1322 results about "Smart city" patented technology

The present disclosure relates to a blockchain-based authentication system for electric vehicles and drones, and a method for authenticating electric vehicles or drones in a smart city. The proposed blockchain-based authentication system enhances security, privacy, and scalability for electric vehicles (EVs) and drones in smart city environments. It employs a consortium blockchain managed by city authorities and stakeholders, utilizing smart contracts for identity registration, credential issuance, and access control. Device nodes store cryptographic keys securely, while an optional authentication server facilitates off-chain integration. The authentication operation of the system includes, ensuring tamper-proof identity verification through on-chain validation. Authentication attempts are immutably recorded for auditing and anomaly detection. By eliminating single points of failure and strengthening data privacy, this decentralized authentication framework offers a scalable and secure solution for integrating EVs and drones into smart city infrastructures.

A system and method for adaptive edge-cloud data processing dynamically distributes computational tasks between edge devices and cloud infrastructure in response to changing conditions. The system continuously monitors resource availability, network parameters, and workload characteristics while predicting future conditions using hierarchical forecasting models. A multi-objective optimization approach determines optimal task distribution, balancing processing latency, energy consumption, bandwidth utilization, and result quality. The system implements a partitionable processing pipeline that enables seamless task migration through state synchronization protocols and checkpoint mechanisms. During migration, the system preserves processing continuity by establishing dependencies, creating execution checkpoints, and verifying successful state transfer. Performance metrics may be continuously collected and analyzed to improve future decision-making. The system maintains operational resilience during connectivity disruptions through local decision-making capabilities and eventual consistency protocols, making it suitable for diverse applications including industrial IoT, connected vehicles, healthcare wearables, and smart city infrastructure.

The invention discloses a geographic information big data-oriented multi-source data processing system, and relates to the technical field of data acquisition and sensors, and the system comprises a data acquisition module which accesses a remote sensing satellite, an unmanned aerial vehicle, an Internet of Things sensor and social media in real time through a multi-source heterogeneous interface, and carries out the adaptive analysis of a data format and metadata marking; the distributed storage module is used for performing partition storage on the geographic information data based on a space-time database and an object storage architecture, and establishing dynamic space-time index and version control; the data fusion module is used for realizing coordinate system conversion, time sequence calibration and semantic knowledge graph matching based on a multi-source data alignment method of dynamic weight distribution; and an intelligent analysis module and a security control module. According to the method, core pain points such as data splitting, low storage efficiency, extensive analysis and compliance risks in the geographic information field are systematically solved, and a full-stack type technical base is provided for scenes such as smart cities, emergency disaster relief and environment monitoring.

The invention relates to the technical field of unmanned vehicle control, and discloses a smart city environmental sanitation unmanned vehicle path planning and real-time monitoring method. The method comprises the following steps: firstly, acquiring a multi-source environment sensing data set such as laser radar point cloud data, a camera image sequence and real-time traffic flow information; local road network features are extracted based on laser radar point cloud data, a dynamic target motion prediction map is generated according to a camera image sequence, and real-time traffic flow information is processed to generate a regional traffic efficiency evolution curve. And inputting the data into a path optimization model to generate an initial path sequence, dividing cleaning task priorities, fusing related data and generating a final path planning scheme through a reinforcement learning algorithm. In addition, operation state data of the unmanned vehicle are collected in real time, and a path correction instruction set is generated through an anomaly detection model to update the strategy network. According to the method, the rationality and the operation efficiency of the path planning of the environmental sanitation unmanned vehicle can be improved, real-time monitoring is realized, and the operation safety and the management intelligence level are enhanced.

The invention relates to the technical field of traffic management, in particular to a smart city intelligent traffic management system based on big data, which comprises a multi-source data acquisition module, a data fusion module, a dynamic feature generation module, a situation prediction module, a multi-target optimization module and an instruction execution module, the multi-source data acquisition module acquires multi-source data in real time; the data fusion module generates a standardized traffic data flow and outputs a three-dimensional data cube; the dynamic feature generation module outputs a traffic feature matrix; the situation prediction module generates a dynamic situation map; the multi-objective optimization module outputs an optimization instruction set; and the instruction execution module issues a control instruction to the traffic signal controller, the variable information board and the vehicle-mounted terminal. According to the invention, traffic jam can be relieved, road traffic efficiency can be improved, bus priority scheduling can be optimized, vehicle delay can be reduced, and emergency response speed can be improved.

The invention provides an unsupervised video monitoring anomaly detection method and system based on semantic modeling, and belongs to the technical field of computer vision, artificial intelligence and video monitoring. Comprising the following steps: carrying out key frame identification on a monitoring video by adopting an image-text joint embedding model, and carrying out target cross-frame tracking based on a depth target detection algorithm to extract behavior semantic information so as to construct a semantic behavior map; sending the key frame sequence of the graph structure information into a frame prediction model, and predicting a next frame image or a target state; and carrying out abnormal scoring on the obtained prediction result, and carrying out threshold judgment by outputting a comprehensive abnormal score value so as to determine whether the current frame is an abnormal event or not. According to the method, the intelligent level and the overall efficiency of video anomaly detection can be effectively improved on the premise of ensuring the real-time performance and the stability, and support is provided for video monitoring anomaly detection in actual scenes such as smart cities, rail transit, industrial parks and commercial security.

The invention relates to a 5G network slice dynamic scheduling method and system based on multi-modal space-time perception and an event knowledge graph, and belongs to the technical field of mobile communication network resource management. According to the method, the change of a physical scene is sensed in real time by constructing a dynamically evolved event knowledge graph and designing a double-flow space-time cross network in combination with visual semantic analysis; dynamically adjusting the resource prediction model by adopting an event-scene dual-drive mechanism, dynamically adjusting parameters of the gated recurrent neural network through an elastic adjustment factor, and optimizing a multi-target resource allocation strategy based on a reinforcement learning algorithm; a two-stage resource scheduling mode is adopted, non-preemptive resource allocation of priority guarantee is implemented in an event triggering stage, and an optimization strategy of continuous adjustment is deployed in a steady-state stage. According to the method, the resource utilization efficiency and the service quality in a high-concurrency scene are remarkably improved, the method is compatible with an O-RAN standard interface, and the method is suitable for high-reliability and low-delay communication scenes such as smart cities and industrial internet.

The invention relates to the technical field of energy scheduling, and discloses a smart city energy dynamic scheduling system and method based on big data analysis, and the method comprises the following steps: the operation state of a transformer substation, the basic parameters of a charging pile and regional load prediction data are collected in real time, data cleaning and abnormal value filtering are carried out; constructing a complete power grid-charging facility dynamic information base; calculating the power supply margin of each region based on the capacity loss of the faulty transformer substation, establishing a weight scoring system of charging pile power adjustment in combination with the charging demand urgency, and determining the reduction or recovery priority of each charging load; and generating a charging pile power adjustment instruction through a multi-target optimization model, and iteratively correcting a power distribution scheme and generating a final scheduling instruction set by taking minimization of user satisfaction loss as a target while meeting the power grid capacity. According to the invention, by constructing a dynamic response mechanism and a multi-target collaborative optimization model, accurate and rapid regulation and control of the traffic load are realized in the scene of sudden power shortage of the power grid.

The present invention discloses a bus route optimization design method and system based on resident travel big data, the method comprising the steps of: obtaining mobile phone signaling data of residents in a target area; obtaining the stop points of residents' travel trajectories; dividing the target area into a number of traffic areas; obtaining an OD matrix based on the stop points of residents' travel trajectories and traffic areas; constructing a bus route planning model; generating a number of bus alternative routes based on a shortest path algorithm; obtaining a target bus route from the alternative routes based on a genetic algorithm and in combination with an objective function and an OD matrix. Compared with the prior art, the present invention not only innovatively combines spatiotemporal big data and genetic algorithms in technical means, improving the accuracy and efficiency of bus route planning, but also demonstrates strong advantages in system architecture and data processing capabilities, and can provide a more scientific, dynamic and sophisticated optimization solution for the urban public transportation system, contributing to the efficient operation of urban transportation and the construction of smart cities.

The invention discloses a smart city traffic resource optimal configuration method, a platform and a storage medium, and relates to the technical field of traffic optimization. When a road accident is detected, accident information is collected, and a traffic accident position and traffic accident characteristics are obtained; time consumption prediction is carried out according to the traffic accident characteristics, and traffic control duration is obtained; calling real-time traffic data from an intelligent traffic system to obtain associated road data; executing multi-thread traffic flow back-stepping analysis to obtain associated traffic flow prediction information; carrying out shunting performance evaluation; and dynamically updating the signal lamp configuration of the plurality of shunting candidate roads by taking the associated traffic flow prediction information as a constraint. According to the invention, the technical problems of aggravation of traffic jam and low emergency management efficiency caused by lack of scientificity and dynamic adaptability of shunting planning and lagging of signal lamp regulation in a traffic accident scene in the prior art are solved, and the technical effects of optimizing traffic resource configuration, relieving traffic jam and improving traffic emergency management efficiency are achieved.

The invention relates to the field of new energy charging, in particular to a new energy charging management method for a smart city. The method comprises the steps that a municipal vehicle operation feature data set is acquired, on the basis, municipal vehicles are subjected to type division through a vehicle type classification strategy, and a municipal vehicle driving type set is determined; acquiring a municipal vehicle task set, and on the basis, tracking a dynamic task variable set of a task execution vehicle in a municipal vehicle driving type set in a task execution process; according to the dynamic task variable set, analyzing an association relationship between the dynamic task variable set and the real-time power consumption data through a multi-modal fusion strategy, and determining dynamic power change information; and acquiring a charging station information set, dynamically optimizing a charging decision in combination with the municipal vehicle task set and the dynamic electric quantity change information, and determining and outputting an optimal charging strategy. In the municipal vehicle charging decision-making process, the utilization rate of charging resources is improved, and the continuity of municipal task execution is ensured.

The invention discloses a smart city traffic signal lamp management and control system, and relates to the technical field of traffic signal control. The system comprises a region division module, a traffic flow detection sensor, a region traffic flow analysis module, a signal lamp management and control strategy generation module, an inter-region signal lamp cooperative control module and a variable information sign and information processing and generation module. The system can accurately monitor and analyze urban traffic flow conditions in real time by comprehensively applying an advanced region division algorithm, a traffic flow detection technology and an accurate traffic flow state evaluation model, and can intelligently generate a signal lamp management and control strategy according to an analysis result, so that the traffic lamp management and control efficiency is improved. Comprising the steps that a congestion relieving dynamic timing algorithm is adopted in a congestion area, the number of times of signal lamp switching is reduced, a flow balance guiding timing algorithm is applied in an area with small flow, in addition, cooperative control over signal lamps between the areas can be achieved, and efficient connection and balanced distribution of traffic flow between the areas are ensured.

The invention relates to the technical field of smart city traffic management, in particular to an urban traffic jam intelligent prediction and dispersion system and a method thereof, and the system integrates a data acquisition module, a traffic prediction module, an event influence evaluation module and an intelligent dispersion module. The data acquisition module updates multi-source traffic data in real time, and a solid foundation is provided for prediction; and the traffic prediction module accurately predicts the traffic flow by using a deep learning model combining a graph neural network and a space-time attention mechanism based on the received data. The event influence evaluation module analyzes the influence of emergencies on traffic and ensures the pertinence of a dispersion strategy; the intelligent dispersion module optimizes a traffic signal lamp control scheme in real time by using deep reinforcement learning in combination with the prediction result and the event influence; the system comprehensively senses the traffic condition through multi-source heterogeneous data fusion, the traffic flow prediction accuracy is remarkably improved, and intelligent and efficient traffic management is achieved.

The invention relates to the technical field of geographic information systems, in particular to a map tile data efficient access method and system based on hybrid storage and intelligent layering, and the method comprises the following steps: constructing a multi-level storage architecture, carrying out dynamic heat analysis, carrying out intelligent layering scheduling, preloading and space prediction, and carrying out transmission and access optimization. The method has the beneficial effects that the multi-level storage pool is constructed by combining the advantages of object storage and a distributed file system; adopting a dynamic tile popularity analysis model to realize hot data cache acceleration and cold data hierarchical archiving; and meanwhile, a tile request preloading mechanism is introduced, and an access path is optimized in combination with a spatial locality prediction algorithm. According to the method, the storage cost can be remarkably reduced, the tile request delay is reduced, quick response in a high-concurrency scene is supported, and the method is suitable for application scenes such as Internet map services and smart cities.

The invention discloses a massive multi-source and multi-modal data fusion method, which is a technology for efficiently fusing and processing various types of data from different sources, realizes effective integration, utilization and seamless integration of multi-source and multi-modal data, and improves the breadth and depth of data analysis. According to the multi-modal feature extraction and fusion model based on deep learning, the deep features of all modal data can be automatically learned and extracted, and efficient fusion is carried out in the model. Besides, a data quality evaluation and self-adaptive adjustment mechanism is introduced, parameters and strategies in the data fusion process are dynamically adjusted according to the quality and distribution condition of the data so as to ensure the stability and reliability of the fusion result, and the method can be widely applied to multiple fields such as big data analysis, artificial intelligence, social media analysis, medical health and smart cities.

The embodiments of the present disclosure provide a method for predicting a water accumulation risk in a smart city implemented based on a management platform of an Internet of Things (IoT) system for predicting a water accumulation in a smart city. The method may include: predicting, based on obtained area information of a target area, a water accumulation risk in the target area; determining, based on the water accumulation risk, an adjustment scheme corresponding to the target area; and executing an adjustment instruction corresponding to the adjustment scheme.

The invention discloses a smart city data sharing system based on a block chain technology, and relates to the crossing field of medical data processing and information technologies. The system comprises the following modules: 1, a data storage and block chain mapping module, which generates an abstract by using a salting SHA-256 algorithm, stores the abstract in a block chain, stores original data in an IPFS distributed manner, sets redundancy and cache, and regulates abstract storage according to data popularity; 2, a data sharing module driven by the intelligent contract, which is used for subdividing authorization levels, setting pre-request verification, dynamically updating rules and automatically executing functions to guarantee compliance and controllability; the privacy protection technology fusion module expands zero-knowledge proof application and optimizes multi-party security calculation by adopting hybrid encryption; and 4, a data tracing and compliance auditing module which records logs to form a historical chain, traces and outputs an auditing report. According to the invention, the security, efficiency and compliance of medical data sharing can be comprehensively improved, and the application prospect is wide.

The invention relates to the technical field of computer vision and automatic driving, in particular to a natural driving accident scene key element extraction method based on a visual large model. Comprising the steps of data collection and preprocessing, accident data cue word design, ontology-based accident scene description and data annotation, pre-training model fine tuning and optimization, key element identification and post-processing and the like, the efficiency of natural driving accident analysis is remarkably improved, and the time and cost of manual examination are reduced. Through an ontology-based accident scene description method, dynamic feature extraction based on an optical flow method and more accurate cue word design, the recognition accuracy of the model on key elements is improved. Through a data enhancement technology and reasonable loss function design, the robustness and generalization ability of the model in different scenes are improved. The method is not only suitable for analysis of natural driving accidents, but also can be expanded to other scenes, and a high-efficiency scene understanding method is provided for management of intelligent traffic, smart cities and safe cities.

The invention discloses a road and bridge disease analysis and early warning method and system based on big data, and relates to the technical field of smart cities. Multiple groups of sensors are deployed, acquired related transverse wind load information data are associated with related vertical traffic load information data, and an external force load influence coefficient Xhz is constructed; after comparison, a preliminary early warning signal is sent out, related surface state data information and related vibration state data information of the current road and bridge are further collected and associated with an external force load influence coefficient Xhz, a comprehensive disease degree index Zbh is calculated through fitting in combination with a disease evaluation model, and the comprehensive disease degree index Zbh is compared with an evaluation threshold value K; according to the method and the system, the multi-dimensional data is acquired to judge whether the road bridge has a disease risk, a disease early warning instruction of a corresponding grade is generated and executed, and accurate identification and real-time early warning of the road bridge disease are realized through dynamic association and intelligent analysis of the multi-dimensional data.

The invention discloses a smart city monitoring system and method, and relates to the field of city monitoring, and the system comprises a digital twin model, an intelligent cooperation module, a decision optimization module, an execution module, a feedback and adjustment module, an interactive display module, and a public participation platform. Meanwhile, the city modeling and the city entity are subjected to state synchronization work through the Internet of Things; the intelligent cooperation module is used for subdividing city monitoring into a plurality of sub-tasks, and each sub-task is responsible for a specific intelligent agent; and the decision optimization module performs decision optimization on the intelligent cooperation module through a reinforcement learning algorithm. According to the smart city monitoring system and method, omnibearing and three-dimensional city information collection is achieved, the coverage range is wide, data sources are rich and complementary, the city operation state can be obtained in real time, and sufficient data support can be provided for subsequent analysis and decision making.

The invention provides an intelligent video monitoring system and method based on deep learning, and belongs to the technical field of video monitoring, and the system comprises a heterogeneous sensing array, a cognitive driving feature fusion module, a dynamic evolvable detection model, a causal reasoning tracking engine, a meta-knowledge enhancement behavior analysis module and a quantum-classical hybrid computing architecture. The heterogeneous sensing array comprises a reconfigurable visible light / infrared dual-mode camera group, a distributed microphone array and a millimeter wave radar, and is configured to generate multi-physical field sensing data; and the cognitive driving feature fusion module adopts a space-time-frequency spectrum joint coding technology, and integrates a three-dimensional convolutional network and a graph attention mechanism to realize cross-modal feature interaction. According to the invention, through multi-dimensional technical innovation, the intelligent video monitoring system with autonomous evolution capability is constructed, the perception capability, reasoning precision, resource efficiency and safety are obviously superior to those of a traditional scheme, and the high-standard requirements of smart cities, security and other scenes are met.

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention discloses a smart city street lamp intelligent control system and method. The method comprises the following steps: performing multi-aspect inspection on each hardware device through a comprehensive intelligent control module and a communication module; the environment monitoring module is used for collecting multi-source data in real time and integrating and analyzing the multi-source data through the data fusion module; generating a dynamic dimming strategy of the common road section and the high-priority road section according to the collected data and regional requirements; brightness adjustment and on-off control are executed one by one through the single lamp control module, and equipment operation parameters are collected through the real-time feedback and monitoring module. The remote management platform is used for monitoring the running state of the street lamp in real time and dynamically adjusting strategy optimization analysis; different operation scenes are simulated through a system simulation test, actual deployment is carried out in a pilot area, and parameters and operation logic are optimized. Through modular design, intelligent decision making and data driving modes, the management efficiency and the energy-saving level of urban street lamps are remarkably improved, and reliable technical support is provided for smart city construction.

The invention discloses an energy-saving illumination control system and method for an urban illumination LED lamp group, and relates to the technical field of illumination control, and the system comprises an ambient light detection module, a traffic flow monitoring module, a time period division module, a weather information acquisition module, a weather information control module, an LED lamp group driving module, a wireless communication module, a high-precision photosensitive sensor for the ambient light detection module, a filtering and temperature compensation module, and an intensity measurement accurate data transmission module. The traffic flow monitoring module depends on geomagnetism or camera shooting, is high in precision and strong in recognition, and facilitates regulation and control through data; the time period division module accurately times and corrects time according to a clock chip, and sets illumination measures according to the time; the weather information acquisition module is networked to acquire meteorological data, and lamps are adjusted according to day conditions; and the control module multi-core microprocessor calculates illumination levels and brightness according to multi-source data to control lamp groups. According to multi-factor intelligent lamp control, energy saving is remarkable, lighting quality is good, comfort and safety are improved, remote monitoring and management can be achieved, troubleshooting is accurate and efficient, maintenance is convenient and fast, intelligent and green development of urban lighting is promoted, and the intelligent city construction requirement is met.

The invention discloses a single-view large-scale outdoor scene three-dimensional reconstruction method based on three-dimensional Gaussian splashing, and the method comprises the steps: collecting a pseudo aerial image, and constructing a panoramic multi-mode supervision end-to-end single-view three-dimensional reconstruction model; meanwhile, panoramic consistency supervision, semantic constraint depth regularization and a radial weighted luminosity loss and Gaussian cutting mechanism are introduced, so that the defect of insufficient geometric constraint of traditional single-view three-dimensional reconstruction is effectively overcome, and high-efficiency and high-fidelity three-dimensional modeling of a large-scale outdoor scene under single image input is realized; the method is suitable for various actual scenes such as smart city construction, automatic driving simulation, virtual reality / augmented reality, digital twinning and the like.

The invention relates to the technical field of smart city traffic, and discloses a smart city traffic dynamic optimization system and method based on digital twinning. The system obtains urban traffic network multi-dimensional data from a plurality of heterogeneous data sources through a traffic multi-dimensional data acquisition module and integrates the urban traffic network multi-dimensional data into a traffic related data warehouse; a traffic digital twinning model construction module extracts features from the data warehouse to generate a traffic related feature matrix, and a digital twinning traffic dynamic model is constructed according to the traffic related feature matrix to output a theoretical traffic state value; the traffic flow map construction module determines a dimension link map of each dimension and constructs a traffic flow link map; the traffic core feature screening module screens a traffic core feature sequence based on the map; the traffic multi-dimensional optimization analysis module performs multi-dimensional difference analysis on the theoretical traffic state value and real-time actually measured traffic data, and generates a region-level difference coefficient matrix in combination with the core feature sequence; and the traffic event association positioning module can realize accurate management and dynamic optimization of urban traffic.

The invention relates to the field of urban gas pipeline integrity management, and discloses a ground gas leakage diffusion simulation calculation method, which comprises the following steps of: initializing environmental parameters and leakage physical conditions, constructing a rotation alignment wind field calculation grid, and dividing dense and sparse time sequences by adopting a dynamic time step strategy; a leakage flow dynamic sequence is generated in combination with linear interpolation, diffusion coefficients are calculated based on atmospheric stability level sections, and an intermediate coefficient matrix is pre-calculated to optimize the calculation efficiency of a Gaussian puff model; an RK4 numerical solution is introduced to process an unsteady state diffusion process, and fusion of an analytical model and a numerical method is realized; and performing standard condition conversion, anomaly correction and structured storage on the concentration data to generate a multi-dimensional visualization result. According to the method, minute-level high-resolution grid leakage diffusion simulation is realized, leaked gas volume concentration distribution and diffusion trend are accurately output, and reliable technical support is provided for smart city operation, city gas safety management, real-time early warning and emergency decision making.

The invention relates to the technical field of smart city operation, and discloses a pressure regulation and loss reduction method based on steam simulation and condensate water analysis, comprising the following steps: step S1, collecting operation parameters of a steam pipe network system; s2, converting the preprocessed operation parameters into scale space parameters; s3, establishing a condensate water source term equation, a heat source term equation and a condensate water generation model to obtain pressure, temperature and condensate water content; s4, solving the optimal pressure adjustment amount and the condensate water adjustment amount by combining the pressure and condensate water coupling matrix, and optimizing the circulation degree; and S5, correcting model parameters in the condensate water source term equation, the heat source term equation and the condensate water generation model based on a comparison result. By coupling and simulating the condensate water source item, the heat source item and the generation model, dynamic distribution of condensate water can be accurately analyzed, and the problems that in the prior art, calculation efficiency and model precision are difficult to balance, and dynamic coupling is lacked are solved.

The invention discloses a method for evaluating evolution characteristics and evolution driving factors of a space-time pattern of a park green land, and particularly relates to the technical field of crossing of smart cities and landscape ecology, and the method comprises the steps: obtaining multi-period remote sensing image data and planning maps, and constructing a multi-source database; interpreting the image based on a random forest supervised classification algorithm to generate a park green space distribution map, and optimizing the precision; adopting a landscape ecology theory to select a landscape pattern index to quantify space-time evolution characteristics; constructing a driving evaluation index system, and analyzing a driving mechanism by means of a multiple regression model and a space measurement model; and generating a three-level spatial pattern optimization strategy based on the result and outputting a visual decision map. According to the method, the spatial-temporal pattern evolution characteristics and driving factors of the urban park green land can be comprehensively analyzed, and a scientific basis and decision support are provided for planning, management and protection of the urban park green land.