359 results about "Crowds" patented technology

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 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.

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 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 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.

The invention discloses a dynamic label flipping backdoor attack method facing RGB-T crowd counting, and belongs to the technical field of neural networks. The method comprises the following steps: randomly selecting 10% of all samples as poisoning samples for pollution, and taking the rest 90% as benign samples; designing a local disturbance trigger and embedding the local disturbance trigger into RGB image information of the poisoning sample; designing a local patch trigger and embedding the local patch trigger into T image information of the poisoning sample; designing a label information modification strategy and finishing modification of poisoning sample label information; the poisoning and benign samples are mixed according to a preset proportion to serve as a training set to train any RGB-T crowd counting model, and an RGB-T crowd counting poisoning model with a backdoor function is generated; the intoxication model is used to test clean and intoxication test sets respectively to assess the attack performance thereof. According to the method, different triggers and different label modification strategies are designed according to the difference among the modes, and a new attack and defense reference is provided for security defense research of the multi-mode visual system.

The invention discloses a user strategy-based map operation method and system. The method comprises the steps of constructing a crowd strategy map tree based on user multi-dimensional data; setting a multi-scene personalized operation strategy according to the crowd strategy map tree; and carrying out operation analysis on user behavior data collected by each key node of the platform according to the multi-scene personalized operation strategy, and carrying out operation optimization according to an analysis result. Precise marketing and personalized service are realized, the conversion rate and the user value are improved, and continuous growth of platform business is promoted.

The invention discloses an intelligent dynamic guiding method based on a multi-agent system, and relates to the technical field of multi-agent systems, and the method comprises the following steps: S1, constructing an environment macroscopic state abstract tensor, and inputting the environment macroscopic state abstract tensor into an improved QMIX model; s2, extracting an optimal evacuation decision intention tensor; s3, constructing a local observation state tensor; s4, extracting an individual Q value tensor; s5, extracting a global team Q value; s6, generating an anti-fact state through the VRNN model, calculating time sequence differential loss, and jointly optimizing parameters of the whole QMIX model; and S7, based on the individual Q value tensor, selecting an evacuation guide action. The method overcomes the limitation that a traditional crowd guiding method depends on a fixed plan, the guiding strategy is single and response lags behind, and an efficient and accurate solution is provided for intelligent crowd flow management and control and cooperative guiding of large public places.

The invention relates to the technical field of traffic prediction, in particular to a crowd traffic prediction method and system based on multi-party longitudinal federated learning, and the method specifically comprises the following steps: multi-party data owners perform local feature coding, and perform spatial-temporal feature coding on spatial-temporal crowd traffic data of spatial-temporal traffic data owners; the method comprises the following steps: performing external factor feature coding on external factor data of an external factor data holder, performing encryption and differential privacy protection on coded features of each holder through privacy protection distributed joint modeling, and transmitting encrypted features subjected to encryption and privacy protection to a global module of a trusted third party; and finally, decrypting the global module encryption feature of the third party, performing crowd flow prediction according to the decrypted feature, and updating the local model of each data holding party. On the premise of ensuring data privacy security, the problem of predicting mobile crowd traffic of scattered data sources can be effectively solved by utilizing cross-domain collaboration of space-time traffic data and external factors.

The invention discloses an electronic coupon algorithm recommendation system based on a specific crowd, which belongs to the technical field of electronic coupon recommendation and comprises a user demand perception module, a key factor priority ranking module, an electronic coupon strategy generation and scene adaptation module and an evaluation and feedback module. According to the electronic coupon algorithm recommendation system based on the specific crowd, key factors are generated and sorted by mining user preferences and purchase frequencies, and priorities are dynamically adjusted when conversion does not reach the standard; pushing associated coupons N days before exhaustion in combination with commodity consumption prediction; optimizing access based on the user active window and the channel response rate; the method solves the problems of pushing dislocation, opportunity loss and low conversion efficiency of a traditional system, realizes optimal configuration of marketing resources and improvement of user conversion efficiency, and is suitable for a precise electronic coupon marketing scene.

The present invention belongs to the technical field of data mining and relates to a method for identifying population types based on venue code data and clustering integration. Based on the venue code data, this method first extracts relevant features of population travel from different perspectives and analyzes the representational ability of different features for population types; then uses three classic clustering algorithms to perform clustering analysis on different features respectively, selects the best clustering algorithm for each feature, and generates base clustering members; finally, through the integration method of weighted voting, assigns corresponding weights to the base clustering members according to the importance of the features, and integrates the results of the base clustering to obtain the final clustering result, realizing the identification of population types.

The present application relates to a training method, apparatus, computer equipment, computer-readable storage medium, and computer program product for a crowd detection model. The method comprises: obtaining unlabeled features and labeled features obtained from capturing a crowd scene from multiple perspectives; predicting the uncertainty of the number of people based on the unlabeled features of each group of perspectives to obtain the uncertainty of the number of people corresponding to each group of perspectives; each group of perspectives contains a different number of perspectives; comparing the uncertainty of the number of people corresponding to each group of perspectives to obtain a comparison result; determining the unlabeled loss based on the comparison result; and training a crowd prediction model based on the unlabeled loss and the labeled loss corresponding to the labeled features. This method can alleviate the dependence on labeling and improve the accuracy of crowd prediction.

The invention is suitable for the field of computer vision, provides a crowd evacuation path dynamic recommendation method and system based on visual data, and discloses a crowd evacuation path dynamic recommendation method and system based on visual data in the field of computers. Crowd motion video streams are collected in real time, pedestrian head orientation vectors are extracted after preprocessing, a crowd motion velocity field is formed, and a crowd motion state database is established; constructing a visual inertia dynamic model to predict future crowd evacuation path distribution change; the real-time bearing rate of each channel is calculated, and grading early warning is generated by combining capacity threshold pre-judging saturation time; and when the channel reaches the optimal capacity threshold value, determining an upstream branch node, calculating a diversion starting time window and strength, generating a control instruction, and transmitting the control instruction to an indication execution end. Accurate dynamic path recommendation is realized, and evacuation safety and efficiency are improved.

The invention provides a commercial complex crowd management system based on a real-time LoT sensor, and relates to the technical field of intelligent management. The management main system comprises a data acquisition module, a data processing module, a dynamic environment optimization module, a people flow guidance and excitation module, a resource scheduling module, a safety monitoring and response module, an external cooperation module, a space value optimization module, a user feedback and experience module and a control flow management module; the real-time LoT sensor is deployed to comprehensively monitor the people flow density, the environmental parameters and the equipment state in the commercial complex, the problems of crowded areas and uneven resource allocation in the peak period can be found in time, and user experience decline and potential safety hazards caused by delayed response are effectively avoided. The system achieves minute-level density monitoring in peak hours, and the real-time performance and response efficiency of crowd management are remarkably improved.