390 results about "Intelligent city" 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 technical field of edge task scheduling, and discloses a smart city dynamic task scheduling method based on cloud end-to-end collaboration and a storage medium, and the method comprises the steps: training an XGBoost priority prediction model at a smart city cloud control center based on the resource characteristics of historical tasks and the state of end-side equipment, constructing a cloud experience pool through a genetic algorithm, and carrying out the optimization of the cloud experience pool; periodically issuing to an edge node; meanwhile, all the tasks to be distributed are distributed to edge nodes of corresponding jurisdictions in a balanced mode; an edge node constructs an edge experience pool, a cloud experience pool is fused, and a strategy generation model is obtained through near-end strategy optimization training; according to the order of the task priorities predicted by the XGBoost priority prediction model, sequentially inputting the task priorities into the strategy generation model, obtaining target end side equipment of the current to-be-allocated task, and issuing the to-be-allocated task; and after updating the edge experience pool and the strategy generation model in real time based on the distributed task, distributing the next task to be distributed.

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 power enterprise intelligent collaborative office method and system based on artificial intelligence, and relates to the technical field of power informatization. According to the intelligent collaborative office method and system for the power enterprise based on artificial intelligence, request data is received from a user terminal through a deep learning model, a service demand type, an emergency degree and task complexity are identified, a service priority index is generated, classification and priority distribution are carried out, and based on the service priority, the user terminal is subjected to intelligent collaborative office. The method comprises the following steps of: matching associated tasks and resource requirements through a model, retrieving related historical data to optimize a resource configuration path, dynamically tracking task progress, execution state and resource consumption, identifying a bottleneck in an execution process and generating an optimization instruction, and finally, dynamically adjusting model parameters and resource configuration rules through an adaptive learning algorithm. And a closed-loop feedback mechanism is formed. Information flow and resource configuration are effectively integrated, the flexibility and response speed of collaborative office are improved, and the development requirements of smart grids and smart cities are met.

The invention discloses a target detection method, system and equipment based on knowledge distillation and a medium, belongs to the technical field of computer vision, machine learning and artificial intelligence, and aims to solve the technical problem of how to overcome the limitation of the existing knowledge distillation technology in smart city target detection application. According to the technical scheme, the method comprises the steps of hierarchical attention fusion: in combination with local attention and global attention mechanisms, capturing detail information of a target through a high-resolution feature map, extracting overall features of the target through a low-resolution feature map, and carrying out hierarchical attention fusion; a final attention feature map is generated through a self-adaptive fusion strategy, and the focusing capability of a student model on target features is enhanced; carrying out weighted fusion on the fusion strategy in combination with a channel attention mechanism, and further enhancing the expression ability through a lightweight convolutional network, thereby improving the expression ability of a student model for target features; improving a distillation loss function; optimizing a training strategy; and network training.

The invention belongs to the technical field of smart city construction and intelligent construction management, and particularly relates to an intelligent scheduling and cooperation system and method for the whole life cycle of city reconstruction. The system comprises an edge sensing layer, a digital twinning network module, a space-time brain module, a block chain collaborative trust layer and an XR collaborative interaction layer. The system collects multi-source data of a construction site in real time through a 5G edge device, realizes fusion modeling of BIM, GIS and TIN models by using a graph convolutional neural network, and performs multi-target scheduling optimization in combination with meta reinforcement learning and a dynamic graph neural network; and meanwhile, construction task verification and fund payment linkage is realized through a block chain smart contract, and immersive cooperation and visual acceptance are provided in cooperation with an AR / VR platform. According to the method, the problems of model splitting, scheduling response lagging, low data collaboration efficiency, insufficient credibility of the performance process and the like in the urban reconstruction project are solved, and the intelligence, transparency and automation level of the construction process is remarkably improved.

The invention relates to the technical field of smart cities, in particular to an urban climate risk monitoring system based on multi-source data fusion and early warning application thereof, and the urban climate risk monitoring system comprises a data reconstruction module, a precursor identification module, a trend chain extraction module, a grade assignment module and a risk diffusion module. According to the method, the reliability of multi-source meteorological data is effectively enhanced, the precision of data fusion and the reliability of spatial positioning are improved by carrying out credibility level evaluation on a plurality of data sources, and the sensitivity of risk precursor events and the accuracy of factor propagation prediction are enhanced by utilizing deep identification of trend consistency and response delay relation; according to the method, the regional spatial continuity factors are combined, the stability and spatial continuity of risk grade division are optimized, spatial impedance characteristics are brought into diffusion path prediction, accurate deduction of urban climate risk spatial diffusion paths and dynamic marking of risk regions are realized, and the accuracy and dynamic of urban climate risk monitoring and prevention and control decisions are enhanced.

The invention relates to the technical field of smart city data visualization, in particular to a visual smart city display method, which comprises the following steps: S1, collecting traffic flow data, energy consumption data, environment monitoring data and city three-dimensional geographic information data in real time; s2, constructing a reference three-dimensional model with a space coordinate system; s3, mapping the data into a dynamic data layer with a timestamp; s4, superposing the dynamic data layer to the reference three-dimensional model according to space-time coordinates to generate a fused dynamic scene; s5, receiving deduction parameters input by a user, and calculating a corresponding evolution path in real time; and S6, converting the evolution path into a dynamic visualization effect, and outputting the dynamic visualization effect to a display terminal. According to the invention, through integrated processing of multi-source dynamic data fusion, space-time path deduction and visual output, continuous expression and dynamic evolution display of the operation state of the smart city are realized.

The invention provides an intelligent urban municipal planning method and system based on geographic information, and relates to the technical field of data processing, and the method comprises the steps: obtaining urban spatial data of different data sources, including terrain, land utilization, building distribution, traffic network, population distribution and environment index data; and analyzing the urban spatial data, including spatial overlay analysis, buffer analysis and network analysis, and identifying key problems and potential risks in urban municipal planning to obtain a data analysis result. The urban space data can be comprehensively and accurately analyzed, the optimized municipal planning scheme is automatically generated and updated and adjusted in real time, and the urban traffic fluency, the environment quality and the public facility utilization efficiency are improved.

The invention provides a smart city security abnormal event detection method and system, and the method comprises the steps: obtaining an initial large model, and adding a positioning reasoning normal form in the initial large model, so as to obtain an adjustment large model; designing and adjusting a reward function of the large model; performing first training on the adjustment large model based on a reward function and a first training sample to obtain a first training model; updating the reward function to obtain an updated reward function, and training the first training model for the second time based on a second training sample and the updated reward function to obtain a second training model; and inputting to-be-recognized smart city security anomaly input data into the second training model to output an anomaly recognition result, the method has significant advantages in the aspects of target positioning precision, interpretability and training efficiency, can adapt to various city security scenes, and provides important technical support for smart city construction.

The invention relates to a smart city planning monitoring system, which comprises a multi-source data acquisition module, a database construction and management module, a planning analysis and monitoring module, a communication cooperation module and an interactive decision support module, the database construction and management module is used for constructing and managing a database of urban planning and monitoring data, the planning analysis and monitoring module is used for planning scheme evaluation and progress tracking, the communication cooperation module is used for data communication and module cooperation, and the interactive decision support module is used for data interaction, monitoring visualization and decision support. According to the smart city planning monitoring system, a city planning multi-source data fusion algorithm based on kernel density analysis is put forward to fuse multi-source data, and a smart city planning monitoring algorithm based on analytic hierarchy process and fuzzy comprehensive evaluation is put forward to monitor and evaluate smart city planning.

The invention relates to a smart city ESG multi-source data visualization analysis method, device and equipment and a medium, and the method comprises the steps: carrying out the standardized semantic annotation and space-time alignment of city multi-source heterogeneous data, and generating a semantic triple set with a space-time label; the real-time intensity of the causal relationship in the ESG dynamic knowledge graph is constructed and quantified; semantic binding is carried out on the map and the city information model, and a visual descriptor is generated; discovering and rendering a causal relationship chain in the three-dimensional scene based on user interaction; and deducing simulation intervention and optimizing model parameters. According to the method, dynamic and integrated presentation and interactive deduction of complex causal relationships of environment, society and governance in a multi-dimensional space-time scene are realized, and the problems of surface analysis, disjunction of data and space and insufficient decision support in the prior art are effectively solved.

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 provides a pelican optimization strategy-based mobile edge computing unloading method, and aims to solve the problems of low computing task unloading efficiency and poor system performance in application scenes such as Internet of Things and smart cities. By introducing chaotic mapping and a flight strategy, a traditional pelican optimization algorithm is improved, and the global search ability and the ability of jumping out of a local optimal solution of the algorithm are enhanced. A calculation unloading model of a multi-user multi-edge server is constructed, and delay and energy consumption in the data transmission and calculation process are comprehensively considered. Experimental results show that the method can effectively reduce the time delay and energy consumption of the system, optimize resource allocation and improve the overall performance of the system. Especially in a multi-user and multi-task complex scene, the method disclosed by the invention is excellent in performance, and a new solution is provided for the calculation unloading problem in the mobile edge calculation.

The invention relates to the technical field of video monitoring communication, in particular to a 5G high-definition video monitoring method and system for a smart city, and a medium, and the method specifically comprises the steps: obtaining a 5G high-definition video based on the moving features of each feature point of a moving target between each frame and a previous frame image in a monitoring video, and the gray gradient change condition in each channel image of an RGB image of each video frame; constructing an information rich characteristic value of each video frame; calculating a bandwidth influence coefficient of each detection time period based on a data fluctuation abnormal condition of the network bandwidth data in each preset detection time period; constructing a video transmission quality coefficient by combining the features, optimizing a quantization parameter of a video coding algorithm in the current detection time period, carrying out coding transmission on the monitoring video in the current detection time period, and carrying out intelligent analysis on the transmitted monitoring video; the problem of frame loss or low video quality of video content with relatively high potential information value is reduced; and the interference on subsequent video monitoring intelligent analysis and target detection can be reduced.

The invention provides a smart city emergency response method and system combined with artificial intelligence, and the method comprises the steps: firstly carrying out the situation awareness of a city emergency event, generating an event situation map containing spatial-temporal evolution characteristics and influence levels, calling a pre-trained strategy migration model to match historical cases, and outputting a preliminary response strategy set; and dynamically adapting and adjusting the preliminary response strategy set based on the situation map real-time updating characteristics, generating an adaptive response strategy set, carrying out cooperative scheduling analysis on the adaptive response strategy set and the urban emergency resource network, generating a target cooperative scheme matched with resource supply and strategy requirements, and outputting an emergency response instruction containing an execution priority according to the target cooperative scheme. And the information is sent to a corresponding node to trigger a multi-main-body linkage response, so that the scientificity and the high efficiency of the emergency response of the smart city are improved.

The invention discloses an intelligent city planning method and system based on multi-source remote sensing data fusion, and particularly relates to the technical field of city planning, and the method comprises the steps: S1, region division: dividing a planning region into M dynamic management grids based on GIS geographic coordinates and city function zoning data, carrying out the auxiliary recognition of topographic relief and building height through LiDAR point cloud data, and adjusting the boundary of the grids, establishing a bidirectional mapping relation between the grid and the multi-source sensing equipment; s2, multi-source data acquisition: constructing a city holographic data set through global coverage and a multi-modal sensing technology, acquiring target city data in real time, then executing multi-stage cleaning and space-time alignment, and performing normalization operation on each layer of data by adopting a standardized processing mode to obtain city data; according to the invention, an air-sky-ground-network global sensing network and a multi-level space-time grid framework are constructed, satellite remote sensing, unmanned aerial vehicle point cloud, ground sensors and urban operation data are deeply fused, and the limitation of single-source data of a traditional system is broken through.

The invention relates to the technical field of artificial intelligence, in particular to a visual large screen development method and system based on a multi-modal large model, and the method comprises the following steps: a demand analysis step, an intelligent generation step, a visual arrangement step, a configuration management step and an operation support step. The method has the beneficial effects that the system can synchronously analyze natural language instructions, business data parameters and visual reference examples through a multi-modal semantic alignment engine, and a design scheme with both function completeness and aesthetic consistency is generated. In scenes of smart city situation awareness, industrial equipment real-time monitoring and the like, a user only needs to input demand descriptions of'displaying energy consumption trends and abnormal point locations of all regions in recent half a year 'and the like, a complete large screen scheme including a thermodynamic map, a time sequence broken line graph and a 3D alarm mark can be automatically generated, and the development period is shortened by more than 70% compared with a traditional mode.

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 a smart city planning three-dimensional scene reconstruction optimization system combined with semantic segmentation, and belongs to the technical field of three-dimensional scene reconstruction optimization. The system comprises a data sensing module which collects aerial images and laser point clouds, and complements a sheltered area to obtain city modeling data; the preprocessing module performs denoising, data registration and data fusion on the city modeling data to generate texture point cloud data; the semantic understanding module realizes multi-modal semantic segmentation of the texture point cloud data through a fine-tuned SAM network and an improved RandLA-Net, and semantic tag data is obtained through restoration and optimization; the reconstruction optimization module constructs an initial three-dimensional grid model based on the texture point cloud data, and optimizes a ground feature boundary and a missing region of the model in combination with a semantic tag; and the output application module converts the optimized model into a standard format and outputs the model. Through deep coupling of semantic segmentation and reconstruction optimization, the precision of the three-dimensional scene model is improved, and reliable support is provided for smart city planning.

The invention discloses an intelligent video monitoring system and method supporting AI strategy cloud side end cooperative execution. According to the system, unified management of computing power resources and dynamic deployment of an AI algorithm are realized based on an extended standardized protocol through a three-level architecture of a cloud side center platform, an edge computing node and an end side camera. The method comprises the following steps: a cloud platform acquires computing power information of edge equipment; an AI algorithm execution strategy is generated and issued according to service requirements; if the required algorithm is not deployed in the equipment, the equipment is controlled to download and automatically deploy an algorithm module; and finally, the driving equipment executes AI analysis and recovers the structured data. According to the invention, AI computing tasks are reasonably distributed through a cloud side-end cooperative computing architecture, so that the computing power construction cost of a central platform is effectively reduced; through algorithm and hardware decoupling and a standardized protocol interface, ecological binding is broken, on-demand loading and flexible scheduling of the algorithm are realized, and the economical efficiency, the flexibility and the intelligent level of the system in large-scale scenes such as smart cities and the like are remarkably improved.

The invention relates to the technical field of garbage treatment, in particular to a smart city-oriented garbage classification and recognition system, which comprises an image acquisition and restoration module, a multi-modal feature fusion module, a dynamic classification decision module and a classification result output module, wherein the image acquisition and restoration module is used for acquiring and restoring an image of a garbage throwing point and outputting a restored three-dimensional image; the multi-modal feature fusion module is used for fusing the extracted features into multi-modal fusion features through a space-time alignment algorithm; the dynamic classification decision module is used for classifying the parallel double-branch convolutional neural network integrated with the attention mechanism; and the classification result output module is used for triggering a control signal of the garbage recycling equipment. According to the intelligent urban garbage classification system, through collaborative design of multi-modal feature fusion and a parallel classification structure, accurate recognition of garbage materials and purposes in a complex environment is achieved, the recycling equipment can be automatically linked, and the overall intelligent level of the intelligent urban garbage classification system is improved.

The invention belongs to the technical field of smart cities, and relates to a smart city public service opinion feedback multi-dimensional evaluation system, which comprises a data acquisition and processing module for analyzing object contour features in public service original data to generate a structured data pool; the space-time atlas modeling module is used for generating a demand migration feature vector with a weight coefficient; the dynamic scheduling engine module is used for calculating a municipal resource real-time scheduling path according to the demand migration feature vector, and generating an emergency execution scheme when a resource gap value exceeds a preset threshold value; the task execution monitoring module is used for splitting an emergency execution scheme into interactive task nodes, acquiring running state data of an execution terminal in real time, and triggering an automatic replacement path supplement mechanism when detecting that the overheating duration of equipment exceeds a safety critical value; and the feedback optimization evaluation module is used for generating an urban governance health index report containing a compensation scheme and a priority adjustment factor. According to the invention, the service can be recovered only through manual intervention when abnormal events are solved.

The invention relates to the technical field of big data, and discloses a smart city big data multi-source heterogeneous acquisition and convergence fusion method, which comprises the following steps: step A, multi-source heterogeneous data acquisition: performing data acquisition by using multiple data interface protocols; b, preprocessing and standardizing the data, cleaning the data, and performing unified processing on the data; step C, data aggregation and integration: carrying out logic partitioning on the preprocessed data according to a subject domain; d, performing data fusion and semantic understanding, and performing calculation and recognition by utilizing a machine learning algorithm and semantic similarity; e, data quality control; and step F, a data security and privacy protection mechanism. By adopting a distributed data acquisition framework, various data sources in the smart city are comprehensively covered. By means of the design, universality and diversity of data sources are guaranteed, a rich data basis is provided for subsequent data processing, and the efficiency and accuracy of data collection are greatly improved.

The invention relates to the technical field of smart city services, and discloses a city service agent scheduling method and system based on intention recognition, and the method comprises the steps: collecting the multi-modal original request data of a city complex, and carrying out the preprocessing and feature extraction; identifying a multi-level intention, extracting an intention entity and a constraint condition, and evaluating an intention priority; constructing an intelligent agent capability model and a knowledge base, calculating a semantic matching degree between the intention and the intelligent agent, and generating an intelligent agent combination scheme according to a matching result; constructing a multi-objective optimization model and generating a scheduling scheme of a single intention and a composite intention; task issuing is carried out, execution abnormity is processed, fault-tolerant recovery is carried out, task completion quality is verified, and user feedback is collected; intention recognition optimization is carried out, scheduling strategy parameters are adjusted, and intelligent agent capability evaluation is updated; according to the method, the multi-dimensional intention of the user can be accurately understood, and multi-agent efficient collaboration and resource optimization configuration are realized.

The invention relates to the technical field of geographic information systems, and discloses a vector geographic element collaborative updating management method and device and a medium, and the method comprises the steps: a user edits a vector, and generates GeoJSON Delta data; matching the GeoJSON Delta data with a baseline version, and carrying out automatic / manual conflict processing through an attribute rule or spatial topology analysis; after conflict processing is completed, the three-dimensional version tree is updated, and real-time synchronous storage is carried out. The method can be applied to the fields of land investigation, smart cities and the like, and solves the problems of version chaos, frequent conflicts and the like in traditional GIS data updating.

The invention discloses an automatic page generation method of a software construction platform based on a large language model, which comprises the following steps of: extracting a scene, a page and constraint description from a natural language demand, and generating JSON (JavaScript Object Notation) content containing a data model, a query condition, a table column and static data through the large language model; according to component type constraints and verification rules, the JSON is mapped to components and combinations of buttons, labels, texts, pictures, pull-down selection, multi-choice, single choice, tables, dates and the like of the code-free construction platform; and automatically filling a component set based on the upper and lower partition templates, and injecting static data and verification logic. Through industry knowledge adaptation, component semantic binding and dynamic constraint, it is ensured that the page conforms to field specifications and interaction logic, the learning cost is reduced, manual intervention is reduced, and the method is suitable for the fields of smart cities, natural resources, urban and rural residence construction, emergency management, enterprise digital transformation and the like.

The invention discloses a smart park platform planning and designing system, and belongs to the technical field of smart city construction. Multi-source static and dynamic data are integrated through the data acquisition and integration module, and the space function planning module and the dynamic resource allocation optimization module construct an optimization model based on core function requirements and resource requirements, so that scientific planning of space layout and resource allocation is realized; the simulation evaluation display module dynamically evaluates the traffic flow, the environmental influence and the resource efficiency by using a three-dimensional digital park model and a multi-dimensional simulation technology, provides a visual and quantitative evaluation result for a planning scheme, effectively reduces the design risk, combines the planning intelligent auxiliary decision-making module and the user participation feedback module, and improves the design efficiency. Through a rule engine and a multi-dimensional comparison model, an optimal design strategy is recommended for the dimensions of cost, efficiency, environment and the like, meanwhile, user requirements are collected and converted into optimization suggestions, a closed-loop optimization process is formed, and the applicability of a planning scheme and the user satisfaction degree are improved.

The invention relates to the field of smart city management, and discloses a multi-source data fusion and treatment method for a CIM platform, and the method comprises the steps: sorting the current situation of city spatial data, and carrying out the unified access and classification of the data; carrying out unified conversion on the data, and carrying out merging and layer sequence arrangement; carrying out unified space coding on the geographic space object, and constructing a coding rule; the granularity is divided into construction granularity and expression granularity, data are fused through spatial association, and unified expression of multi-scale data is achieved; extracting spatial semantic information, establishing a mapping relation between a unified address and a CIM model, and realizing semantic expression of a place name and the address; constructing a pyramid model, and realizing fusion of the building information model and the CIM; and performing graph falling processing on the government affair data to form a thematic data base plate associated with the CIM model. According to the method, efficient access, unified processing and deep fusion of the multi-source data are realized, and the integration and availability of the data in the CIM platform are improved.

The invention relates to the technical field of smart cities, and discloses a smart city multi-modal data acquisition and fusion method and system. The method comprises the following steps: acquiring smart city system data; performing data cleaning on the smart city system data according to a pre-trained naive Bayesian model, and extracting metadata of data items; performing data standardization according to the metadata of the data item to obtain standardized data information; performing data conversion on the standardized data information according to a pre-formed semantic mapping rule base to obtain machine readable data; merging and compressing the machine readable data based on principal component analysis to obtain dimension-reduced readable data; and performing data fusion on the dimension-reduced readable data according to a preset fusion strategy to obtain fused data information, and storing the fused data information. According to the method, various kinds of heterogeneous data are converted into information which can be understood by a machine, data fusion is effectively carried out, and the availability of the data is improved.