222 results about "Urban management" patented technology

The invention relates to the technical field of data processing, and discloses a data processing method and system for a smart city and a storage medium. The method comprises the following steps: acquiring city multi-source data through a distributed sensor network, and performing data cleaning and standardization processing to obtain standardized city data; establishing a domain concept system for semantic mapping to obtain semantic association data; analyzing the spatial-temporal characteristic and regional interaction relationship to obtain dynamic characteristic data; constructing a multi-level abnormal feature library for event recognition to obtain abnormal mode data; carrying out distributed optimization decision making to obtain management decision data; and establishing an evaluation system for strategy optimization to obtain a city management scheme. According to the method, efficient processing and intelligent analysis of the city data are realized, specifically, through a distributed computing architecture and a multi-level data processing method, the problems of low data processing efficiency, insufficient data association analysis, low computing resource utilization rate and the like are solved, and thus the overall performance of smart city data processing is improved.

The invention relates to the field of distributed energy, in particular to a distributed energy intelligent allocation method based on Internet of Things big data in a smart city, which comprises three steps of data acquisition, processing and output. Firstly, photovoltaic, energy storage and charging pile real-time data are obtained through a multi-protocol analysis engine, and meteorological cloud charts, traffic thermodynamic diagrams and building energy consumption historical data are combined. And secondly, constructing a dynamic weight model by utilizing a space-time joint attention mechanism, generating a global wind and light output prediction network through federated learning, constructing an elastic scheduling strategy library by adopting a multi-target dynamic game algorithm, optimizing a scheduling model on a digital twin platform, and finally generating a real-time scheduling scheme through a two-stage increment optimization architecture. The method effectively improves the efficiency, economical efficiency, reliability and environmental protection property of an energy system, provides a powerful tool for urban managers, guarantees stable, economical and clean energy supply for residents, and promotes sustainable development of smart cities.

The invention discloses a low-carbon city subjective and objective mixed planning interval entropy weighting evaluation method, and relates to the technical field of city management and low-carbon evaluation, and the method comprises the steps: collecting and preprocessing index data in real time, building a user behavior prediction model according to the index data, and predicting the index data of a user; dynamically adjusting weight distribution of subjective and objective evaluation indexes based on the predicted index data, and constructing a comprehensive evaluation model to calculate a comprehensive score of each region in the city; and formulating optimization measures of each region according to the comprehensive score. A prediction model is established by using a support vector machine, an evaluation result is highly matched with real-time data through dynamic weight adjustment, the hysteresis problem of traditional static evaluation is avoided, and more accurate and flexible low-carbon city evaluation is realized through dynamic adjustment of weight distribution and construction of a comprehensive evaluation model. And the comprehensive score of the urban area is calculated by adopting a nonlinear transformation function, so that the marginal effect change of the evaluation index under different load conditions can be better reflected.

The invention discloses an intelligent city management information visualization comprehensive management method and system, particularly relates to the technical field of city management, and is used for solving the problem of misalignment of emergency response caused by false association and causal confusion in the prior art. Precise management is realized through multi-modal time sequence data feature alignment, causal chain network dynamic construction and a visual decision mapping technology, firstly, multi-dimensional event features of video monitoring, sensors and text work orders are extracted, a cross-modal association chain is constructed, interference chains are screened out through causal strength evaluation and anti-fact verification, and a visual decision mapping algorithm is established. According to the method, dynamic correction parameters are generated in combination with space-time weights and feedback priorities, finally, a visual guidance scheme is output by fusing a causal chain network and a geographic information system, decision optimization is driven through causal logic, and city event disposal accuracy and cross-department cooperation efficiency are remarkably improved.

The invention relates to the technical field of urban intelligent management, and discloses a cloud service-based software-defined urban component internet-of-things control method and system, and the method comprises the steps: constructing an urban component digital twin model, and fusing multi-source data to generate a state feature vector; predicting resource demand distribution by using a graph neural network prediction model in combination with environment and user behavior data; a reinforcement learning algorithm is adopted to optimize a network strategy, and cross-layer protocol adaptation and time delay constraints are considered; deploying an edge cloud cooperative control mechanism to ensure state synchronization; and constructing protocol conversion middleware to realize heterogeneous device protocol adaptation. According to the method, accurate state perception, reasonable resource allocation, efficient network operation, edge cloud collaboration and heterogeneous equipment compatibility of the urban components can be realized, the intelligent management level of the urban components is effectively improved, the urban operation efficiency and the resource utilization rate are improved, and the method has a good application prospect.

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 field of smart city management and information security, and provides a federated learning CIM system information security protection method based on a block chain and a TEE, and the method achieves the security sharing and cooperative computing of multi-party data through combining the data tamper resistance of the block chain technology and the security computing capability of the TEE, and improves the security of the multi-party data. And the data privacy and the security of the model training process are ensured. According to the core technical scheme, the method comprises the steps of deploying an intelligent contract in a block chain network, and managing data access permission; local model training and security aggregation are carried out in a trusted execution environment, and global model parameters are protected through a differential privacy technology. The method can be used for traffic management, environment monitoring, energy optimization and other scenes in a smart city, and provides efficient, safe and intelligent decision support for a city management system.

The invention relates to the field of smart city management, and provides a smart city operation data management system and method, which adopts a deep learning-based data processing and analysis technology to understand the time sequence change of the traffic flow of an intersection by performing time sequence analysis on the time queue data of the traffic flow of a target management intersection. And the time configuration of the traffic signal lamp is dynamically adjusted based on the time sequence change of the intersection traffic flow. Therefore, the traffic signal lamp time can be scientifically and reasonably configured according to the actual traffic flow, and the operation effect of an urban traffic network can be optimized.

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 provides an AI-based smart city monitoring system and method, and relates to the technical field of smart cities, and the system comprises a data analysis module which is used for carrying out the analysis of a city monitoring data flow according to a double-flow network, extracting structured data, and carrying out the correlation analysis, so as to obtain a correlation analysis result; the processing module is used for processing the structured data to obtain an event abstract and a statistical analysis report; and the abnormity monitoring module is used for monitoring and predicting abnormal events including urban pipe network faults, traffic congestion and public safety events in real time according to the analysis result output by the processing module, and sending out an early warning signal. According to the invention, through integration of data analysis, processing, abnormity monitoring and dynamic decision-making functions, intelligent management of city operation states is realized, and city management efficiency and response capability are improved.

The invention discloses an urban construction intelligent monitoring system integrating geographic information and the Internet of Things technology, and belongs to the technical field of urban construction. In order to solve the problems that response to urban problems is lagged and development imbalance among urban areas is difficult to effectively reveal and solve, environment monitoring and infrastructure state evaluation are fused, the intelligent degree, real-time monitoring and quick response of urban management are remarkably improved, the processing speed of the urban problems is effectively improved, and the urban management system is suitable for urban management. The urban area is comprehensively evaluated through the evaluation index system, meanwhile, policy making is more refined through the area grading function, data support is provided for resource allocation, the timeliness of information is kept through continuous data updating, and the evaluation result is combined with a G IS system, so that the system is more efficient and reliable. The problem area and the dominant area are visually displayed, the balanced development of a city is promoted, and the overall living environment quality is improved.

The invention discloses an intelligent planning method and system for municipal drainage facilities, and relates to the technical field of municipal drainage planning. An intelligent planning system for municipal drainage facilities comprises a data acquisition module, a drainage modeling module, a settlement analysis module, an optimization modeling module, a cost performance evaluation module, a dynamic feedback module and a result output module. According to the method, a multi-objective optimization function including drainage capacity improvement, settlement risk control and transformation cost control is set, a non-dominated sorting genetic algorithm is introduced, an optimal solution set is efficiently generated, and urban planning requirements of different budget and performance requirements are met; by constructing a cost performance scoring mechanism, quantitative evaluation and graded output are performed on the generated scheme, basic-enhanced-optimal multi-grade reconstruction suggestions are provided for urban managers, and the flexibility and scientificity of decision making are enhanced.

The embodiment of the invention provides an urban green land community ecosystem regulation service optimization factor identification method and device based on a fusion algorithm and electronic equipment, and belongs to the technical field of ecological data processing, and the method comprises the steps: collecting a plant community, an elevation model and meteorological monitoring data to form a basic data set; uniformly quantifying the data and evaluating the ecological service in combination with the measurement and calculation model to form a standardized data set; inputting the features into a plurality of machine learning models, and screening out a key feature set through operations such as guided aggregation and the like; constructing an IOFESRM-LMEM model, outputting an optimal model through a series of operations, and analyzing key factors; and finally, outputting an optimization result, a key factor and an evaluation result by using a multi-dimensional index evaluation model. According to the invention, data support is provided for improving the ecological function of the urban green land, formulating ecological planning and optimizing urban management.

The invention discloses a city update action visual evaluation method and system, and relates to the technical field of city management, and the method comprises the steps: connecting a city management system, obtaining city monitoring data, constructing a city distribution diagram, including a three-dimensional distribution diagram and a space group composition, determining the evaluation demand of city update planning, carrying out the hierarchical analysis, and determining a demand hierarchical system. The city evaluation module is supervised and trained; monitoring data of a first city updating stage are obtained and transmitted to the city evaluation module, the city distribution diagram is updated and evaluated, and an evaluation result is determined. The method and the device are used for solving the problems that in the prior art, the comprehensiveness and the dynamic adaptability of city updating evaluation aspects are insufficient; and the evaluation accuracy of the city updating action is limited. Urban updating evaluation is carried out by combining the three-dimensional distribution map and the demand hierarchy system, so that the comprehensiveness and dynamic adaptability of evaluation are improved, and a scientific, dynamic and comprehensive evaluation tool is provided for urban updating.

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 present invention discloses a method and device for evaluating a multi-modal large model for urban governance based on multi-level texts, comprising the following steps: obtaining the true label text of a sample picture and the output text of the multi-modal large model for urban governance; using an urban management thesaurus to segment the text into independent words and calculating the similarity score of lexical units; using the urban management thesaurus to create a bag-of-words model to convert the text data into a word frequency matrix and calculating the low-level word frequency similarity score; using a trained language model to extract semantic features with context in the text and converting them into vector representations, and calculating the semantic similarity score; using a general large language model with a formatted request with business guiding words and evaluation requirements to obtain the high-level semantic similarity score corresponding to the text; and combining the similarity scores at each level to calculate the comprehensive evaluation score of the multi-modal large model. The present invention fully considers the similarity measurement in the multi-level space of texts, improving the accuracy of evaluating the text output of the urban governance large model.

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 relates to the technical field of passenger flow information acquisition, and discloses a passenger flow information acquisition system and method based on multiple scenes, and the method comprises the steps of data acquisition, user classification, real-time analysis, regional analysis and information display.In the aspect of crowd flow analysis, mobile signaling data are accurately acquired, and timestamps and base station information are combined, so that the crowd flow analysis efficiency is improved; high-temporal-spatial-resolution analysis is realized, a crowd real-time flow diagram can be accurately generated, and urban traffic planning, large-scale activity security and the like are assisted; and in area evaluation, key inflow and normal flow areas are determined, a key area map is generated, and a basis is provided for commercial layout and public facility construction. User classification is accurate, and tourism management and local public service optimization are facilitated. An analysis result is visually presented through visual display, scientific decision making is assisted, and the method is suitable for multiple fields such as urban emergency management; a real-time updating mechanism ensures the timeliness of data, and improves the flexibility of city management and the capability of dealing with emergencies.

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 relates to the technical field of smart cities, and discloses an urban life entity full-cycle smart management digital system, which comprises a multi-source data acquisition module, an edge computing node module, a data transmission and integration module, an intelligent analysis and prediction module, an intelligent decision support and feedback module and a system integration and user interface module. The multi-source data acquisition module is used for acquiring environment data, traffic data and infrastructure data of urban operation states through different types of sensors, cameras, mobile devices and unmanned aerial vehicles; and the edge computing node module is used for carrying out data localization processing and pre-analysis at a position close to a data source. Through the standardization and semantic integration technology of multi-source data, the common problems of data islands and heterogeneous data in city management are effectively solved, and the system realizes cross-department and cross-platform data sharing and fusion by utilizing the ontology and a data mapping tool, so that the interoperability among different systems is improved.

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 provides a pollution source VOCs emission accounting method and system based on electric power data analysis, and relates to the technical field of city management, and the pollution source VOCs emission accounting method comprises the following steps: S1, obtaining a basic data set of an enterprise in a grid based on a grid matching strategy; the basic data set comprises release time, and VOCs monitoring data, meteorological data and electric power data collected within a moment T; s2, mining data features of the release time, the VOCs monitoring data, the meteorological data and the electric power data, and integrating the data features into the basic data set to obtain a feature data set of an enterprise in the grid; and S3, based on a model fusion strategy of a linear regression model, a Light GBM model and a neural network model, estimating the VOCs at the moment T according to the feature data set to obtain VOCs estimation data at the moment T.

The invention discloses an unmanned aerial vehicle urban management scene patrol inspection system and method based on geographic information, and the system comprises an event processing module, a task management module, a route planning module, an unmanned aerial vehicle flying module and a three-dimensional modeling module, and achieves the intelligent planning of an unmanned aerial vehicle urban management patrol inspection task in combination with the geographic information. Through fusion of urban management inspection business process, task multi-mode issuing, route self-defined planning of a three-dimensional map and task scene management, high fusion of unmanned aerial vehicle operation and business management is realized, a whole-process closed loop is formed, the unmanned aerial vehicle is ensured to complete tasks in an optimal state in different scenes, and the working efficiency of the unmanned aerial vehicle is improved. And the flexibility and adaptability of task issuing are greatly enhanced. Through the spatial geographic information of the three-dimensional map, the attitude perception capability of obstacles in route planning is enhanced, the flight risk is reduced, the safety risk in the flight process of the unmanned aerial vehicle is greatly reduced, and the patrol inspection effect can be further optimized.

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 information processing, in particular to a sponge city-oriented multi-source data early warning method and device, and the method comprises the steps: collecting simulation data of water seepage simulation early warning of a cloud platform and rainfall data of each type of city block; wherein the simulation data comprises urban overground layout data and urban underground layout data; determining the rainfall bearing limit of each city block according to the simulation data, the rainfall data and city management data of a multi-source early warning interface; according to the rainfall bearing limit of each city block, determining the single-day water seepage early warning times of each city block in accordance with the range rainfall permissible state; and according to the simulation data and the single-day water seepage early warning times of each city block, allocating associated simulation water seepage early warning to each city block. According to the invention, the processing time consumption for the multi-source data early warning of the sponge city can be saved, and the multi-source data early warning efficiency of the sponge city is improved.