Remote Intelligence People Flow Statistics Control System Based on Internet of Things (IoT)

Abstract

The present invention discloses a remote intelligent people flow statistical control system based on Internet of Things (IoT), the system includes: a data collection module, a data storage module, a people flow control module, an image analysis module, a target recognition module, a model analysis module and a linkage analysis module, the image analysis module, the target recognition module and the model analysis module perform model analysis on the pedestrian target movement trajectory of the people flow in different passage spaces, and then the linkage analysis module performs overall dynamic analysis on the evacuation network consisting of all passage spaces, and based on the model analysis results of different nodes, analyzes the correlation between nodes to obtain a correlation coefficient, and further analyzes the influence between evacuation routes to obtain the linkage value between different evacuation routes, and finally establishes a dynamic observation equation, the people flow control module performs remote control on the intelligent people flow, greatly improving the efficiency of the intelligent people flow remote control, and making the analysis result of the people flow movement conform to the actual people flow movement regulation.

Description

[Technical Field]

[0001] The present invention relates to the technical field of intelligent control, in particular to a remote intelligence people flow statistical control system based on the Internet of Things (IoT). [Background technology]

[0002] When an emergency occurs on a campus where people are densely distributed and evacuation is necessary, many students will evacuate from multiple teaching laboratory centers and machine rooms, and crowd accidents are likely to occur in the crowd flow. In order to ensure the safe evacuation of pedestrians in the building's corridors, it is important to implement intelligent statistical monitoring detection and flow diversion command for the crowd flow within the building. In conventional technologies, people flow control systems use image recognition and pedestrian detection to analyze the movement characteristics of people flow within a space, thereby realizing people flow monitoring within the monitoring range. However, the movement characteristics of people flow in different types of corridors within a building are different, and the models and model judgment criteria used to analyze people flow in different corridors are not unique. Therefore, under different types of corridor monitoring cameras, People flow routes are interconnected, and in the event of an emergency, different types of passageways form the entire evacuation route. However, it is difficult for remote monitoring personnel to monitor the entire people flow through multiple monitoring screens, and the cameras in different passageways are limited to covering the space within the passageway. When there are location requirements in the evacuation process, people flow analysis under a single monitor alone cannot dynamically monitor the entire evacuation process. In order to increase the degree of cascade matching of different types of passageways, realize remote intelligent control of people flow, and improve the accuracy of evacuation route configuration, this invention proposes a remote intelligent people flow statistics and control system based on the Internet of Things (IoT). Summary of the Invention [Means for solving the problem]

[0003] In order to overcome the shortcomings of the prior art in light of the above situation, the present invention proposes a remote intelligent people flow statistics and control system based on the Internet of Things (IoT), in which a model analysis module performs micro-analysis based on a model that matches the people flow movement structures of different monitoring and collection ports, and then a linkage analysis module combines the analysis results of the micro-model to analyze the people flow movement rules of all evacuation lines, thereby improving the accuracy of people flow movement analysis, and the linkage analysis module can perform cascading analysis of the mutual influences between different monitoring and collection ports to improve the dynamic monitoring efficiency of remote people flow statistics and control.

[0004] The remote intelligence people flow statistical control system based on the Internet of Things (IoT) includes a data collection module, a data storage module, a people flow control module, an image analysis module, a target recognition module, a model analysis module, and a linkage analysis module; The people flow control module compiles statistics of all evacuation routes in the building and labels the evacuation routes. i Let i∈[1,n], n is the number of evacuation routes in the building, and n evacuation routes intersect to form an evacuation net. The data collection module collects people flow movement data in different passage spaces through N monitoring collection ports, and stores the collected people flow movement data in a data storage module. i The number of monitoring collection ports is R i , R i ∈[1,N], which corresponds to the number of inlets of different people flow movement spaces covered by monitoring collection ports, Video information of people flow movement is collected through monitoring collection ports, the image analysis module performs image frame processing on the video information from different monitoring collection ports, and the target recognition module performs target tracking on the moving target in the people flow, and the image analysis module and the target recognition module map the pixel coordinates of the image into a multidimensional spatial coordinate system; The model analysis module combines the analysis processes of the model analysis module and the target recognition module to perform data statistics on the changes in people flow within the passage space, and then constructs a corresponding micro model based on the speed, density, and flow rate structure of people flow movement. Different micro models are used for people flow movement spaces with different numbers of entrances to analyze the movement patterns of people flow within the space-time, and the spatiotemporal distribution characteristics of the speed and density of people flow movement within the passage are obtained. The linkage analysis module establishes an objective function by combining with the evacuation end point position of the people flow movement, and analyzes the feedback relationship between N nodes. The linkage analysis module further performs macro-linkage analysis on the people flow movement path characteristics of all passages in the evacuation network. The people flow movement space covered by one monitoring collection port is regarded as one node in the people flow movement path, and R i The system contains nodes, and the spatiotemporal distribution characteristics of the speed and density of different nodes are extracted to obtain the feature matrix of the entire evacuation route. Then, dynamic analysis is performed on the connections between different evacuation routes to obtain the influence value between evacuation routes. Finally, the linkage analysis module predicts the evacuation process of people flow through the established dynamic observation equation. The people flow control module is combined with the prediction results of the linkage analysis module to direct the evacuation of people in the building, so that the people flow movement follows the optimal evacuation route during emergency evacuation.

[0005] Furthermore, the linkage analysis module first determines the end point of the pedestrian flow, and then the pedestrians at different positions perform a self-organized evacuation movement of the pedestrian flow. Then, it performs dynamic observation through a comprehensive analysis of the evacuation network consisting of all nodes. Specifically, The spatial distance between the end point of the evacuation route and each node is X i , i∈[1,N], the linkage analysis module determines the objective function f(t) by combining the node data based on the end point location of the people flow movement. The node data includes spatial distance, people flow rate and time, and the end point coordinate is (x * ,y * ,z * ) and the spatial coordinates of the node are (x, y, z),

[0006]

number

[0007] Step 1: determining the process of people flow movement along different evacuation routes based on an objective function; Let the current node p be the merging node in the people flow path. Calculate the correlation coefficient between the current node p and the adjacent node q. S1 and S2 are the feature vectors between the current node p and the adjacent node q, respectively. The calculation formula is given by the following formula:

[0008]

number

[0009] Step 2: where ρ represents the correlation coefficient, Cov represents the covariance, and E represents the mathematical expectation value. The correlation between adjacent nodes is determined based on the value of the correlation coefficient. If ρ>0, the people flow movement rule between nodes is positive feedback; if ρ<0, the people flow movement rule between nodes is negative feedback. The node where two evacuation routes intersect is defined as an overlapping node, and the pedestrian flows of different evacuation routes are merged at the overlapping node. Then, a micro-analysis is performed on the pedestrian flow rules at the overlapping node using a model analysis module. At the overlapping node of evacuation routes, the pedestrian flows influence each other, and the degree of change in the pedestrian flow at the overlapping node of evacuation routes is expressed as a linkage value. The linkage value of the pedestrian flow between different evacuation routes is calculated to obtain the W ij where i and j represent the subscripts of the path, i, j∈[1,n], R i and R j are the numbers of nodes in the two evacuation routes i and j, respectively,

[0010]

number

[0011] where U i (k) and U kj (k) is the node flow rate adjacent to the stacked node and having the same inflow direction of the pedestrian flow. The two nodes belong to two evacuation routes, respectively. k is the number of intersecting routes. X k , X ik , X jk Step 3 is the spatial distance, The linkage analysis module combines the analysis results of the model analysis module to analyze the spatiotemporal distribution characteristics of the speed and density of different nodes on all evacuation routes, and derives the corresponding feature matrix A. nRi is obtained, and R i ∈[1,N], the linkage analysis module combines the linkage degree between different evacuation routes, performs dynamic observation of the dynamic state of different nodes in the process of people flow, and makes judgments on the observed values.

[0012]

number

[0013] where:

[0014]

number

[0015] η1 is velocity, η2 is density, η3 is flow rate, η4 is the covered space, t i The dynamic prediction model includes step 4 of predicting the parameter fluctuations of the evacuation process based on the changes in the predicted values ​​at different times, and combining this with the set objective function to determine the predicted values.

[0016] The target recognition module recognizes multiple pedestrians in the monitored video, and then converts the coordinates of successfully recognized pedestrians and corrects their positions based on the recognition effect judgment. After the image analysis module and the target recognition module convert the coordinates of the collected data, the model analysis module analyzes and calculates the density and speed of people flow within the spatial area. By constructing an analytical model of the spatiotemporal distribution of pedestrian target positions, basic maps of the spatiotemporal distribution of pedestrian flow speed and density are obtained, and the movement rules of people flow are further analyzed through the basic maps.

[0017] The model analysis module constructs different models based on the type of corridor space to analyze the people flow movement rules, and classifies corridors including Y-shaped corridors, straight corridors, T-shaped corridors, and cross corridors based on the number of people flow junctions in the area covered by the current monitoring collection port.

[0018] The relationship between the bottom and center coordinates of the detection frame of the target analysis module is expressed by the following equation:

[0019]

number

[0020] Here, u1 is the abscissa of the center of the detection frame in the pixel coordinate system, v1 is the ordinate of the center of the detection frame in the pixel coordinate system, h is the height of the detection frame in the pixel coordinate system, u is the abscissa of the bottom center of the detection frame in the pixel coordinate system, and v is the ordinate of the bottom center of the detection frame in the pixel coordinate system.

[0021] The image analysis module converts the coordinates of the pixel coordinate system of the pedestrian in the image, and the coordinates are expressed by the following equation:

[0022]

number

[0023] where M is the transformation matrix and x w is the abscissa in the spatial coordinate system, and yw is the ordinate in the spatial coordinate system, and z w is the vertical coordinate under the spatial coordinate system, and z is the distance for human distance monitoring under the camera coordinate system.

[0024] The pedestrian flow velocity analysis relationship is expressed by the following equation:

[0025]

number

[0026] where v i (t) represents the instantaneous velocity of pedestrian i at time t, and w i (t) represents the walking distance of pedestrian i from time t to (t+T), and N represents the total number of pedestrians.

[0027]

number

[0028] where v - (where " - " indicates the overline of v) represents the time-averaged velocity, and X i,t represents the coordinates of the t-th frame of pedestrian i.

[0029] The density in spacetime is ρ, which is given by:

[0030]

number

[0031] Here, the space covered by the surveillance is divided into areas, and A m represents the area of ​​the mth area, and N t represents the number of pedestrians in the study area at time t. [Effects of the Invention]

[0032] By adopting the above technical means, the present invention has the following advantages over the prior art: First, in this invention, the image analysis module and the target recognition module are used to analyze the video information of people flow movement collected by the monitoring collection port, and the position coordinates of pedestrians in the people flow are converted through image frame processing and target recognition, and the movement data of pedestrians in the people flow is quickly determined through the video information, thereby realizing pedestrian flow statistics and remote monitoring, and improving the efficiency of people flow statistics and control. Second, in the present invention, the model analysis module combines the coordinate information converted by the image analysis module and the target identification module to construct a corresponding analysis model, and different models are adopted to analyze the people flow in different types of passage spaces on the evacuation route. The model analysis module matches the model with the passage space type to reduce errors caused by the model, and adapts the model analysis results to the actual people flow movement rules, thereby constructing different people flow analysis models to improve the accuracy of people flow data analysis. Third, in this invention, the linkage analysis module is combined with the model analysis module to perform linkage analysis for all nodes. When there is a target in the people flow movement, the linkage analysis module dynamically analyzes the overall people flow movement rules through the interaction relationships between different routes and the analysis results of the micro-models of different nodes. The people flow control module controls and directs people flow movement based on the prediction results of the linkage analysis module, thereby improving the accuracy of dynamic monitoring in the people flow evacuation process, strengthening the degree of cascading between different nodes, and avoiding human monitoring errors when remote monitoring personnel are looking at multiple monitoring screens. In the event of an emergency evacuation, people flow can be remotely controlled and directed to support the establishment of evacuation routes. [Brief explanation of the drawings]

[0033] [Figure 1] FIG. 1 is an overall module diagram according to the present invention. [Figure 2] 1 is an overall analysis flowchart according to the present invention. [Figure 3] 10 is an analysis flowchart of a linkage analysis module according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0034] The above and other technical contents, features and effects of the present invention can be clearly shown in the detailed description of the examples in the following formulation reference figures 1 to 3. The embodiments and features of the embodiments of the present application may be combined with each other, and the terms used in the specification have the meanings commonly understood by those skilled in the art of the present invention.

[0035] An embodiment of the present invention discloses a remote intelligence people flow statistical control system based on the Internet of Things (IoT), which includes a data collection module, a data storage module, a people flow control module, an image analysis module, a target recognition module, a model analysis module, and a linkage analysis module.

[0036] Campuses are crowded places, and when an unexpected event occurs and emergency evacuation is necessary, a large number of people will gather within the space. Take the statistics and application of people flow in and out of a school's teaching laboratory and machine room as an example. In an emergency, people flow within multiple laboratory rooms will rapidly flow out, and students in different locations will need to quickly evacuate. Analysis of people flow rules and people flow statistics is extremely important. To prevent crowd accidents when people flow is congested within the same space, the installation of evacuation routes and remote people flow statistics and control management are extremely important. Image recognition is used to analyze the movement characteristics of pedestrian flow along the aisles of a teaching building in a real-life scenario, and further analyze the movement characteristics to ensure orderly evacuation of different people within the evacuation route through people flow division and command. To prevent crossings and pedestrian interference between different evacuation routes, remote control personnel must coordinate the analysis of people flow at multiple monitoring points. The people flow control module statistics all evacuation routes within the building, labels the evacuation routes, and performs r iLet i∈[1,n], n is the number of evacuation routes in the building, and the n evacuation routes intersect to form an evacuation network. The people flow in the building flows into the evacuation end point from all directions, and the evacuation people flow passes through different evacuation routes. Different evacuation routes weave an evacuation network, and the number of monitoring and collection ports on each evacuation route is different. The spatial types of people flow movement are different, and the probability of people flow crowd accidents occurring is also different. In the process of people evacuation, the pedestrian movement characteristics change constantly, and the people flows between different evacuation routes influence each other, and different evacuation directions change.

[0037] The data collection module collects people flow movement data in different passage spaces through N monitoring collection ports, and stores the collected people flow movement data in a data storage module. i The number of monitoring collection ports is R i , R i ∈[1,N], the monitoring and collection ports are used to cover the different people flow movement spaces corresponding to the number of people flow inlet entrances, the monitoring and collection ports are used to cover the different people flow movement spaces corresponding to the number of people flow inlet entrances, the data collection module includes a monitoring device, a heat detection device, and an infrared detector at the monitoring and collection port, and all monitoring devices installed in the building monitor and cover the different people flow movement spaces.

[0038] Video information of people flow movement is collected through monitoring collection ports, the image analysis module performs image frame processing on the video information from different monitoring collection ports, and the target recognition module performs target tracking on moving targets in the people flow. The image analysis module and the target recognition module map the pixel coordinates of the image into a multidimensional space coordinate system. During the people flow movement process, the image analysis module and the target analysis module perform dynamic target analysis and detection on pedestrians in the collected people flow data, and then use the polygon detection method to calculate the people flow rate within the area.

[0039] The model analysis module combines the analysis processes of the model analysis module and the target recognition module to perform data statistics on the changes in people flow within the corridor space, and then constructs a corresponding micro-model based on the speed, density, and flow rate structure of people flow movement. Different micro-models are used for people flow movement spaces with different numbers of entrances to analyze the space-time people flow movement rules, and the space-time distribution characteristics of the speed and density of people flow movement within the corridor are obtained. The model structures in different types of corridor spaces will bring corresponding analysis errors, and optimal models will be constructed for different corridor spaces, making the analysis results closer to the actual intelligent people flow movement rules.

[0040] The linkage analysis module establishes an objective function by combining with the evacuation end point position of the people flow movement, and analyzes the feedback relationship between N nodes. The linkage analysis module further performs macro-linkage analysis on the people flow movement path characteristics of all passages in the evacuation network. The people flow movement space covered by one monitoring collection port is regarded as one node in the people flow movement path, and R i The system contains nodes, and the spatiotemporal distribution characteristics of the speed and density of different nodes are extracted to obtain the feature matrix of the entire evacuation route. Then, dynamic analysis is performed on the connections between different evacuation routes to obtain the influence value between evacuation routes. Finally, the linkage analysis module predicts the evacuation process of people flow through the established dynamic observation equation. The people flow control module is combined with the prediction results of the linkage analysis module to direct the evacuation of people in the building, so that the people flow movement follows the optimal evacuation route during emergency evacuation.

[0041] Furthermore, the linkage analysis module first determines the end position of the people flow movement, and then the pedestrians at different positions perform the people flow evacuation movement in a self-organized manner, and then performs dynamic observation through the overall analysis of the evacuation network consisting of all nodes. The linkage analysis module analyzes the overall people flow movement rules of all evacuation routes, and the specific meanings of the same letters in different formulas refer to the specific interpretation of the letters in the formulas, specifically: The spatial distance between the end point of the evacuation route and each node is X i, i∈[1,N], where N is the number of nodes. The linkage analysis module combines node data based on the end point of the people flow movement to determine the objective function f(t), where ?? represents time. The objective function is set according to the end point of the evacuation and the constraints required in the evacuation process. The constraints include time constraints, people flow load thresholds, evacuation distance, and people flow analysis parameters along the evacuation route. The node data includes spatial distance, people flow rate, and time. The end point coordinate is (x * ,y * ,z * ) and the spatial coordinates of the node are (x, y, z),

[0042]

number

[0043] Step 1: determining the process of people flow movement along different evacuation routes based on an objective function; Let the current node p be the merging node in the people flow path. Calculate the correlation coefficient between the current node p and the adjacent node q. S1 and S2 are the feature vectors between the current node p and the adjacent node q, respectively. The calculation formula is given by the following equation:

[0044]

number

[0045] Step 2: where ρ represents the correlation coefficient, Cov represents the covariance, and E represents the mathematical expectation value. The correlation between adjacent nodes is determined based on the value of the correlation coefficient. If ρ>0, the people flow movement rule between nodes is positive feedback; if ρ<0, the people flow movement rule between nodes is negative feedback. The node where two evacuation routes intersect is defined as an overlapping node, and the pedestrian flows of different evacuation routes are merged at the overlapping node. Then, a micro-analysis is performed on the pedestrian flow rules at the overlapping node using a model analysis module. At the overlapping node of evacuation routes, the pedestrian flows influence each other, and the degree of change in the pedestrian flow at the overlapping node of evacuation routes is expressed as a linkage value. The linkage value of the pedestrian flow between different evacuation routes is calculated to obtain the W ij where i and j represent the subscripts of the path, i, j∈[1,n], R i and R j are the number of nodes in the two evacuation routes i and j respectively. The movement speed, direction, density, and flow rate parameters between different evacuation routes influence each other. The generation and transformation of people flow direction and the linkage value are used to represent the people flow transformation rate between different evacuation routes.

[0046]

number

[0047] where U i (k) and U k j (k) is the node flow rate adjacent to the stacked node and having the same inflow direction of the pedestrian flow. The two nodes belong to two evacuation routes, respectively. k is the number of intersecting routes. X k , X ik , X jk Step 3 is the spatial distance, The linkage analysis module combines the analysis results of the model analysis module to analyze the spatiotemporal distribution characteristics of the speed and density of different nodes on all evacuation routes, and derives the corresponding feature matrix A. nRi is obtained, and R i ∈[1,N], the linkage analysis module combines the linkage degree between different evacuation routes, performs dynamic observation of the dynamic state of different nodes in the process of people flow, and makes judgments on the observed values.

[0048]

number

[0049] where:

[0050]

number

[0051] η1 is velocity, η2 is density, η3 is flow rate, η4 is the covered space, t i Step 4 includes: a people flow evacuation time, t representing different time values; a dynamic prediction model predicts parameter fluctuations in the evacuation process based on changes in predicted values ​​at different times; and combines with a set objective function to determine the predicted values; and if the predicted value of the interlocking analysis module is abnormal, the people flow control module sends a people flow diversion command.

[0052] The target recognition module recognizes multiple pedestrians in the monitored video, and then converts the coordinates of successfully recognized pedestrians and corrects their positions based on the recognition effect judgment. After the image analysis module and the target recognition module convert the coordinates of the collected data, the model analysis module analyzes and calculates the pedestrian flow density and pedestrian flow speed within the spatial area. Based on the spatiotemporal distribution of pedestrian target positions, an analytical model is constructed to obtain basic diagrams of the spatiotemporal distribution of pedestrian flow speed and density, and then the movement rules of the pedestrian flow are analyzed through the basic diagram.

[0053] Furthermore, the model analysis module constructs different models based on the type of passage space to analyze people flow movement rules. The model analysis module constructs models based on different people flow movement spaces and uses model judgment criteria to judge the models so that the constructed models are consistent with the actual people flow movement regulations. Based on the number of people flow junctions in the area covered by the current monitoring collection port, the passages are classified into Y-shaped passages, straight passages, T-shaped passages, and cross passages. The number of people flow junction entrances for Y-shaped and T-shaped passages is three, and the number of entrances for straight passages is two.

[0054] Furthermore, in order to ensure that the letters in different formulas represent unique mathematical meanings and are not confused with the same letters in other formulas, the relationship between the bottom and center coordinates of the detection frame of the target analysis module is expressed by the following formula:

[0055]

number

[0056] Here, u1 is the abscissa of the center of the detection frame in the pixel coordinate system, v1 is the ordinate of the center of the detection frame in the pixel coordinate system, h is the height of the detection frame in the pixel coordinate system, u is the abscissa of the bottom center of the detection frame in the pixel coordinate system, and v is the ordinate of the bottom center of the detection frame in the pixel coordinate system.

[0057] The image recognition technology of the image analysis module can obtain more objective people flow data and provide multi-dimensional verification criteria. Image recognition is an important means of data acquisition. When inspectors monitor people flow through multiple surveillance cameras, they can determine the people flow movement space collected by one surveillance camera through data analysis. However, when people flow in multiple locations needs to be monitored at the same time, the human naked eye cannot make objective evaluations and calculations, and there will be information cascades between different people flow data. Furthermore, the image analysis module converts the coordinates of the pixel coordinate system of pedestrians in the image, which can be expressed by the following formula:

[0058]

number

[0059] where M is the transformation matrix and x w is the abscissa in the spatial coordinate system, and y w is the ordinate in the spatial coordinate system, and z w is the vertical coordinate in the spatial coordinate system, z is the distance for human distance monitoring in the camera coordinate system, The matrix M is actually a multiplication of two matrices, namely M=M1M2, where M1 is the camera's matrix parameters and M2 is the camera's extrinsic parameter matrix.

[0060]

number

[0061] R is a matrix related to the tilt angle of the camera, t is a matrix related to the position of the camera in the world coordinate system, u0 is the abscissa of the image center point, v0 is the ordinate of the image center point, and α x is the focal length and the x-direction pixel conversion ratio, and α y is the focal length and the y-direction pixel conversion rate, and f is the camera focal length.

[0062] Furthermore, the pedestrian flow velocity analysis relationship is expressed by the following equation:

[0063]

number

[0064] where v i (t) represents the instantaneous velocity of pedestrian i at time t, and w i (t) represents the walking distance of pedestrian i from time t to (t+T), and N represents the total number of pedestrians.

[0065]

number

[0066] where v - (where " - " indicates the overline of v) represents the time-averaged velocity, and X i,trepresents the coordinates of pedestrian i in the t-th frame. The speed of the pedestrian flow directly affects the accuracy of image collection, and the spatial distribution characteristics of speed are related to the movement status of each individual in the pedestrian flow. When individuals stand or gather in groups in the pedestrian flow, this affects the speed analysis process.

[0067] Furthermore, the density in spacetime is ρ, given by:

[0068]

number

[0069] Here, the space covered by the surveillance is divided into areas, and A m represents the area of ​​the mth area, and N t represents the number of pedestrians in the study area at time t. The methods for calculating pedestrian flow density vary depending on the corridor. Pedestrian density and speed are analyzed within the polygon formed by pedestrians, and the average density and average speed within the polygon are calculated. Polygonal statistical methods are generally consistent with traditional statistical methods, but are very time-consuming. Polynomials are used to fit pedestrian coordinates, 2D kernel density estimation, relative density distribution maps, and pedestrian location preference characteristics. Density spatial change characteristics are obtained from pedestrian coordinates, density statistics, maximum density change trends, and density spatial distribution. Special behaviors such as waiting, loitering, turning around, embracing, and running are individually filtered out. Pedestrian coordinate spatiotemporal data, the spatial distribution of speed, and linear interpolation function approximation and average nearest neighbor distance analysis are used to obtain the spatial speed component.

[0070] In specific use of the present invention, the system includes a data collection module, a data storage module, a people flow control module, an image analysis module, a target recognition module, a model analysis module, and a linkage analysis module. The data collection module collects people flow data of different passage spaces of people flow movement through monitoring collection ports, and records the collection process of different monitoring collection ports as a node. The image analysis module and the target recognition module dynamically analyze the movement trajectories of pedestrian targets in the people flow. The model analysis module further analyzes a model matching the people flow movement regular structure in different nodes, and analyzes the people flow movement characteristics in the corresponding space through different monitored video information. The people flow movement characteristics and research models of different passage spaces are different. The linkage analysis module performs overall dynamic analysis on the evacuation network consisting of all nodes, firstly connects to the evacuation end position of the people flow movement to establish an objective function, then analyzes the correlation between the interactions between different nodes to obtain a correlation coefficient, then analyzes the mutual influence between the evacuation routes to obtain the linkage value between different evacuation routes, finally establishes a dynamic observation equation and combines and analyzes the objective function, the people flow control module performs remote control of the people flow based on the monitoring results of the linkage analysis module, performs dynamic analysis on the entire node through the linkage analysis module, greatly improves the efficiency of intelligent people flow remote control, the people flow control module sends corresponding people flow control commands based on the dynamic changes of the people flow, and combines the micro model analysis of people flow with the overall cascade analysis process through the combination of the model analysis module and the linkage analysis module, reduces the error of people flow statistical control, and makes the analysis results of people flow movement more consistent with actual people flow movement regulation.

[0071] Although the present invention has been described in more detail above based on specific embodiments, it is readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent modifications or substitutions to the relevant technical features, and all technical proposals after these modifications or substitutions fall within the scope of protection of the present invention.

Claims

1. It includes a data collection module, a data storage module, a people flow control module, an image analysis module, a target recognition module, a model analysis module, and a linkage analysis module. The people flow control module compiles statistics of all evacuation routes in the building and labels the evacuation routes. i Let i∈[1, n], n is the number of evacuation routes in the building, and n evacuation routes intersect to form an evacuation net. The data collection module collects people flow movement data in different passage spaces through N monitoring collection ports, and stores the collected people flow movement data in a data storage module. i The number of monitoring collection ports is R i and R i ∈[1, N], and the monitoring collection ports cover the different numbers of inlets of the people flow movement space, Video information of people flow movement is collected through monitoring collection ports, the image analysis module performs image frame processing on the video information from different monitoring collection ports, and the target recognition module performs target tracking on the moving target in the people flow, and the image analysis module and the target recognition module map the pixel coordinates of the image into a multidimensional spatial coordinate system; The model analysis module combines the analysis processes of the model analysis module and the target recognition module to perform data statistics on the changes in people flow within the passage space, and then constructs a corresponding micro model based on the speed, density, and flow rate structure of people flow movement. Different micro models are used for people flow movement spaces with different numbers of entrances to analyze the movement patterns of people flow within the space-time, and the spatiotemporal distribution characteristics of the speed and density of people flow movement within the passage are obtained. The linkage analysis module establishes an objective function by combining with the evacuation end point position of the people flow movement, and analyzes the feedback relationship between N nodes. The linkage analysis module further performs macro-linkage analysis on the people flow movement path characteristics of all passages in the evacuation network. The people flow movement space covered by one monitoring collection port is regarded as one node in the people flow movement path, and R i The system contains nodes, and the spatiotemporal distribution characteristics of the speed and density of different nodes are extracted to obtain the feature matrix of the entire evacuation route. Then, dynamic analysis is performed on the connections between different evacuation routes to obtain the influence value between evacuation routes. Finally, the linkage analysis module predicts the evacuation process of people flow through the established dynamic observation equation. The people flow control module is combined with the prediction results of the linkage analysis module to direct the evacuation of people within the building, and ensures that people evacuate along the optimal evacuation route during an emergency evacuation. This is a remote intelligence people flow statistical control system based on the Internet of Things (IoT).

2. The linkage analysis module first determines the end point of the pedestrian flow, and then the pedestrians at different locations perform a self-organized evacuation movement of the pedestrian flow. Then, it performs dynamic observation through a comprehensive analysis of the evacuation network consisting of all nodes. Specifically, The spatial distance between the end point of the evacuation route and each node is X i , i∈[1, N], the linkage analysis module determines the objective function f(t) by combining the node data based on the end point position of the people flow movement. The node data includes the spatial distance, the people flow rate and time, and the end point coordinate is (x * , y * , z * ) and the spatial coordinates of the node are (x, y, z), [Equation 22] Step 1: determining the process of people flow movement along different evacuation routes based on an objective function; Let the current node p be the merging node in the pedestrian flow path, and calculate the correlation coefficient between the current node p and the adjacent node q. S1 and S2 are the feature vectors between the current node p and the adjacent node q, respectively. The calculation formula is expressed as follows: [Equation 23] Step 2: ρ represents the correlation coefficient, Cov represents the covariance, and E represents the mathematical expectation value. Based on the value of the correlation coefficient, the correlation between adjacent nodes is determined. If ρ>0, the people flow movement rule between the nodes is positive feedback, and if ρ<0, the people flow movement rule between the nodes is negative feedback. The node where two evacuation routes intersect is defined as an overlapping node, and the people flows of different evacuation routes are merged at the overlapping node. Then, the model analysis module performs a micro-analysis of the people flow rules at the overlapping node. The degree of change in people flow at the overlapping node of the evacuation routes is expressed as a linkage value, and the linkage value of people flow between different evacuation routes is calculated to obtain the W ij where i and j represent the subscripts of the path, i, j∈[1, n], R i and R j are the numbers of nodes in the two evacuation routes i and j, respectively, [0000] Here, U i (k) and U ｋ j (k) is the node flow rate adjacent to the stack node and having the same inflow direction of the pedestrian flow. The two nodes belong to two evacuation routes, respectively. k is the number of intersecting routes. X k , X iｋ , X jｋ Step 3 is the spatial distance, The linkage analysis module combines the analysis results of the model analysis module to analyze the spatiotemporal distribution characteristics of the speed and density of different nodes on all evacuation routes, and derives the corresponding feature matrix A. nRi is obtained, and R i ∈[1, N], and the linkage analysis module combines the linkage degree between different evacuation routes to dynamically observe the dynamic states of different nodes in the process of people flow, and makes judgments based on the observed values. [Equation 25] where: [Equation 26] η １ is the velocity, η ２ is the density, η ３ is the flow rate, η ４ is the covered space, t i The remote intelligence people flow statistical control system based on the Internet of Things (IoT) of claim 1, further comprising: step 4, wherein the people flow evacuation time, t, represents different time values, and the dynamic prediction model predicts the parameter fluctuations of the evacuation process based on the changes of the predicted values ​​at different times, and combines the predicted values ​​with the set objective function to determine the predicted values.

3. 2. The remote intelligent people flow statistics and control system based on the Internet of Things (IoT) of claim 1, wherein the target recognition module recognizes multiple pedestrians in the monitored video, and then converts the coordinates of successfully recognized pedestrians and corrects their positions based on the recognition effect; the image analysis module and the target recognition module convert the coordinates of the collected data, and then the model analysis module analyzes and calculates the people flow density and people flow speed within the spatial area, and obtains a basic map of the spatiotemporal distribution of pedestrian flow speed and spatiotemporal distribution of pedestrian flow density through an analytical model constructed based on the spatiotemporal distribution of pedestrian target positions, and further analyzes the movement rules of the people flow through the basic map.

4. The remote intelligence people flow statistical control system based on the Internet of Things (IoT) according to claim 1, characterized in that the model analysis module builds different models based on the type of passage space to analyze people flow movement rules, and classifies passages including Y-shaped passages, straight passages, T-shaped passages and cross passages based on the number of people flow junctions in the area covered by the current monitoring collection port.

5. The relationship between the bottom and center coordinates of the detection frame of the target recognition module is expressed by the following equation: [0000] where u １ is the horizontal coordinate of the center of the detection frame in the pixel coordinate system, and v １ 4. The remote intelligence people flow statistical control system based on the Internet of Things (IoT) of claim 3, wherein x is the ordinate of the center of the detection frame in the pixel coordinate system, h is the height of the detection frame in the pixel coordinate system, u is the abscissa of the center of the bottom of the detection frame in the pixel coordinate system, and v is the ordinate of the center of the bottom of the detection frame in the pixel coordinate system.

6. The image analysis module converts the coordinates of the pixel coordinate system of the pedestrian in the image, and the coordinates are expressed by the following equation: [0000] where M is the transformation matrix and x w is the abscissa in the spatial coordinate system, and y w is the ordinate in the spatial coordinate system, and z w The remote intelligence people flow statistical control system based on the Internet of Things (IoT) according to claim 3, characterized in that z is the vertical coordinate under the spatial coordinate system, and z is the distance of people distance monitoring under the camera coordinate system.

7. The pedestrian flow velocity analysis relationship is expressed by the following equation: [0000] where v i (t) represents the instantaneous velocity of pedestrian i at time t, and w i (t) represents the walking distance of pedestrian i from time t to (t+T), N represents the total number of pedestrians, [Equation 30] where v － (where " - " indicates the overline of v) represents the time-averaged velocity, and X i，t The remote intelligence people flow statistical control system based on the Internet of Things (IoT) according to claim 3, characterized in that:

8. The density in spacetime is ρ, which is given by: [Equation 31] Here, the space covered by the surveillance is divided into areas, and A m represents the area of ​​the mth area, and N t The remote intelligence people flow statistical control system based on the Internet of Things (IoT) according to claim 3, characterized in that: t represents the number of pedestrians in the study area at time t.

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