196 results about "Crowd density" patented technology

The invention discloses an AI-based smart park multi-source data fusion method and system, and the method comprises the steps: generating a time-space aligned standardized data flow according to environment parameters, energy consumption waveforms, security signals and personnel trajectory data collected by a heterogeneous sensor network; generating a multi-modal fusion feature matrix based on the standardized data stream; according to the multi-modal fusion feature matrix, generating a three-dimensional twinborn body including the equipment state, the people flow density and the energy consumption hot spot in real time; inputting the three-dimensional twin into a multi-target constrained reinforcement learning algorithm, and fusing real-time data and prediction data to generate a Pareto optimal solution set; and based on the Pareto optimal solution set, generating a final instruction set for driving park equipment regulation and control, and triggering collaborative response of a security and protection system and an energy consumption system at the same time. According to the embodiment of the invention, intelligent upgrading of park management can be realized through cross-modal feature extraction, dynamic digital twin modeling and reinforcement learning optimization.

The invention relates to the technical field of virtual modeling, in particular to a lamp arrangement method and system based on virtual modeling and a medium. The method comprises the following steps: obtaining dynamic monitoring data of a target space, wherein the dynamic monitoring data comprises real-time illumination intensity data, people flow density data and ambient light change data; performing prediction modeling on the illumination demand of the target space according to the dynamic monitoring data to obtain illumination demand prediction data including expected illumination requirements and light distribution characteristics in different time periods; the adaptive light propagation grid is constructed in the digital twinborn space based on the illumination demand prediction data, grid division data are obtained, and the adaptive light propagation grid has high grid density in a region with severe illumination change and low grid density in a region with smooth illumination change. According to the method, comprehensive illumination analysis and evaluation can be carried out in the initial stage of design, and blindness and low efficiency in a traditional design method are avoided.

The invention discloses a video monitoring method and system, electronic equipment and a storage medium, and relates to the field of video monitoring. According to the method, a three-dimensional digital twinborn scene is constructed according to the space coordinates of each camera in a target area, and a node relation matrix is constructed according to the space relation between the cameras; according to the three-dimensional digital twinborn scene and the node relation matrix, a dynamic vision field splicing algorithm is adopted, the real-time position and the action path of the target object are determined, and the people flow density of the target area is displayed; determining a behavior mode of the target object through skeleton key point detection and interaction behavior analysis, and predicting an action track of the target object according to the behavior mode; and evaluating an initial risk level of the action track, adjusting the initial risk level in combination with the people flow density to obtain a final risk level, and generating a disposal suggestion according to the final risk level. By implementing the technical scheme provided by the invention, comprehensive and accurate video monitoring and risk management of the target area are realized.

The invention discloses an unmanned aerial vehicle and robot dog cooperative inspection system, and relates to the technical field of unmanned aerial vehicles, the unmanned aerial vehicle and robot dog cooperative inspection system comprises an unmanned aerial vehicle inspection module, a robot dog inspection module and a cooperative control module, the unmanned aerial vehicle inspection module is used for aerial aerial view of large-scale crowds, and real-time calculation of crowd density, velocity field and congestion hotspots; the robot dog inspection module is used for carrying out close inspection in a ground crack corridor, detecting gait stability and falling individuals in real time, forming a soft boundary to carry out differential diversion guidance and carrying out physical intervention on dangerous situations, and carrying out real-time inspection on the ground crack corridor. The cooperative control module is used for integrating the aerial view angle of the unmanned aerial vehicle and the ground detection information of the robot dog, dynamically updating a routing inspection path and triggering sound-light alarm and linkage guidance when a risk is identified, so that air-ground integrated risk prevention and control are realized, the unmanned aerial vehicle routing inspection module comprises a visual perception unit, and the unmanned aerial vehicle routing inspection system has the characteristic of near execution.

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 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 is applicable to the technical field of venue navigation, and provides an intelligent navigation method for venue digital service, which comprises the following steps: acquiring positioning information, behavior track and interaction record of a user; performing combined analysis on the user data and preset venue interest point attribute data to obtain a user feature vector containing a stay preference coefficient, movement speed analysis and a subject interest weight; extracting a topological relation between the current position and the target interest point according to the user feature vector, and calculating a constraint parameter according to the topological relation; based on the constraint parameters, constructing an initial path planning model taking the minimum expected passing time as a target function; determining a people flow density gradient map of each region of the stadium in a preset time period through the obtained historical people flow data and the user real-time positioning data; the initial path planning model is optimized by using the people flow density gradient map, and the optimal path is generated by dynamically adjusting navigation path nodes, so that the intelligence level and the user satisfaction of venue navigation are effectively improved.

The invention discloses a cross-modal feature extraction and adaptive fusion crowd counting system and method, and belongs to the technical field of computer vision. The crowd counting system comprises a visible light image acquisition module and a thermal infrared image acquisition module which are parallel; a visible light double-layer feature extraction network layer and a visible light feature preference selection interaction layer are sequentially connected behind the visible light image acquisition module, and a thermal infrared double-layer feature extraction network layer and a thermal infrared feature preference selection interaction layer are sequentially connected behind the thermal infrared image acquisition module. A common lightweight cross-modal feature interaction fusion attention module and a crowd density map estimation module are connected behind the visible light feature preference selection interaction layer and the thermal infrared feature preference selection interaction layer, and crowd counting can be realized by using a crowd density map obtained by the system. The method can effectively solve the problem that the counting performance is limited due to the fact that different hierarchical features cannot be adaptively fused according to the current scene.

The invention discloses a video crowd counting method based on a cascaded cross-domain feature interaction network. The method comprises the following steps: carrying out data enhancement processing of random cutting and horizontal flipping on a current frame and front and back frames of the current frame; and constructing a cross-domain feature interaction network composed of a spatial domain branch and a frequency domain branch. The frequency domain branch extracts frequency domain feature output of different stages through a high and low frequency signal aggregation module and a feature encoder based on adjacent frames; the spatial domain branch is based on a single-frame image, and static spatial semantic features are extracted through a feature encoder. Cascade fusion is carried out on the double-branch features on multiple scales, two-way channel cross attention is utilized to reconstruct time sequence correlation frequency domain features of a current frame, and fusion and reconstruction of the two domain features are achieved through a cross-domain feature mutual modulation module. And after the reconstructed double-branch features are processed by the fusion network, outputting a crowd density map of the current frame by a density regression head. And after training is completed, storing the optimal model for video crowd counting. According to the invention, through cross-domain feature cascade and bidirectional time sequence modeling, the accuracy and robustness of crowd counting in a video scene are effectively improved.

The invention discloses a multi-scale crowd counting method and system based on VSSM and mask reconstruction and a medium, and belongs to the field of computer vision and deep learning, and the method comprises the steps: carrying out the multi-scale transformation and segmentation operation of an input crowd image, so as to obtain an image block under each scale; analyzing each image block to obtain an information entropy graph, performing masking operation on each image block according to the information entropy graph, inputting the masked image block into a VSSM to perform feature extraction so as to obtain coding features, performing decoding reconstruction on the coding features through a Transform decoder, and finally obtaining a reconstructed feature image; fusing the reconstructed feature images of different scales by using a multi-scale fusion module to generate fused features; and generating a crowd density map based on the fusion features, and analyzing the crowd density map to obtain a crowd counting result. Counting errors caused by single-scale analysis are avoided, and the crowd counting precision in a full-scale range is remarkably improved.

Disclosed in the present invention is a people flow analysis system based on Internet of Things information acquisition. The system comprises a data acquisition module, a data storage module, a people flow statistics module, an obstruction analysis module, a coupling analysis module and a predictive analysis module. The data acquisition module acquires all people flow data of a laboratory server room by means of an image acquisition apparatus; then, the people flow statistics module separately performs crowd density analysis, wherein an average distance is determined by means of hierarchical and clustering analysis of head labeling points, and further a Gaussian function is used to determine the size of heads, thus solving the problem of uncertainty caused by a nearest neighbor method when crowd densities are low and uneven; the obstruction analysis module performs feature fusion on features of an obstacle in a coverage space and neighboring crowds to obtain a new data set; the predictive analysis module performs overall people flow predictive analysis on the basis of the new data set and people flow data of crowds not adjacent to the obstacle. Therefore, the accuracy of people flow analysis and control efficiency are greatly improved.

The invention discloses a robot hierarchical path planning method and device in a complex environment, a medium and a product, and the method comprises the steps: responding to a path planning request of a robot in an airport terminal, dividing the airport terminal into a plurality of dynamic Voronoi regions according to an underlying environment grid map and people flow density information, a minimum path cost algorithm with people flow density weighting is adopted to construct a global path anchor point sequence, the global path anchor point sequence is further split into a plurality of local path planning requests, a double-heuristic cost function is constructed, an improved algorithm is adopted to generate local sub-paths, and according to surrounding environment information collected by a robot in real time, the local sub-paths are calculated. And optimizing each local sub-path by adopting a reinforcement learning algorithm, generating motion control instructions for the robot at different control time points, and realizing dynamic obstacle avoidance of the robot. The path planning efficiency and the motion safety of the mobile robot in a super-large-scale dynamic environment are remarkably improved, and the technical problems that a traditional method is high in calculation complexity and poor in real-time performance are effectively solved.

The invention discloses a hospital multi-scene transportation robot hybrid scheduling method based on dynamic priority, and belongs to the technical field of robot control and scheduling. The method comprises the following steps: acquiring hospital dynamic environment data; receiving and analyzing the multi-mode instruction to form task parameters; preliminarily grading according to task parameters and preset rules, and dynamically adjusting task priorities in combination with a dynamic map and the current state of the robot; based on the adjusted task priority and robot type, distributing robots and generating an initial path considering distance and people flow density; the robot is driven to track along the path trajectory, and obstacle behaviors are predicted in real time and dynamically avoided; decomposing a transportation task into task chains, and when the electric quantity is lower than a threshold value, deciding whether to insert a charging task in combination with the priority; and after the task is completed, prompting and transmitting feedback data. According to the invention, dynamic priority, behavior prediction and hybrid scheduling are adopted, intelligent distribution and path optimization of transportation tasks are realized, and the efficiency and safety of internal logistics of hospitals are improved.

The system uses LiDAR to determine crowd density analysis and flow. The system places a plurality of sensors and distributes them in an area where a crowd will gather. To create a comprehensive 3D representation of the environment, the LiDAR sensor emits numerous laser pulses in different directions, covering a wide field of view. The sensor collects the returning light for each pulse. The collected data, consisting of distance measurements and their corresponding angles, is processed to create a point cloud. The generated point cloud can be further processed and analyzed to extract various information about the environment.

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 relates to the technical field of monitoring layout, in particular to a modeling camera layout method and system. The method comprises the following steps: carrying out geometric topology analysis on a venue to obtain a venue functional region model; performing fluid mechanics simulation on the venue functional area model to obtain people flow density distribution characteristic data; performing risk grade division on the venue according to the people flow density distribution characteristic data to obtain a venue key area evaluation matrix; performing dynamic density field modeling on the venue based on the crowd density distribution characteristic data to obtain a crowd density field mathematical model; performing monitoring demand analysis on the venue according to the crowd density field mathematical model and the venue key area evaluation matrix to obtain a monitoring coverage scheme; and performing density equalization configuration on the cameras according to the monitoring coverage range scheme to obtain a camera distribution density scheme. According to the invention, the camera layout is optimized, the resource waste is avoided, and the monitoring continuity and no dead angle are ensured.

The invention discloses a subway multi-line transfer station flood multi-target dynamic evacuation path planning method. The method comprises the following steps: establishing a three-dimensional space model by acquiring subway station structure information, and abstracting the three-dimensional space model into network representation; real-time water regimen information is obtained through a hydrological sensor and video monitoring, preprocessing and fusion are carried out, and water regimen sensing data under unified space-time reference are generated; in-station crowd information is obtained in real time through video target detection, the crowd density and the traffic capacity are calculated, and crowd situation perception data are formed; generating a plurality of group evacuation path candidate schemes for different types of passengers by adopting a real-time path search algorithm in combination with the water regimen, the people flow situation and the trafficability constraint; evacuation paths are classified according to passenger types, evacuation guidance is issued through a multi-terminal channel, and differentiated dynamic guidance of departure passengers, transfer passengers and mobility-disabled passengers is achieved. According to the method, flood disasters and people flow changes can be responded in real time, and the evacuation efficiency and safety of the subway transfer station are improved.

The embodiment of the invention discloses a camera arrangement evaluation method and device based on operation data and a medium. The method comprises the steps that a three-dimensional space model of a to-be-evaluated area is acquired and visually displayed, and a virtual monitoring scene is generated; in response to an adding operation of camera point locations, displaying a visible range of each camera in the virtual monitoring scene in an overlapping manner; in response to an import operation of historical operation data, superposing and displaying a thermodynamic diagram of historical people flow density and / or work order density in the virtual monitoring scene; according to a superposition display result, determining a coverage condition of the camera on the people flow density and / or the work order density; and evaluating the point location layout of the camera according to the coverage condition. According to the embodiment of the invention, intelligent and precise camera arrangement evaluation is realized.

The invention relates to a commercial district consumer population dynamic prediction method and system based on unmanned aerial vehicle identification, and the method comprises the steps: firstly obtaining a visible light image sequence which is continuously collected by a low-altitude unmanned aerial vehicle in a commercial district region and comprises a continuous timestamp mark, and then carrying out the end-to-end feature extraction of the image sequence through a pre-trained multi-task feature extraction network; generating a three-dimensional feature tensor containing crowd density distribution, individual movement tracks and crowd aggregation morphological features, then calling a space-time dynamic prediction model to carry out time sequence association and space mapping processing on the three-dimensional feature tensor, and generating consumer population distribution prediction results of the commercial district in a plurality of continuous time periods in the future. And performing historical trend matching analysis on the prediction result by using a confidence verification module, outputting verification data including a prediction confidence score and a key change time period, and finally generating a dynamic prediction report including a corresponding relationship between a time dimension and a space region based on the verification data. Therefore, the dynamic state of the consumer population in the business district can be comprehensively and accurately predicted in real time.

The invention discloses a crowd density detection method fusing optical flow and texture features, and relates to the technical field of computer vision, and the method comprises the following steps: collecting a real-time video stream of a camera, and carrying out graying, Gaussian filtering and perspective correction preprocessing; performing motion compensation by using image registration and offset transformation; modeling based on a Gaussian mixture model and extracting a foreground to generate a binary mask; analyzing a foreground coverage rate, an optical flow and texture features; pre-defining a multi-ROI and a density early warning standard; inputting the fusion features into a regression model and outputting initial density; dynamically calibrating and correcting the deviation; and generating a thermodynamic diagram superposition video to realize visualization. According to the invention, the optical flow and texture features are fused to improve density estimation precision, illumination resistance and dynamic background interference resistance; the dynamic calibration maintains long-term accuracy, and the dynamic ROI adapts to scene change; the thermodynamic diagram can quickly identify risks, is adaptive to multiple scenes, and meets real-time monitoring requirements.

The application discloses a fine-grained crowd analysis method based on passive WiFi sensing, which comprises the following steps: obtaining a coarse-grained crowd density map in a target monitoring area obtained by WiFi sensing at a continuous time; inputting the coarse-grained crowd density map into a trained deep convolutional autoencoder model to output a fine-grained global crowd density map at the continuous time; identifying the center pixel point coordinates of each local high-density crowd in the global crowd density map by using a density clustering method; cropping a local block from the corresponding position in the global crowd density map for each center pixel point coordinate, and combining the local blocks at the same position at several previous times to form a crowd density block sequence; inputting the crowd density block sequence into a trained bidirectional convolutional long short-term neural network model to output the velocity vector of each local high-density crowd. The method can improve the accuracy of crowd distribution estimation in a scene, and simultaneously realize the estimation of the velocity of local high-density crowds.

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 relates to the technical field of computer vision, in particular to an AI technology-based data acquisition device for crowd statistics, which comprises an acquisition module for acquiring crowd density image data and real-time video image data in various scenes; the auxiliary acquisition module is used for acquiring interactive crowd density image data; the data preprocessing module is used for preprocessing the image data; the model training module is used for constructing and training a crowd statistical model; the model optimization module is used for compressing and optimizing the trained crowd statistical model; the crowd quantity statistics module is used for generating a crowd quantity by utilizing the optimized crowd statistics model; and the mobile client module is used for providing a mobile APP client for the user. According to the method, video image data containing diversified scenes and crowd density are collected, and deep learning and model optimization are carried out by using a mercuric chloride AI calculation framework, so that the detection precision and robustness of the crowd density in a complex scene can be improved.

The invention belongs to the technical field of intelligent traffic and passenger flow monitoring, and discloses a passenger flow statistics fusion system and method based on multi-modal big data. The system comprises an infrared induction data acquisition module, a video identification data acquisition module, a WiFi probe data acquisition module, an entrance and exit environmental factor sensing module, a confidence coefficient calculation module and a data fusion module. The system calculates the confidence coefficient of various data sources by collecting various types of passenger flow data and the shielding intensity, crowd density and bandwidth state in the current environment, and performs weighted fusion according to the confidence coefficient to obtain a passenger flow statistical result with higher credibility. The system improves the accuracy and stability of passenger flow statistics in a complex environment, and is suitable for dynamic monitoring and management of a high-density passenger flow scene.

The invention discloses a large-scale subway group escape system based on panic emotion infection, and relates to the technical field of escape systems.The large-scale subway group escape system based on panic emotion infection comprises the steps that 1, a crowd evacuation scene is constructed according to subway related information; step 2, initializing a crowd (Crowd); each individual in the crowd Crowd comprises personality characteristics phi, an initial emotion value e0 and an initial movement speed v0; 3, performing an emotion infection simulation algorithm; step 4: first; the range between the pedestrian carrying the luggage and the luggage is divided to be less than or equal to 0.5 m; carrying out related weighting and improvement on the evacuation speed and behavior of pedestrians according to the luggage volume and the luggage weight when the luggage volume and the luggage weight exceed the individual bearing capacity; a discarding behavior will occur; 5, calculating the crowd density rho of the walking area at the t + 1 moment according to the crowd movement information; the crowd evacuation behavior is updated; and step 6, realizing crowd evacuation visualization in a graphical mode.

The invention relates to the technical field of fire monitoring, in particular to an intelligent fire monitoring and early warning method. A Gaussian distribution model is introduced to simulate the diffusion effect of a heat source, the adaptability of smoke flowing to airflow in a building is enhanced, limitation of a traditional smoke diffusion model is corrected and optimized according to local turbulence driven by differential pressure, especially the non-uniform airflow effect in areas such as narrow galleries and courtyards, and accurate monitoring and early warning of fire disasters are achieved. The expected speed and comfortable distance of the crowd are dynamically adjusted by combining light flow sensing with environment sensing (such as smoke concentration and temperature), and the problems of people flow and evacuation path optimization in a high-density scene can be effectively solved. Especially in a narrow space, interaction between crowds and obstacles can be fed back in real time, and secondary accidents caused by crowd density or high temperature and smoke influence are prevented.

The invention discloses a crowd density monitoring system and method in a multi-scale complex scene, and relates to the technical field of data monitoring. Comprising the following steps of: 1, deploying multiple cameras and configuring and calibrating the multiple cameras; 2, performing three-dimensional coordinate reconstruction of a scene by utilizing images acquired by the multiple cameras; 3, performing feature extraction and data fusion; 4, performing discrete element representation and simulation; the crowd distribution condition of the whole monitoring area is displayed, and an efficient and reliable solution is provided for crowd management monitoring in a complex scene.

The invention discloses a campus security informatization management method based on artificial intelligence, and relates to the technical field of campus security informatization management, and the method comprises the steps: collecting the multi-modal identity features of passing personnel in real time, combining with a pre-stored authorized personnel database, employing a deep learning-based convolutional neural network CNN and a spatial-temporal feature extraction model, and obtaining the multi-modal identity features of the passing personnel; the method comprises the following steps: performing high-precision comparison on human face and gait features, firstly matching human face features, triggering gait feature recognition after failure, and generating suspicious person marks or check notifications in a classified manner according to the matching failure condition in combination with a dynamic comparison decision-making mechanism, so that the check process is efficient and accurate; a people flow density monitoring model and a congestion prediction model are integrated, a long short-term memory (LSTM) network is utilized to carry out trend prediction on data acquired in real time, a density growth rate and congestion state marks are calculated, peak congestion risks are identified in advance, and a passing mode is automatically adjusted; and an additional channel is started or a rapid verification mode is switched, so that the passing efficiency in peak periods is guaranteed.