88 results about "Urban management" patented technology

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 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 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 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 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 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 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 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.

The application provides a building layering intelligent recognition method and system based on multi-modal remote sensing data, and belongs to the technical field of building detection. Multi-modal and multi-view remote sensing images including optical remote sensing images and radar remote sensing images are continuously acquired; the remote sensing images are input into a deep learning prediction model to realize accurate prediction of the height of urban building groups; the building height information predicted at multiple time nodes is calculated by a difference analysis method and a change vector analysis to obtain the change value of height data in different periods; the buildings whose height change values exceed a first threshold value are preliminarily marked as layering buildings; a weighted local spatial anomaly measurement method is further applied to calculate a spatial consistency index of height change in the region, and if the index exceeds a second threshold value, the building is finally determined as a layering building. The application can realize automatic and intelligent identification of illegal layering buildings in urban areas, and provide a scientific basis for urban management and law enforcement.

The invention relates to the technical field of city management, and discloses a city governance command and dispatch system based on one-network unified management. Comprising a work order queue summarizing module for summarizing civil complaint work orders of the same work order type into a work order queue, a target jurisdiction marking module for marking a target jurisdiction from an urban jurisdiction, a target jurisdiction dividing module for dividing the target jurisdiction into an emergency jurisdiction and a common jurisdiction, and a work order strategy analysis module for analyzing a command and dispatch strategy of the civil complaint work orders. According to the method, the problems of cross-jurisdiction matching and misjudgment of similar or similar public conditions and meanings in different urban jurisdictions can be avoided, the recognition accuracy of the target jurisdictions is improved, and the emergency priority degree of processing and solving the civil complaint work order of the target jurisdictions can be expressed from different time dimensions of the past time and the current moment; different analysis logics and ideas are provided for subsequent command and dispatch strategies, and the effects of intelligent analysis, research and judgment and command and dispatch treatment of the public hotspots are guaranteed.

The embodiment of the invention provides an urban illegal building identification method based on an unmanned aerial vehicle, and the method is applied to the technical field of urban management, and comprises the steps: obtaining a building image obtained through the aerial photographing of a target region by the unmanned aerial vehicle according to a preset route; preprocessing the building image; building edge detection is carried out on the preprocessed building image to obtain a building contour; extracting a building contour, and converting the extracted building contour into a vectorized building boundary; and comparing the vectorized building boundary with a corresponding legal building boundary in an urban planning database, and determining whether the corresponding building is an illegal building or not. Therefore, the method can achieve the automatic recognition of urban illegal buildings based on the unmanned plane, greatly reduces the manual intervention, and effectively improves the recognition efficiency.

The invention discloses a smart city data intelligent fusion system. The invention discloses a smart city data intelligent fusion system, and the system comprises a map information collection device which is used for obtaining the digital elevation model data of a target region, and constructing a three-dimensional scene basic model based on the digital elevation model data; according to the system, multi-source heterogeneous city management information formats are unified through the information standardization processing device, standardized information such as traffic, population, economy and energy is accurately rendered to corresponding preset data layers through the multi-dimensional scene rendering device, and finally the layers are overlaid on a unified three-dimensional scene basic model. The barrier of'data islands' among different departments is effectively broken, and deep integration and space coupling of cross-domain data are realized.

The application discloses a street tree missing plant rapid continuous detection method and system based on dynamic edge calculation. The method comprises the following steps: acquiring images, GPS coordinates and time stamps, and constructing a space-time trajectory; pre-processing the images, identifying and classifying vegetation, municipal facilities and moving shelter targets, and outputting feature information and confidence score to obtain image recognition results; based on the image recognition results, filtering street trees close to lane lines and predicting corresponding positions, constructing motion trajectories through continuous multi-frame data association, calibrating in a coordinate system to generate a predicted attention area, and forming a street tree position prediction result; and comprehensively judging and classifying the missing plant situation in the green belt based on the street tree position prediction result, and generating relevant event information. Through the implementation of the method, the limitations of the prior art can be overcome, more efficient and accurate technical support can be provided for the daily maintenance of urban greening, and the quality and efficiency of urban management can be improved.

The present application relates to the technical field of city management, and particularly relates to a city interest point co-location pattern mining method and device. The present application uses a topic model to identify city functional areas based on the mobile trajectory of city residents; in the case of a given city road network, each POI is projected onto the road network, the distance between POIs is calculated by finding a shortest connection path, and the distance is stored in a distance matrix to establish a network distance matrix between city POIs; based on the network distance matrix between POIs in the city road network, co-location pattern mining of POIs is carried out within various functional areas to identify co-location patterns. Compared with traditional co-location pattern mining, the present application takes into account the existence of spatial heterogeneity, and co-location pattern mining is carried out on the premise of functional area identification, which more comprehensively and accurately reflects the actual situation.

The invention relates to the technical field of urban infrastructure management, and discloses an urban infrastructure intelligent supervision method and system based on digital twinning, and the system comprises an urban infrastructure recognition module, an urban infrastructure diagnosis module, and an urban infrastructure feedback module. According to the urban infrastructure supervision node object entity model information and the target urban infrastructure supervision node normal three-dimensional entity model information, urban infrastructure supervision node facility hardware structure abnormity scientific dynamic diagnosis is carried out, and urban infrastructure hardware external structure digital inspection is realized; the abnormal entity model identification data of the target city infrastructure is transmitted to the city management platform on line through the Internet of Things communication network and is displayed and output in cooperation with the display screen, city infrastructure supervision based on digital twinborn visualization is achieved, and the intuition and applicability of city infrastructure supervision are improved.

We provide the system. [Solution] A means including multiple artificial intelligences that collect data related to urban management and perform analyses on energy, transportation, and the environment based on said data, A means for conducting virtual meetings among multiple artificial intelligences, integrating urban management information, and generating an optimal management plan, A means for automatically executing urgent management instructions based on the generated management plan, A means of notifying the user of the results of the execution and enabling them to make further decisions, A system that includes this.

This invention belongs to the field of urban management technology and discloses an intelligent video acquisition, analysis, and management system and method for urban management. It includes a video acquisition module for acquiring video data from various urban areas and monitoring the status of the acquisition equipment; an intelligent analysis module connected to the video acquisition module for analyzing the video data, identifying urban management-related abnormal events, and outputting analysis results; a data management module connected to both the video acquisition module and the intelligent analysis module for storing various types of data generated during system operation, supporting data retrieval, updating, and export; an event processing module connected to both the intelligent analysis module and the data management module for receiving abnormal event analysis results, generating alarm records, and supporting event linkage processing; an inspection management module connected to the data management module for creating and monitoring urban inspection tasks, recording inspection results, and generating reports; and a system management module connected to each module.

This invention discloses a method, system, device, and medium for predicting urban population density, relating to the field of urban management. The method involves: constructing a static population distribution benchmark model; acquiring traffic congestion data for various road segments in the city; calculating the congestion index of each grid unit at the current time based on the traffic congestion data; calculating the standardized deviation of the congestion index from a historical congestion benchmark; calculating a dynamic change representing the effective integration degree of each grid unit based on the standardized deviation and a preset proportionality coefficient; and superimposing the dynamic change onto the weighted spatial integration degree in the preset static population distribution benchmark model to predict the real-time population density of each grid unit. Therefore, by implementing this invention, the population density of a city can be accurately predicted, providing high-timeliness and high-precision population distribution data support for urban management decisions such as urban planning, traffic management, and emergency response.

This invention discloses a data collector assessment system based on motion trajectory feature extraction, belonging to the field of smart urban management technology. It includes: a trajectory mapping module, which maps the location points uploaded by the data collector's terminal to patrol grids and road segments, generating a grid patrol base map, a road patrol base map, and a two-dimensional patrol base map containing patrol items; an evidence collection and arrangement module, which associates and arranges the corresponding grid patrol base map and road patrol base map to generate a patrol evidence collection record set; and a joint verification module, which uses the two-dimensional patrol base map as a trajectory constraint carrier and the patrol evidence collection record set as an on-site evidence carrier to perform joint verification of the data collector's patrol behavior, generating patrol trajectory features and a shift patrol record set. This invention extracts patrol trajectory features and performs problem merging, unifying the patrol authenticity, on-site confirmation, evidence consistency, and processing continuity into the shift assessment results.

The invention relates to the technical field of city management, in particular to a digital management system based on city update project propulsion nodes, which comprises a node activation joint control module, a state cross check module, a path structure adjustment module, a field crossing retrieval module and a risk identification module. In the method, a state identification mechanism is established by combining field cross analysis in task state identification, behavior deviation identification is realized by comparing a node state with a task completion condition, and a stage process number and a responsibility role field are introduced during path archiving to construct a path structure. When the field information appears among a plurality of discontinuous nodes and process stages and task attributions are inconsistent, marking the field information as a path-crossing field, identifying field risk nodes and forming a risk prompt identifier; and the effects of improving the node task execution consistency, the document filing path clearness and the field information abnormity identification capability in the city updating project are achieved.

The invention provides a city service business system construction method based on AI event driving, and belongs to the field of city service business. According to the technical scheme, the method comprises the following steps: S1, constructing an event sensing layer; s2, AI event identification and analysis; s3, constructing an event-driven engine; s4, the business module performs cooperative processing; and S5, event processing feedback and optimization. The beneficial effect of the invention is that the city management efficiency is greatly improved through intelligent perception, real-time analysis and cooperative processing.

The invention discloses a smart city environment health and safety risk prediction system and method based on multi-modal perception, and relates to the field of smart city management, artificial intelligence and data fusion domains. The system comprises a heterogeneous data acquisition module used for acquiring and standardizing multi-modal heterogeneous data including time sequence environment data, space security and protection data and graph structure health association data in city management; the heterogeneous data field alignment module is used for carrying out cross-domain distribution alignment and modal-level feature calibration on the standardized multi-modal heterogeneous data; the joint representation learning module is used for coding and fusing the aligned and calibrated time sequence, space and graph structure features and outputting a fused feature vector; and the AI large-modulus prediction module is used for carrying out classification or regression prediction on environmental health and safety risks according to the fusion feature vector. Therefore, the problems of difficulty in cross-domain alignment, poor fusion effect, weak prediction model generalization ability and the like are solved, and the accuracy and robustness of the risk prediction task are improved.