faults and responding to the system state change in real time. Once an abnormality is detected, an alarm is generated and a notification is sent to related city management personnel, so that actions are taken in time to solve problems, the time from fault discovery to response is shortened, the negative influence of the fault on urban traffic and infrastructure is adjusted, and system operation feedback information is collected regularly.

The invention is suitable for the technical field of city management, and provides a community service convenience level evaluation method, device, equipment and program product, and the method comprises the steps: obtaining the building information of a target community; respectively counting the number of different types of service facilities in the target community in a preset service range; according to the number of different types of service facilities in the target community, service facility related parameters of the target community are calculated, and the service facility related parameters comprise a facility diversity parameter, a per capita facility parameter, a facility distribution density parameter and a traffic coupling degree parameter; and evaluating the service convenience level of the target community according to a target evaluation index determined by the service facility related parameters of the target community. According to the method, the parameter design in the service convenience level evaluation method is enriched, the supply richness of the service facilities is fully considered, the precision of the community evaluation result is improved, and a reliable basis is provided for optimizing the construction of the community life service facilities.

The invention discloses an AR / VR urban management law enforcement intelligent decision-making method and system based on digital twinning and multi-modal fusion, and the method integrates digital twinning scene construction, multi-modal heterogeneous data deep learning and AR / VR real-time interaction. The method realizes full-process intelligent decision-making from multi-mode law enforcement data acquisition, preprocessing and feature extraction to model training, event prediction, virtual deduction and actual combat disposal, is suitable for accurate research and judgment and dynamic response of city-level management events, and is especially suitable for complex scenes such as major activity guarantee and cross-regional cooperative law enforcement. The problems of single perception dimension, static decision support, low cross-department cooperation efficiency and the like in the traditional urban management law enforcement decision are solved, the full-link intelligent decision of the urban management event from data perception to actual combat disposal is realized, the event research and judgment accuracy can be improved, the scheme generation speed can be increased, and the cross-department cooperation efficiency can be enhanced.

The application relates to the technical field of urban infrastructure management, and discloses a city infrastructure intelligent supervision method and system based on digital twinning, which comprises a city infrastructure identification module, a city infrastructure diagnosis module and a city infrastructure feedback module; scientific dynamic diagnosis of abnormal hardware structure of a city infrastructure supervision node is carried out according to city infrastructure supervision node object entity model information and target city infrastructure supervision node normal three-dimensional entity model information, so that digital inspection of the external structure of the city infrastructure hardware is realized; target city infrastructure abnormal entity model identification data is transmitted to a city management platform through an Internet of Things communication network and is displayed on a display screen, so that visualized supervision of the city infrastructure based on digital twinning is realized, and the intuitiveness and applicability of the city infrastructure supervision are improved.

The invention provides an urban operation similar event identification method and system based on graph neural network reasoning, and relates to the technical field of urban management, and the method comprises the steps: for a new event reported to an urban operation management platform, extracting historical event data in the platform, and constructing an event attribute heterogeneous graph by taking event content and attributes as nodes; then, features of all nodes in the event attribute heterogeneous graph are aggregated through a graph neural network model, and vectorization expressions of a new event and a historical event are obtained respectively; by calculating the similarity between the new event vector and each historical event vector, a plurality of most relevant historical event data can be accurately screened out; and pushing the similar historical events and the contained disposal schemes to city management personnel. The method has the beneficial effects that through the graph neural network and the event attribute heterogeneous graph, the similarity is calculated on the basis of semantics and association relationships instead of depending on surface keywords, and the recall rate and accuracy of similar event recognition are remarkably improved.

This invention discloses a city low-altitude user equilibrium system with generalized cost, comprising: constructing an air-ground cooperative three-dimensional transportation network for users to travel between origin and destination in low-altitude environments; defining a generalized path cost model and constructing an air-ground cooperative three-dimensional transportation network cost model based on the topology of the network; transforming the user equilibrium problem in the air-ground cooperative three-dimensional transportation network into a variational inequality model based on traffic flow within the network; and solving the variational inequality model using a combination of the K-shortest path algorithm and adaptive projection method to calculate the equilibrium flow of the air-ground cooperative three-dimensional transportation network, thereby completing the planning of the city low-altitude travel system. This system accurately characterizes many real-world factors considered by users when traveling in the air-ground cooperative network, providing decision support for urban management departments to effectively construct and fully utilize the performance of the city's three-dimensional transportation network.

The invention belongs to the technical field of smart cities, and particularly relates to a smart city environment management method and system based on digital twinning, and the method achieves the high-precision collection of species distribution, vegetation coverage, surface temperature and wind speed data in a city region. By constructing a dynamic space model containing a species-environment interaction relationship, the dynamic change of surface thermal radiation absorption and release caused by increase and decrease of different species is successfully simulated; furthermore, by calculating hourly influence coefficients and iteratively deriving a ten-year microclimate evolution trend, a priority ranking scheme for species protection or regulation is finally generated. According to the method, the understanding depth and response speed of urban managers for ecological environment changes are greatly improved, real-time calibration based on latest monitoring data becomes possible, and scientificity and foresight of urban management decisions are ensured.

The invention provides a smart city service management method and system, and relates to the technical field of city service management. The method comprises the steps of collecting multi-dimensional operation data of a target city; performing functional region division on the city; calculating a basic service type and a layout density reference value of each region; quantifying the spatial position correlation degree and the functional complementation correlation degree among the functional areas; generating an optimal service management layout scheme; and dynamic adaptation and closed-loop optimization of service management are realized. According to the invention, by dynamically adjusting service layout, demand fluctuation caused by population tides, emergencies and the like is dealt with, and the city service toughness is improved. And meanwhile, invalid investment is reduced through resource optimization configuration, the urban management cost is reduced, and powerful support is provided for sustainable development.

The invention discloses a city management method and system. The city management method comprises the following steps: constructing a city component facility management platform; carrying out collection field self-definition based on an urban component facility management platform; after the collection field self-definition is completed, part information is collected in real time; performing data processing on the part information acquired in real time; and performing intelligent management according to the processed component information. According to the invention, by constructing a dynamic and precise urban part facility management system, the management cost is significantly reduced, the management efficiency is greatly improved, and the disposal period is shortened. And meanwhile, the accuracy and timeliness of the data are improved by further processing the data. And the safe operation of the facility is ensured by timely repairing the components.