478 results about "Crowds" patented technology

The invention discloses a crowd clustering analysis method, system and device based on spatio-temporal data. The method comprises the following steps: acquiring spatio-temporal data; performing feature processing on the spatio-temporal data to obtain a feature number, and constructing a spatio-temporal data matrix based on the feature number; performing dimension reduction on the spatio-temporal data matrix through a preset analysis algorithm to obtain a data matrix; according to a preset point in the data matrix, the volume of a preset radius with the preset point as the center and the point number in the radius are calculated, and the local density is calculated based on the point number in the radius and the volume; calculating the distance between each point in the data matrix and the nearest target point, and selecting a target neighborhood radius based on the distance and the local density; determining a data dimension based on the number of features, and setting a minimum point number through the data dimension; marking each point in the data matrix through a density clustering algorithm according to the radius of the target domain and the minimum point number to obtain a clustering result; and analyzing features of clustering results based on the plurality of clustering clusters.

The invention relates to the technical field of urban geography, in particular to a crowd movement flow-based multi-scale community structure importance identification method, which comprises the following steps of: constructing a crowd movement space-time network according to a preset geographic space unit and a time interval on the basis of crowd movement starting place and destination data; identifying a dynamic community structure based on a crowd mobile space-time network; performing multi-scale community structure object role and importance identification; and performing pattern classification by using the time sequence importance of the multi-scale community structure object. According to the method, the problems of inaccurate dynamic community structure recognition and lack of multi-scale object contribution decoupling mechanisms are solved.

The invention relates to the technical field of crowd counting, in particular to a crowd counting method based on a DGCC-Net model, and the method comprises the steps: obtaining a crowd counting image; a DGCC-Net model is constructed, and pre-training is carried out; extracting a multi-scale feature map from the crowd counting image by using a feature extraction network; a local attention module is used for extracting context information and detail information of a crowd region from a feature map on a high-resolution scale in the multi-scale feature map, and a high-resolution feature map is obtained; feature fusion of semantic and detail information is carried out on a feature map on a low-resolution scale and a high-resolution feature map in the multi-scale feature map by using a multi-level feature fusion network, and a density guide feature map is generated from the fused feature map through a density guide module; and predicting a crowd counting result according to the density guide feature map by using a prediction network. According to the invention, the counting precision and robustness are improved when crowd scenes with uneven density distribution are processed.

The invention relates to the field of artificial intelligence, discloses a crowd advertisement accurate putting determination method based on a knowledge graph, and aims to solve the problem of mismatching caused by interest model lag in traditional advertisement putting. The method comprises the following steps: constructing a dynamic evolution type user interest knowledge graph, fusing multi-source behavior flow and static portrait data, and introducing a time decay factor and an event triggering mechanism to realize node weight self-adaptive updating; a short-term interest pulse is accurately captured through a time sequence attention propagation and cross-domain association edge dynamic generation algorithm; and constructing a dual-channel graph neural network inference engine, respectively processing long-term stable and short-term sudden interest paths, and generating a delivery decision through confidence weighted fusion. According to the invention, the timeliness and accuracy of advertisement matching are improved, the mismatching rate is reduced, and the user experience and the self-calibration capability are enhanced.

The invention relates to the technical field of Internet red scenic spot tourist crowd gathering risk monitoring, and discloses an Internet red scenic spot tourist crowd gathering risk intelligent monitoring and early warning system which comprises a central processing unit, a crowd density analysis module and the like. The crowd density analysis module generates basic density distribution based on region division and historical passenger flow data; the environment state acquisition module acquires environment and facility data in real time; the tourist behavior analysis module generates a behavior feature vector through the track and the stay duration; the central processing unit generates a risk early warning instruction through a spatial superposition algorithm; the risk early warning execution module adjusts monitoring parameters according to instructions and feeds back states, and triggers a dynamic adjustment algorithm when abnormity occurs. The system also improves the accuracy and the resource utilization rate through an optimization and adaptation module, achieves the intelligent monitoring and precise early warning of the crowd gathering risk, and guarantees the tourism safety.

The invention provides an elevator multi-mode crowd feature perception and intelligent advertisement putting method and system, and relates to the technical field of intelligent advertisement putting, and the method comprises the steps: collecting multi-mode perception data in an elevator through an edge computing terminal; performing feature decoupling on the data, performing cross-modal semantic alignment, establishing a directed association relationship between modals, and constructing a scene feature map; calculating the topology importance degree of map nodes, screening feature nodes, and extracting context information for semantic coding; mapping the scene semantic code and the advertisement audience semantic code to a two-dimensional coordinate system to construct a semantic matching graph, and extracting an optimal matching path to form a candidate set; predicting a scene evolution trend based on the scene characteristic spectrum evolution trajectory, and calculating an advertisement adaptive score to generate a playing sequence; putting and collecting user interaction data feedback according to the sequence to update the graph structure. According to the invention, accurate crowd feature recognition and advertisement dynamic matching are realized, and the advertisement putting efficiency and the user experience are improved.

The invention provides a city-level large-scale crowd activity track generation method based on mobile phone signaling, and belongs to the field of intelligent transportation, and the method comprises the steps: S1, obtaining signaling data of a specific crowd in a selected city in one day, carrying out the preprocessing operation, and carrying out the grid division of a map region; s2, processing each preprocessed track sequence by using a mask auto-encoder model to obtain the probability that each track point belongs to different grids; s3, constructing a loss function of trajectory accuracy and distribution diversity, and finally obtaining parameters of a pre-training stage model by reconstructing training parameters of mask trajectory points; s4, generating a complete final track by using the pre-training stage model; and S5, covering the corresponding latitude and longitude coordinates of the specific personnel at a certain moment in the generated trajectory points, and finally forming a complete final trajectory of the specific personnel. According to the method disclosed by the invention, the large-scale crowd activity trajectory data consistent with the original signaling trajectory data in distribution can be generated on the premise of not exposing personal privacy.

The invention relates to the technical field of artificial intelligence, in particular to a video GIS intelligent analysis method based on deep learning, and the method comprises the steps: detecting a target in a video in real time through a predefined target detection model, and generating a space-time mark which comprises a bounding box and category information; thirdly, associating target tracks of different cameras by using a dynamic graph model and a dynamic graph neural network to form a space-time ID and a track chain; then, combining a visual inertial odometer and a geographic information system to calibrate a homography matrix, and mapping the trajectory chain to a geographic coordinate system to obtain space-time trajectory data; then, constructing a neural network model and a trajectory generation model, and respectively predicting crowd density distribution and pedestrian motion trajectories; and finally, carrying out real-time alarm according to the track and the multi-level geo-fencing rule. According to the invention, intelligent analysis of video data is realized, target tracking and alarm capabilities are improved, and the method is widely applicable to the fields of crowd management and safety monitoring.

The invention discloses an AI exhibition hall control method and system based on crowd thermal distribution monitoring, and the method comprises the steps: obtaining crowd density distribution data, behavior data and environment data through constructing a full-coverage sensing network, employing a thermal imaging camera, a WiFi probe and an infrared sensor, and generating a thermal distribution diagram; through converting discrete density point fusion and labeling into a data set of a continuous thermal field, crowd distribution characteristics are visually displayed, spatial-temporal correlation of crowd flow is effectively modeled by combining a convolutional neural network and a spatial-temporal diagram neural network, a prediction-adjustment-verification closed loop is realized based on a prediction result 6-8 minutes ahead of time, and the prediction efficiency is improved. The control strategy is dynamically adjusted according to the predicted crowd distribution trend and the business target, the guide route or the interaction form can be dynamically adjusted according to the crowd density, the exhibit layout can be adjusted in advance, the number of tourists going back and forth is reduced, the overall flow efficiency is improved, and therefore the display effect of the AI exhibition hall is improved.

The invention belongs to the technical field of image analysis, and particularly relates to a multi-expert voting type crowd counting method based on scene understanding, which comprises the following steps of: extracting visual features from an input image and extracting text features from a text prompt by utilizing a backbone network comprising an image encoder, a text encoder and a feature enhancer; projecting the text features into an embedding space which is the same as the visual features for alignment; constructing a local subject expert used for local crowd target detection, a global context expert used for understanding the overall layout of the scene, a spatial relationship expert used for modeling the spatial relationship between crowds, and an attribute feature expert used for processing the appearance and attribute information of the target in the scene; a knowledge distillation strategy is adopted, and experts are trained in a fine-grained mode; and obtaining the output of each expert, and outputting a final counting result through a decoder. According to the method, the fine-grained crowd counting performance in a complex scene can be optimized by utilizing a voting mechanism and a knowledge distillation strategy of a multi-expert system.

The invention discloses an intelligent crowd monitoring system based on deep learning, and relates to the technical field of intelligent monitoring, and the system comprises a data collection module which collects monitoring video information in real time and generates a monitoring video stream; the video processing module is used for performing target detection on the acquired monitoring video stream and extracting crowd position and quantity information according to a target detection output result; the image recognition module is used for calculating the crowd density, tracking the movement track of the crowd to obtain movement track data, and calculating crowd distribution change data according to the crowd density; the crowd analysis module is used for predicting and analyzing crowd behaviors by using a deep learning model to obtain a predicted congestion path; and the decision adjustment module is used for judging whether the crowd advancing route needs to be dynamically adjusted based on fuzzy reasoning and warning the crowd to avoid the congestion area. The problem that multi-dimensional comprehensive analysis of crowd behaviors, flowing directions and the like is lacked in the prior art is solved, and the crowd monitoring accuracy and the congestion avoiding efficiency are improved.

The invention provides a DMP-based crowd selection method and apparatus, and an electronic device, a data management platform pre-processes multi-source user data, generates user features corresponding to the pre-processed data based on a preset rule, converts the obtained user features into user tags based on a pre-constructed tag system, and sends the user tags to the user management platform; the method comprises the following steps: acquiring a user tag, storing the acquired user tag in a distributed storage mode, displaying the stored user tag through an operation interface, converting a circling operation for a target user tag displayed on the operation interface into a rule tree, and circling a target user corresponding to the target user tag based on the rule tree. And associating the circling result with the identification information of the target user corresponding to the circling result to generate a crowd packet, and expanding the obtained crowd packet by adopting a similar crowd expansion algorithm. According to the invention, the problems of complex rule configuration, insufficient real-time performance, limited expansibility and the like of a DMP-based crowd selection system in the prior art can be solved.

Embodiments of the technology provides a real-time crowd measurement and management system including data capturing devices installed in a plurality of zones for continuously capturing crowd data of a plurality of crowds in a plurality of zones. The system also includes an analysis module for identifying crowd characteristics from the crowd data; analysing the crowd data to determine one or more patterns and changes in mood of the plurality of crowds; and predicting crowd information comprising emergent crowd characteristics, an emergent crowd behaviour of the crowds, and one or more issues based on the analysis in real-time. The system also includes a display module for displaying the predicted crowd information along with at least one of an alert and at least one indicator in real-time.

The invention belongs to the field of big data processing, and discloses a crowd travel mode identification method and system for multi-view subspace learning based on dynamic and static feature fusion, and the method comprises the steps: carrying out the preprocessing of original signaling data, and cleaning invalid data; extracting a travel track of each user, and processing possible abnormal conditions of ping-pong data and drift data; after track data of a single user in a sample time period is obtained, segmenting the track data, and extracting a single trip chain track; extracting static characteristics such as travel behaviors and travel traffic of a travel chain in combination with multi-source data such as mobile phone signaling data, an OSM road network and public traffic data; extracting time sequence features by using an LSTM model, and modeling dynamic features of a travel track; static and dynamic features are fused, and travel modes are captured from multiple views. According to the method, the complex travel mode can be more accurately captured, data redundancy and noise are reduced, and the accuracy of travel mode identification is remarkably improved.

The invention provides a multi-dimensional risk situation awareness method and system based on dynamic people flow modeling, and relates to the technical field of crowd safety management, and the method comprises the steps: obtaining crowd flow data, and generating a distribution situation and a flow situation; constructing a grid flow incidence matrix; convergence and diffusion paths are identified, and risk weights are determined; generating risk probability distribution and grade distribution data; dividing an evacuation area, and planning an evacuation channel; and calculating a safe passing threshold value and a buffer area capacity, and realizing batch evacuation. The risk distribution can be accurately predicted, the evacuation efficiency is improved, and the congestion risk is reduced.

The present invention provides a method and system for analyzing crowd gatherings, wherein the analysis method includes the following steps: setting up video capture terminal groups at multiple different locations to capture video images and thermal images of corresponding locations; receiving the video images captured by the cameras and analyzing the video images to obtain first analysis data; receiving the thermal images captured by the thermal image collector and analyzing the thermal images to obtain second analysis data; calculating crowd density based on the first and second analysis data, and issuing an alarm signal if the crowd density exceeds a preset crowd gathering density threshold; receiving the alarm signal and issuing an alarm. The crowd gathering analysis method of the present invention can monitor crowd gatherings at corresponding locations and promptly notify users or relevant personnel when excessive crowds occur.

The embodiment of the invention provides a crowd gathering recognition method. The method comprises the following steps: acquiring a to-be-recognized image of a target area; performing identification processing based on the to-be-identified image to obtain the number of personnel in the to-be-identified image and a personnel identification frame; when the personnel number is greater than a preset first personnel number threshold value, performing first expansion processing based on the personnel identification frames to obtain a personnel expansion frame corresponding to each personnel identification frame; performing fusion processing based on the personnel expansion frame to obtain at least one fusion region; and based on the fusion region, determining whether crowd gathering exists in the target region. The first expansion processing can reduce identification errors caused by personnel postures or partial shielding, a more accurate personnel expansion frame is obtained, fusion processing is carried out based on the personnel expansion frame, the spatial relation between personnel can be effectively identified, a fusion area is obtained, and the fusion efficiency is improved. Therefore, whether crowd gathering exists in the target area can be accurately judged according to the fusion area, and the accuracy of crowd gathering recognition is improved.

The invention discloses an intelligent question and answer implementation method and system based on a large model and a semantic atlas, and relates to the technical field of supply chain intellectualization, and the method comprises the steps: obtaining a structured distribution data table and an unstructured text data stream, carrying out the data cleaning and normalization processing, and obtaining a structured distribution data table and an unstructured text data stream; constructing a unified knowledge base to perform semantic understanding and knowledge extraction on the unified knowledge base, extracting key entities and semantic relationships through named entity recognition and relationship extraction tasks, and fusing structured distribution data features to construct a preliminary semantic map; and integrating the preliminary semantic map with the spatial dimension features acquired by the data interface and the real-time task execution log of the shop patrol assistant, endowing nodes with space-time attributes through graph neural network training, and outputting a dynamic semantic map integrated with space-time dynamic mode features. According to the method, geofence coordinates and crowd density characteristics are injected by utilizing an Atde API, a store patrol assistant real-time replenishment state log is superposed, and the causal association strength modeling precision is improved through ST-GNN training.

The invention discloses a security cooperative control method and system for realizing mobile crowd perception by unmanned aerial vehicles. The method comprises the steps of setting a random action exploration process, selecting initial actions of multiple unmanned aerial vehicles, calculating rewards based on a mobile crowd perception model of the multiple unmanned aerial vehicles, establishing an experience pool and updating states of the multiple unmanned aerial vehicles. Extracting a sample from the experience pool, calculating an expected income of multi-unmanned aerial vehicle data collection, and updating a reviewer network according to the expected income; constructing a language representation ERNIE model based on knowledge fusion enhancement and a Stackelberg game model, performing gradient descent through a gradient calculated by the Stackelberg game model, and solving a result of minimum gradient descent; and updating the target strategy network, adding different disturbances, observing fluctuation of reward values under different disturbances, if a convergence condition is met, storing the current strategy, otherwise, returning to extract samples from the experience pool again. According to the method, the safety and robustness problems caused by unmanned aerial vehicle cooperative control deviation can be relieved.

The invention discloses a crowd flow dynamic monitoring method and system, and relates to the technical field of crowd flow monitoring, and the method comprises the steps: deploying a full-coverage signal collection network for key nodes in a monitoring region, and associating a unique identifier of a monitoring user RFID tag with an anonymized user file; the RFID reader captures the unique identifier of the RFID tag, the timestamp and the signal intensity to form a multi-dimensional data pair, then an initial moving sequence is generated, the position error is corrected based on the difference value of the signal intensity of the adjacent RFID readers, and an individual smooth path track is generated; and aggregating the individual smooth path tracks of all the RFID tags to generate a full-region people flow density thermodynamic diagram, identifying and marking high-frequency moving paths and retention hotspots, and generating facility layout optimization suggestions based on a predicted hotspot capacity threshold. Full-coverage accurate data acquisition, individual track accurate reduction and macroscopic analysis visual presentation are realized, and powerful support is provided for city management and resource allocation.

The invention discloses an urban comprehensive park space-time network construction method and system based on crowd trajectory data. The method comprises the following steps: firstly, collecting, cleaning and dividing crowd trajectory data in a comprehensive park range; secondly, scenic spots in the comprehensive park are clustered, the number of optimal clusters is determined through clustering analysis, and all the clusters serve as all functional activity districts; then building a trajectory model through space projection and a shortest path algorithm, recovering a user moving trajectory path according to the crowd trajectory data, generating path node sequence data according to the timestamp of each trajectory point, and finally, taking the centroid of each functional activity area as each node of the space-time network, and combining each time period to obtain the user moving trajectory path. And constructing an undirected space-time network and a directed transfer network. The method has the advantages of efficient data processing, high modeling precision and perfect privacy protection, dynamic monitoring and space optimization analysis of the park people flow can be realized, and reliable data support and decision basis are provided for urban comprehensive park planning management.

The invention provides a smart city crowd dispersion system and method based on an Internet of Things large model, and a medium, the system comprises an emergency supervision management platform, and the emergency supervision management platform is configured to obtain area monitoring data through a monitoring device deployed in a monitoring area based on an emergency supervision perception control platform; determining a regional congestion risk based on the regional monitoring data; responding to the condition that the regional congestion risk meets the dispersion condition: determining emergency dispersion parameters based on the regional congestion risk, the emergency dispersion parameters including a traffic control parameter and a traffic guide parameter; based on the emergency dredging parameters, an emergency control signal is transmitted to an emergency dredging device deployed in a monitoring area through an emergency supervision sensing network platform, so that the opening interval of a channel gate machine is controlled based on the passing control parameters, and the display content of an electronic indication board is controlled based on the passing guide parameters; the display content comprises at least one of a display color, a dredging direction and a dredging route.

The invention discloses a domain adaptive crowd counting method based on multi-aspect fine-grained alignment. The method comprises the following steps: 1, weighted sampling of a source domain image and a target domain image; 2, image coding and density map generation of a source domain and a target domain; 3, crowd density alignment based on conditional domain adversarial training; 4, image style alignment based on comparative learning; 5, optimizing model parameters on the crowd counting data set; and 6, performing crowd density map prediction by using the optimized model. According to the method, crowd specific information related to the target domain can be extracted, interference of irrelevant source domain features is filtered, crowd distribution of the source domain and the target domain is aligned in a fine-grained mode from multiple aspects, and the crowd distribution of the target domain is accurately aligned through weighted sampling and two loss functions related to distribution alignment. The consistency of the number of training samples, crowd density and image styles of the source domain and the target domain is constrained respectively, so that crowd distribution between the source domain and the target domain is correctly aligned, and generalization and robustness of a cross-domain crowd counting model can be improved.

The invention discloses an intelligent recommendation method and system based on big data mining and a storage medium, and the method comprises the steps: carrying out the attribute division of obtained user source data, and obtaining a plurality of data sub-tables corresponding to a user basic attribute portrait, a business rule attribute portrait and a behavior attribute portrait; obtaining a portrait wide table according to each data sub-table, and obtaining a portrait label value of each user according to the portrait wide table and the crowd result table; obtaining a user number according to the portrait label value, and performing distribution analysis according to the user number to obtain a crowd portrait analysis result table; calculating according to the behavior attribute portrait to obtain a current behavior score and a historical behavior score, and further obtaining a user portrait score and writing time according to the current behavior score and the historical behavior score; and performing intelligent recommendation through the user portrait large model according to the crowd portrait analysis result table, the user portrait score and the writing time. The intelligent recommendation accuracy of the user portrait large model can be improved, and the method can be widely applied to the technical field of intelligent recommendation.

The invention relates to the technical field of data intelligent analysis, in particular to a crowd abnormal behavior recognition method and system. Comprising the following steps: S1, acquiring video data in a monitoring range; s2, acquiring mobile phone big data information of all individuals in a monitoring range; s3, performing data processing on the video data and the mobile phone big data information; s4, obtaining a feature set of the video data, and obtaining a feature set of the mobile phone big data information; s5, performing data fusion on the feature set of the video data and the feature set of the mobile phone big data information; s6, abnormal behavior recognition is carried out, wherein abnormal behaviors comprise a gathering behavior, a fighting behavior and a trampling event; s7, displaying the abnormal behavior identified in the step S6, and sending abnormal information to a manager; through combined analysis of the video data and the mobile phone big data, the accuracy of crowd abnormal behavior recognition is improved.

The invention discloses a weakly supervised crowd counting method based on a cross-type window self-attention mechanism. The method comprises the following steps: 1) performing feature extraction on a target picture by adopting the cross-type window self-attention mechanism; (2) a CSWinCrowd structure based on global average pooling and regression Token is introduced; the method comprises the steps of (1) designing a CSWinwd-Token scheme based on a learnable position coding module, (2) designing a CSWinCrowd-Token scheme based on a learnable position coding module, and (4) providing a crowd counting model based on CSWinTransform for crowd counting of a target picture in combination with the steps (S1) to (S3). According to the method, the dependency relationship between different positions in a sequence can be well captured, and the problem that the counting effect is poor when the crowd density change of a current Transform model is large is solved.

The invention provides an intelligent fire-fighting monitoring and early warning platform and method combined with the Internet of Things, and relates to the technical field of fire-fighting early warning, and the method comprises the steps: determining a site type through an intelligent building space and a hazard source, mining related combustible materials and related explosives, and building a fire-fighting risk mapping table; determining a first edge computing node set according to the influence range of the fire scene based on a distributed computing core; determining a second edge computing node set according to the spreading path of the fire scene, and configuring an edge computing network; and establishing a fire emergency response mechanism based on the edge computing network. According to the method, the technical problems of low crowd evacuation efficiency and confusion caused by the fact that a matched crowd evacuation strategy cannot be accurately set due to the fact that fire monitoring data cannot be rapidly analyzed and processed in a traditional fire monitoring and early warning method can be solved, the crowd evacuation efficiency and safety can be remarkably improved, and the crowd evacuation experience is improved. Therefore, the timeliness and accuracy of fire emergency response are improved.

The invention provides a crowd intensity identification method based on a multi-modal data fusion and hybrid model, and the method comprises the steps: generating an optimal inspection path through the flight path planning of an unmanned plane, and capturing a target image; s2, realizing time synchronization of the camera and the Internet of Things equipment through an NTP protocol; s3, the image is divided into N * N grids, a thermodynamic diagram is constructed based on historical density data, a high-frequency crowded area is subdivided, complete CNN reasoning is started for high-density sub-blocks, and lightweight MobileNet acceleration is adopted for low-density sub-blocks; s4, estimating crowd density through a hybrid detection regression model; and S5, calculating a density baseline and a standard deviation based on a historical data sliding window, and setting a trigger threshold. Based on the technical scheme of the invention, the detection precision and robustness in a dense scene are effectively improved.

The invention discloses a target object description method, device and equipment based on multi-source data fusion, and relates to the technical field of social simulation.The method comprises the steps that a target community needing object description is selected and determined based on an electronic map, an API interface function is called to obtain description data of a target object of the target community in the mobile internet, and the description data are stored in the mobile internet; probability distribution of the target object in each dimension is obtained; obtaining description data of a target object of the target community based on an active investigation mode, and performing conversion processing to obtain probability distribution of the target object in each dimension; and fusing the probability distribution of the target object on each dimension obtained from the mobile internet with the probability distribution of the target object on each dimension obtained in the active investigation mode to obtain a multi-dimensional description of the target object of the target community. According to the method, the community description data is obtained through two modes of mobile internet big data and questionnaire survey, effective complementation is formed, complete crowd portraying of the community can be realized, and community social simulation is effectively supported.

The invention discloses a global security monitoring method and system based on the Internet of Things, and belongs to the technical field of the Internet of Things, and the method comprises the steps: obtaining an electronic map of a target place, obtaining the number and position data of Internet of Things nodes of each partition, and generating the configuration data of an edge calculation terminal; the edge computing terminal identifies an abnormal condition and sends a patrol instruction to the security terminals in the partitions; the cloud fusion platform constructs a stampede risk prediction model to predict a stampede risk level, and if the stampede risk level exceeds an early warning level, an evacuation request is sent to the management terminal; and generating a crowd evacuation path plan based on the multi-channel spatial-temporal feature matrix and the trampling risk level, and sending a guide instruction to a security terminal to generate a visual interface. According to the global safety monitoring method and system based on the Internet of Things provided by the invention, full-coverage, real-time and refined risk prediction, early warning and management of crowded places are realized, and the crowd safety management efficiency and accuracy are effectively improved.