128 results about "Smart city" patented technology

This application provides a method for configuring video stream algorithms, comprising: extracting visual features, spatiotemporal features, and semantic features of the input video stream; fusing these features to generate a first scene feature of the video stream; and generating a configuration algorithm for the video stream based on the first scene feature and a pre-constructed knowledge graph representing the relationship between scene features and algorithms. The technical solution of this application can effectively solve the problems of high configuration cost, poor scene adaptability, knowledge transfer gaps, and low configuration efficiency of video stream algorithms in smart city scenarios in existing technologies.

The application relates to a smart city-based urban planning decision feasibility analysis system, aiming to solve the problems of experience dominance, static solidification, appeal imbalance and technology isolation in traditional urban planning. The system includes four modules: data collection, algorithm processing, decision output and dynamic optimization. The data collection module dynamically collects urban basic space, dynamic monitoring, industry and livelihood data and completes preprocessing; the algorithm processing module realizes spatial structure quantization, resource allocation optimization, planning risk assessment and dynamic feasibility prediction; the decision output module generates a planning scheme containing land use layout, energy configuration and public service facility distribution parameters; the dynamic optimization module updates and threshold triggering mechanism, iteratively adjusts the planning scheme. The system is suitable for new area planning and stock area renovation scenarios, realizes the transformation of planning decision, and improves the scientificity and landing effect of planning decision.

This invention discloses a method for probabilistic quantification of mixed states in urban functional areas by integrating multi-source heterogeneous data, belonging to the fields of geographic information science, smart cities, and artificial intelligence. The method includes: S1, constructing a traffic analysis area; S2, acquiring multi-source data; S3, preprocessing the multi-source data acquired in S2; S4, generating true probability distribution labels; S5, extracting socio-semantic features; S6, extracting dynamic activity features; S7, extracting physical scene features; S8, feature fusion and distribution prediction; S9, model training; and S10, determining the mixed state and outputting the results. This invention achieves refined quantification of the mixed states of urban functional areas, outputting a continuous probability distribution vector rather than a single category label, significantly improving the information density and application value of the recognition results.

This invention relates to the field of smart city lighting facility operation and maintenance, and discloses a method and system for assessing the health status and predicting faults in street light networks. The method includes the following steps: First, energy domain and information domain data of street light nodes are collected. A dynamic topology map with isomorphic information and energy domains is constructed based on electrical waveform correlation and channel characteristic similarity. Node features are reconstructed using a graph attention network, and passive monitoring residuals are calculated. Based on the dual-threshold judgment results of the residuals, when a node falls into a suspected grayscale range, an active micro-perturbation excitation mechanism is automatically triggered, breaking the steady state by issuing a fine-tuning light command. Subsequently, the degree of anomaly in the circuit physical layer is quantified by comparing the integral deviation between the actual physical response and the ideal response based on the transfer function. Finally, the passive residuals and active deviations are dynamically fused to output a full lifecycle health index. This invention solves the problem of difficulty in identifying early, weak faults under complex noise conditions, improving the accuracy of predictive maintenance of street light networks.

We provide the system. [Solution] A means of receiving and storing individual medical history information, A means for generating medical proposals using generative artificial intelligence based on the received information, A means for providing additional information regarding the generated medical proposal and for generating a response to an inquiry, A means of managing citizens' health information and providing optimal treatment plans through integration with mobile devices, A means of providing the latest medical and facility information in smart cities, A system that includes this.

This invention provides a multi-protocol smart IoT gateway device, solving the problems of traditional gateways such as limited functionality and protocol support. It utilizes a MIPS 1004Kc dual-core chip for computing and integrates WiFi 6E, LoRa, and 5G modules to achieve multi-protocol convergence. The software is based on OpenWRT, featuring intelligent adaptive protocol stack dynamic parameter adjustment, encryption and authentication for security, and intelligent channel and power adjustment to optimize communication and energy consumption. It is suitable for smart cities, industrial IoT, and smart homes, providing a stable and reliable communication solution.

This invention discloses a deep learning method and system for generating trajectory data that supports heterogeneous data fusion, belonging to the fields of smart cities and smart transportation. The invention first acquires multi-source heterogeneous data such as GPS and traffic monitoring video, and performs a preprocessing process. Then, using a divide-and-conquer strategy, an attention-based RNN model predicts pedestrian trajectories, and a fusion model combining macroscopic traffic flow and GNN predicts vehicle trajectories, introducing multimodal features to generate a high-precision set of predicted trajectories. A spatiotemporal database is constructed to create a composite index for each trajectory. Finally, a digital twin system integrating these prediction results is built to achieve dynamic rendering and evolution. This invention effectively solves the problem of multi-source heterogeneous data fusion and significantly improves trajectory prediction accuracy.

A method for secure aggregation, by a server, of client-provided inputs includes receiving, from each of a plurality of clients, a respective client input, for which a commitment is published. The commitments were computed using randomness and are aggregated by at least two super-clients and a sum of the aggregated commitments is published by each super-client. A sum of the received client inputs is published such that validity of the sum is checkable, by the clients, by comparing the sum of the received client inputs to a verification algorithm result that uses a sum of additive shares computed by the clients using the randomness, and by verifying that the published sum of the aggregated commitments is the same for each super-client. The method can be applied to use cases, for example, in digital medicine using medical data or smartcity applications to support decision-making.

1. Name of the product in this design: Emergency Broadcast Active Release Equipment (High Reliability Terminal). 2. Application of this design: It is used for emergency broadcasting and can also be applied to new urban scenarios such as safe city construction, 5G construction, video surveillance, smart transportation, smart parking, video surveillance, and smart city governance. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The application relates to the technical field of geographic information and data storage, and discloses a high-performance map storage updating method, which solves the problems in the background art, and comprises the following steps: dividing map data into a static layer and a dynamic layer, adopting column storage for the static layer, and adopting an in-memory mapping file for the dynamic layer; adopting Z-Order curve coding for the static layer data in combination with Delta compression, and using Snappy compression for the dynamic layer data and merging continuous frames based on time sequence similarity; adopting a multi-version concurrent control mechanism, associating a version chain with each data block, generating a new version for an updating operation, and appending the new version to the tail of the chain; and constructing a space-time joint index, and storing index partitions in distributed nodes. Through hierarchical storage, adaptive compression, distributed version control and joint indexing, the application effectively reduces storage overhead, improves incremental updating efficiency and space-time query performance, and is suitable for large-scale map data management scenes such as automatic driving and smart city.

This invention discloses a mobile edge computing method based on joint optimization of container image layered caching and service migration, belonging to the field of mobile edge computing technology. This method leverages the shared characteristics of image layers to cache the image layer, reducing redundant storage and image retrieval overhead. Simultaneously, it dynamically adjusts the caching strategy in conjunction with service migration decisions, jointly optimizing service migration and caching decisions to reduce bandwidth consumption and improve storage resource utilization. This method can effectively optimize resource allocation in mobile edge computing systems, reduce system overhead, and has wide applications in smart cities, the Internet of Things, and large-scale data processing.

The embodiment of the present specification provides a smart city shared bicycle delivery and operation area planning method and an Internet of Things system. The method is executed based on a management platform of the smart city shared bicycle delivery and operation area planning Internet of Things system. The method comprises the following steps: obtaining reference information of human flow of at least one target area, determining predicted human flow of the at least one target area at a target time based on the reference information of human flow, and determining a shared bicycle demand based on the predicted human flow; obtaining shared bicycle historical data of a plurality of reference areas, predicting a shared bicycle change amount of the at least one target area at the target time based on the shared bicycle historical data; and determining a shared bicycle delivery amount of the at least one target area at the target time based on the shared bicycle demand and the shared bicycle change amount.

This invention presents a threshold-free, privacy-preserving federated learning poisoning attack defense method, addressing the technical problems of intermediate information leakage, difficulty in distinguishing between benign and malicious updates, and difficulty in adapting to high-attack-ratio scenarios. The implementation includes: initialization; training and uploading parameters; secure K-means clustering on the server and threshold-free filtering based on benchmark gradients; server aggregation and dynamic benchmark updates; and updating the number of clusters. This invention uses a multi-party secure computation extension protocol to protect client gradient privacy in cluster-based federated learning poisoning attack defense. It designs a robust defense strategy of multi-cluster partitioning and combination filtering, performs secure K-means clustering, and securely compares different cluster combinations with server benchmark gradients. It does not require a preset malicious user ratio threshold and adaptively filters out benign update sets, achieving a balance between security, robustness, and scalability. It is applicable to federated learning systems involving a large number of heterogeneous entities, such as smart cities and connected vehicles.

This invention belongs to the field of smart city information technology, specifically relating to an adaptive operation method for industrial robots in elevator installation and maintenance. It covers the entire process of industrial robots, from elevator scene perception and operation planning to safe execution. Employing core algorithms such as multi-dimensional feature modeling of the elevator car / shaft, scenario-based adaptation of installation and maintenance operation trajectories, and dynamic early warning of human-machine-equipment safety interaction, it overcomes the technical bottlenecks of traditional elevator operation robots, which suffer from poor scene adaptability, low operation accuracy, and insufficient safety control. This achieves accurate perception and modeling of elevator scenes, dynamic optimization and adaptation of operation trajectories, and real-time early warning and control of safety risks, significantly improving the efficiency, accuracy, and reliability of elevator installation and maintenance operations. It is suitable for the full-scenario operation needs of various types of elevators, including residential, commercial, and high-speed elevators, for installation, commissioning, inspection, and maintenance.

The application discloses a kind of city power grid intelligent modeling method and device based on retrieval enhancement big model and OSM data, it is related to wisdom city and power system planning technical field.The method includes: obtaining OSM power data and carrying out geometric pretreatment and knowledge base rule preliminary screening;Introduce retrieval enhancement big language model with local cache acceleration mechanism, carry out semantic normalization and topological relationship intelligent extraction to candidate elements;By constructing KDTree space index and fusing city POI data, the real building physical load is accurately calculated and mounted to power grid node;Using the consistency checking feedback module with physical constraint, the topological structure is automatically checked and iteratively modified, to generate high-fidelity standardized power grid public information model.The application deeply cooperates big model semantic reasoning and GIS space physical engine, the topological model generated not only has structure connectivity, but also contains real physical load characteristics, greatly improves the accuracy and reliability of city power grid simulation.

The invention relates to the technical field of smart cities, in particular to an IOT-based eCityOS city infrastructure digital intelligence platform, which comprises an Internet of Things equipment layer, an edge side layer, a center side layer and a business application layer, the Internet of Things equipment layer comprises a plurality of Internet of Things equipment; the edge side layer comprises a plurality of edge controllers, and each edge controller is used for collecting data of the Internet of Things equipment corresponding to the edge controller and pushing the collected data of the Internet of Things equipment to the center side layer; the central side layer comprises a plurality of capability modules and is used for acquiring construction instruction information input by a user and constructing a corresponding business application APP by adopting the capability module corresponding to the construction instruction information according to the first data; the first data comprises data of the Internet of Things equipment obtained by the central side layer and / or a software data source obtained in advance; and the business application layer comprises all business applications APP constructed by the central side layer.

This application relates to the field of smart city technology, and in particular to a method, electronic device, readable storage medium, and computer program product for the dynamic configuration of urban service facilities based on multi-source data. The method includes acquiring multi-source data of a target area; generating a human-scale spatial quality score based on dynamically sensed data, obtaining the spatiotemporal distribution characteristics of residents' activities, and calculating the coupling coordination degree between residents' behavior and the spatial environment; constructing an index system of built environment influencing factors at the urban scale based on static spatial data, and identifying key factors and their impact thresholds affecting spatial quality using the human-scale spatial quality score as the target variable; delineating service areas of different levels of living circles based on the spatiotemporal distribution characteristics of residents' activities, coupling coordination degree, key factors, and impact thresholds, and generating optimized configuration schemes and dynamic allocation instructions for public service facilities. This enables quantitative diagnosis of spatial quality and dynamic optimization of facility configuration.

Disclosed are a method and an IoT large model system for smart city elevator operation, and a storage medium. The method includes: determining an anomaly feature of a target elevator based on a load and an elevator feature of the target elevator through an anomaly database corresponding to the target elevator; determining an elevator operation risk of the target elevator based on the anomaly feature; determining a passenger posture feature and a passenger distribution feature based on elevator car image data of the target elevator, and determining a passenger behavior risk; determining an out-of-control risk of the target elevator based on the elevator operation risk and the passenger behavior risk; generating an elevator regulation instruction for the target elevator based on the out-of-control risk; and controlling an operation speed of the target elevator based on the elevator regulation instruction.

This application discloses a roadside data mining method and system, solving the problem that integrated vehicle-road-cloud transportation systems cannot integrate data from vehicles and roadside infrastructure. A roadside data mining method includes the following steps: acquiring first information and second information; the first information includes roadside sensing information acquired through roadside sensing devices; the second information includes terminal status information acquired through information and communication networks; determining the spatiotemporal information of traffic participants in the first and / or second information; generating a traffic participant feature attribute set, the traffic participant feature attribute set including the number, type, and spatiotemporal information of traffic participants, and data representing traffic events determined based on the above information. This application integrates roadside and vehicle-mounted data to form a city-level big data platform, supporting the development and optimization of various smart city applications and improving the level of intelligent urban management.

This invention discloses a method for analyzing massive amounts of data in smart cities based on edge computing, and relates to the field of data analysis technology. The method includes the following steps: constructing edge computing task orders based on the original time-series data stream and business processing intentions of the smart city; collecting static and dynamic data from edge computing nodes to generate a global node capability profile set; constructing an edge computing intelligent scheduling model, inputting the task orders and the node capability profile set into the model, and outputting scheduling decision instructions; ensuring the reliable execution of these instructions, and producing edge local analysis results. This invention enables the analysis of massive amounts of data in smart cities based on edge local analysis results.

This invention relates to the field of smart city safety monitoring technology, and discloses a method for multi-source data fusion monitoring of smart city safety risks. The method includes: collecting heterogeneous data from IoT sensor terminals, emergency management systems, citizen alarm platforms, and video surveillance analysis platforms, and encoding them into standardized event description records; performing spatiotemporal alignment within a unified spatial grid and time window framework; calculating uncertainty metrics for each data source based on data timeliness, completeness, and historical reliability; calculating inter-source conflict degree based on the degree of probability distribution overlap, and calculating fusion weights by combining uncertainty factors and conflict attenuation factors to obtain a fused safety state probability distribution; calculating a comprehensive risk score and outputting the safety monitoring results.

The application discloses a kind of based on multi-source data fusion's electric power load flexibility intelligent evaluation method and system, storage medium, equipment, belong to wisdom city and electric power system technical field.Method includes: constructing the multi-source data system including load data, demand response data, user behavior data and equipment operation data;The feature extraction and selection of the multi-source data are carried out, and a multidimensional feature set is constructed;Based on the multidimensional feature set, a fusion model is used to predict the benchmark load;Actual regulating load is obtained, the actual regulating load is identified with the benchmark load, and the multidimensional flexibility evaluation index is calculated;Based on the index, the comprehensive score is obtained by weighted summation, and the intelligent evaluation is completed.The application realizes the deep fusion of multi-source data, improves the comprehensiveness and accuracy of load flexibility evaluation, and can be widely applied to the evaluation of smart city electric power load regulation effect, demand response effect evaluation and other scenes.

This invention provides a cross-modal spatiotemporal event prediction method, system, computer, and storage medium. The method includes the following steps: receiving a multimodal high-level structured semantic feature sequence of monitoring data output from multiple edge nodes; mapping each semantic feature in the feature sequence to a unified embedding space; summing the weighted results of each modality according to a gating fusion mechanism to obtain joint representation parameters; performing average pooling operation on the joint representation parameters corresponding to the time step within the current sliding time window, and inputting the result into a lightweight prediction head to output decision parameters for the probability of event occurrence. By implementing spatiotemporal modeling through a sliding time window and a memory mechanism, the robustness and adaptability of the system are enhanced, detection sensitivity is improved, and false alarms are reduced. This solution, with its low power consumption, low latency, and high accuracy, solves the core bottlenecks of high computational cost, large latency, and poor scalability in existing technologies, providing a sustainable solution for smart city monitoring.

The application relates to the field of traffic supervision, and provides a smart city tunnel traffic emergency supervision Internet of Things large model system and method, the method comprising the following steps: constructing a fusion feature vector of a vehicle in a target tunnel according to multidimensional data of the target tunnel; determining an abnormal event according to the fusion feature vector and congestion data of the target tunnel; predicting an upstream congestion condition of the target tunnel according to the abnormal event, the congestion data and surrounding traffic flow data of the target tunnel; and generating a signal control instruction comprising a passing time length according to the upstream congestion condition, the signal control instruction being configured to control a target signal lamp to maintain a target state for the passing time length, the target signal lamp being a signal lamp on a key road section in a direction leading to the target tunnel. The application can realize a leap from passive control of 'getting stuck at the source' to active dredging of 'avoiding the congestion point', provide clear and feasible alternative solutions for drivers, and significantly improve the overall passing efficiency and emergency disposal capability of a regional road network.

This application relates to the field of information technology and discloses a multi-source heterogeneous data fusion and analysis platform for smart cities. It includes a data acquisition end error calibration module, a transmission link integrity assurance module, a multi-source data fusion and cleaning module, and a dynamic timeliness management module. Each module achieves data flow through a unified data interaction protocol. The acquisition end is used to collect data from traffic cameras and environmental monitoring sensors. The data acquisition end error calibration module is used to calibrate errors generated by the data collected by the acquisition end. The acquisition end error calibration module also includes an environmental adaptive sensing unit and a device health assessment unit. Through the synergistic effect of the environmental adaptive sensing unit and the device health assessment unit, data reliability is improved from the source. The environmental adaptive sensing unit uses a pure hardware electrical signal transmission link to achieve real-time response to changes in visibility and illumination.

This invention discloses a small target detection method and system based on improved YOLOv11 and fused attention, applicable to small target detection in unmanned aerial vehicles (UAVs). The method includes the following steps: constructing a backbone network using a custom module SFBCR; constructing a neck network using a custom module MFBCR to obtain a custom model; and designing a fused attention mechanism based on the SimAM attention module and PartialConv partial convolutional modules. The model parameters are determined by training on the public UAV small target dataset VisDrone. Compared with existing neural networks, the proposed detection method achieves an average accuracy improvement of 2.03% in UAV small target detection applications while maintaining detection speed. It can be used in smart city construction to improve urban traffic management.

This disclosure provides a method for identifying regional risks, a graph neural network training method, a device for identifying regional risks, a computer system, a readable storage medium, and a computer program product, relating to the field of smart city technology. The method for identifying regional risks includes: generating a regional map including the region to be identified, wherein the regional map includes multiple nodes and multiple connecting edges, nodes are used to represent regions, regions are areas where transport vehicles used for transporting goods stop along their routes, and connecting edges are used to represent the association relationship between two nodes; extracting features from the regional map to obtain a target feature vector; and obtaining the risk identification result of the region to be identified in the regional map based on the target feature vector.

The application discloses a kind of high-precision geographic information dynamic surveying and mapping systems of fusing laser radar and unmanned aerial vehicle, including data acquisition module based on unmanned aerial vehicle platform and the multiple sensor load carried synchronous acquisition multidimensional geographic information data, acquisition pre-processing module is used to carry out space-time synchronization and preliminary purification to multi-source sensor data, real-time data processing module is used to carry out real-time solution and analysis to the data after purification, dynamic modeling analysis module is used to generate high-precision real scene model and carry out time series change analysis by fusing multi-source data, visualization application module is used for the three-dimensional display of achievement, interaction and application integration;The application solves the problems of low efficiency, environmental constraints and inability to update dynamically by multi-sensor collaborative sensing and edge real-time processing, realizes all-weather dynamic geographic information acquisition and processing with centimeter-level precision and minute-level response in complex terrain, and is especially suitable for geological disaster monitoring, smart city management and other scenarios.