A passenger flow real-time monitoring system and method

Abstract

This invention relates to the field of data monitoring technology, specifically disclosing a real-time passenger flow monitoring system and method. The system includes a traffic condition terminal for acquiring real-time vehicle information of non-public transportation vehicles and determining virtual visitor information based on the vehicle information; a monitoring terminal for acquiring real-time image information of the area and generating a target image based on the image information; and a server terminal for receiving visitor information acquired by the traffic condition terminal, reading the visitor probability of corresponding nodes based on the location of the traffic condition terminal, and calculating the number of visitors based on the visitor probability and the visitor information; receiving the target image acquired by the monitoring terminal, determining the number of visitors based on the target image, and determining the passenger flow based on the number of visitors, the number of visitors, and the outbound flow. This invention obtains passenger flow outside the area through the cooperation of the traffic condition terminal and the server terminal, and obtains passenger flow within the area through the cooperation of the monitoring terminal and the server terminal, resulting in more comprehensive and accurate flow monitoring and facilitating its widespread adoption.

Description

Technical Field

[0001] This invention relates to the field of data monitoring technology, specifically a real-time passenger flow monitoring system and method. Background Technology

[0002] With the continuous development of computer application technology, real-time visitor flow monitoring technology is increasingly being applied to various scenarios to monitor visitor flow data in real time, such as scenic spots, large shopping malls, large conference venues, sports event venues, and other large-scale event venues. This allows for flexible development of management plans for various event venues based on real-time visitor flow data, preventing emergencies. For example, for scenic spots, real-time monitoring of visitor flow data and timely push notifications to users not only facilitates off-peak travel and enhances the visitor experience but also facilitates the management of the scenic area.

[0003] However, existing visitor flow monitoring systems only perform simple statistics on the current visitor flow inside the event venue, ignoring the visitor flow outside the event venue that may flood into the event venue in a short period of time, resulting in inaccurate monitoring data on the on-site visitor flow. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time passenger flow monitoring system and method to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A real-time passenger flow monitoring system, the system specifically comprising:

[0007] The traffic information terminal is used to acquire vehicle information in real time and determine virtual visitors based on the vehicle information of non-public transportation vehicles; wherein, the vehicle information includes the number of vehicles and the vehicle capacity; the number of tickets for public transportation vehicles with the on-site location as the destination is acquired to determine the actual visitors; visit information is generated based on the virtual visitors and the actual visitors, and the visit information is sent to the server;

[0008] The monitoring terminal is used to acquire image information in the field area in real time, generate target images based on the image information, and send the target images to the server at regular intervals.

[0009] The server is used to acquire the on-site location, determine the monitoring area based on the on-site location and a preset area radius, and acquire the traffic plans within the monitoring area with the on-site location as the destination; sequentially read the traffic plans, determine the nodes of the traffic plans, classify the nodes according to the distance of the nodes to the on-site location, and determine the visit probability based on the classification results; receive visit information acquired by the traffic condition terminal, read the visit probability of the corresponding node according to the location of the traffic condition terminal, and calculate the number of visitors based on the visit probability and the visit information; receive the target image acquired by the monitoring terminal, determine the number of visitors in the on-site area based on the target image, and determine the on-site passenger flow based on the number of visitors, the number of visitors, and the outbound flow, wherein all the above data are data per unit time.

[0010] The calculation process for determining the on-site visitor flow N based on the number of visitors, incoming personnel, and outgoing flow can be expressed by the following mathematical formula:

[0011] Where n represents the number of road condition ends on different access routes to the site location, N i ω represents the virtual visitor on the i-th traffic terminal. i P represents the probability value of the visit corresponding to the i-th traffic condition terminal. i Let m represent the actual visitors at the i-th traffic condition terminal, k represent the number of non-public transportation vehicles at the i-th traffic condition terminal, and m represent the number of visitors at the i-th traffic condition terminal. i n represents the number of vehicles belonging to the same type of non-public transportation on the i-th road condition end. i ρ represents the carrying capacity of the same type of non-public transport vehicles on the i-th road condition end. i Q represents the occupancy rate of the same type of non-public transportation vehicles on the i-th road condition end, Q represents the number of visitors in the on-site area, and T represents the outbound traffic flow counted at the on-site exit.

[0012] As a further limitation of the technical solution of the present invention, the road condition terminal specifically includes:

[0013] The first personnel identification module is used to acquire vehicle information of non-public transportation vehicles in real time and identify virtual visitors based on the vehicle information; wherein, the vehicle information includes the number of vehicles and the vehicle carrying capacity;

[0014] The second personnel identification module is used to obtain the number of tickets for public transportation with the on-site location as the destination, and to identify the actual visitors;

[0015] The visitor information generation module is used to generate visitor information based on the virtual visitor and the actual visitor, and send the visitor information to the server.

[0016] As a further limitation of the technical solution of the present invention, the server specifically includes:

[0017] The scheme acquisition module is used to acquire the on-site location, determine the monitoring area based on the on-site location and a preset area radius, and acquire the travel schemes within the monitoring area with the on-site location as the destination.

[0018] The probability determination module is used to sequentially read the access plan, determine the nodes of the access plan, classify the nodes according to the distance of the nodes to the site location, and determine the probability of visit based on the classification results of the nodes.

[0019] The visitor calculation module is used to receive visitor information obtained from the traffic condition terminal, read the visitor probability of the corresponding node according to the location of the traffic condition terminal, and calculate the visitor based on the visitor probability and the visitor information.

[0020] The visitor flow determination module is used to receive the target image obtained by the monitoring terminal, determine the number of visitors in the on-site area based on the target image, and determine the on-site visitor flow based on the number of visitors, the number of visitors, and the outflow of the area.

[0021] The data mentioned above are all data within a unit of time, and the outflow traffic is obtained in real time from the on-site exit.

[0022] As a further limitation of the technical solution of the present invention, the monitoring terminal specifically includes:

[0023] The selection module is used to acquire image information within the field area in real time and select image information within a preset time period as a preset image.

[0024] The determining module is used to determine a reference heat source in the preset image, wherein the reference heat source corresponds to a reference target;

[0025] The capture module is used to capture the target heat source in the preset image, the target heat source corresponding to the moving target;

[0026] The judgment module is used to determine the separation distance between the reference heat source and the target heat source in the region image. When the separation distance is greater than a threshold, the preset image is confirmed as the target image, and the target image is sent to the server at regular intervals.

[0027] As a further limitation of the technical solution of the present invention, the first personnel determination module specifically includes:

[0028] The classification unit is used to obtain the types of non-public transportation vehicles in real time and determine the vehicle carrying capacity based on the vehicle types.

[0029] The total load capacity calculation unit is used to obtain the number of vehicles and calculate the total load capacity based on the number of vehicles and the corresponding vehicle load capacity.

[0030] The first execution unit is used to determine virtual visitors based on the preset load factor and the total capacity.

[0031] As a further limitation of the technical solution of the present invention, the visitor calculation module specifically includes:

[0032] The location determination unit is used to sequentially receive the incoming visit information obtained from the traffic condition terminal and obtain its location information;

[0033] The node determination unit is used to determine the traffic plan where the road condition terminal is located based on the location information, and to determine the node based on the distance between the location information and the on-site location, and to read the corresponding visit probability.

[0034] The second execution unit is used to obtain virtual visitors from the visit information, generate predicted visitors based on the virtual visitors and the visit probability, and generate visitors based on the predicted visitors and the actual visitors.

[0035] The probability of a visit is related to the node level; the closer the road condition is to the location on-site, the greater the probability of a visit.

[0036] As a further limitation of the technical solution of the present invention, the passenger flow determination module specifically includes:

[0037] A contour recognition unit is used to receive a target image acquired by a monitoring terminal, perform contour recognition on the target image, and obtain a feature contour.

[0038] The feature extraction unit is used to generate a feature region based on the feature contour, sequentially traverse the pixels of the feature region, and generate feature values ​​based on the color values ​​of the pixels.

[0039] A marking unit is used to determine a hair region based on the feature value, and to mark the feature region when the feature region is a hair region;

[0040] The third execution unit is used to obtain the number of tags, which is used as the number of visitors.

[0041] As a further limitation of the technical solution of the present invention, the contour recognition unit specifically includes:

[0042] The color value confirmation subunit is used to traverse the pixels in the region image and obtain the color value of the pixel;

[0043] The marking subunit is used to confirm the tolerance and read the color values ​​of adjacent pixels in sequence, and determine the size of the color value difference between adjacent pixels and the tolerance. If the color value difference between adjacent pixels is greater than the tolerance, the pixel is marked. If the color value difference between adjacent pixels is less than the tolerance, the next adjacent pixel is read.

[0044] Connecting subunits are used to generate feature contours based on labeled pixels.

[0045] The present invention also provides a method for real-time monitoring of passenger flow, the method being applied to a server, and the method specifically includes:

[0046] The system acquires the on-site location, determines the monitoring area based on the on-site location and a preset area radius, and acquires the travel routes within the monitoring area with the on-site location as the destination.

[0047] The passage plan is read sequentially, the nodes of the passage plan are determined, the nodes are classified according to the distance of the nodes to the site location, and the probability of visit is determined according to the classification results of the nodes.

[0048] Receive visitor information obtained from the traffic condition terminal, read the visitor probability of the corresponding node according to the location of the traffic condition terminal, and calculate the visitor based on the visitor probability and the visitor information;

[0049] The system receives target images acquired by the monitoring terminal, determines the number of visitors in the on-site area based on the target images, and determines the on-site passenger flow based on the number of visitors, the number of visitors, and the outflow of traffic.

[0050] The data mentioned above are all data within a unit of time, and the outflow traffic is obtained in real time from the on-site exit.

[0051] As a further limitation of the technical solution of the present invention, the steps of receiving the visitor information obtained by the traffic condition terminal, reading the visitor probability of the corresponding node according to the location of the traffic condition terminal, and calculating the visitor based on the visitor probability and the visitor information specifically include:

[0052] It sequentially receives incoming visitor information obtained from the traffic condition terminal and obtains their location information;

[0053] The traffic plan for the road condition terminal is determined based on the location information, and the node is determined based on the distance between the location information and the actual location, and the corresponding probability of arrival is read.

[0054] Obtain virtual visitors from the visitor information, generate predicted visitors based on the virtual visitors and the visit probability, and generate actual visitors based on the predicted visitors and the actual visitors;

[0055] The probability of a visit is related to the node level; the closer the road condition is to the location on-site, the greater the probability of a visit.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] This invention collects visitor information through a traffic monitoring terminal and, in conjunction with a server, calculates the total number of visitors at various traffic monitoring terminals leading to the site from outside the site. It also uses a monitoring terminal in conjunction with the server to obtain real-time passenger flow within the site area and simultaneously counts outbound traffic within the site area. By calculating the total number of visitors and the sum of passenger flow within the site area, and then subtracting the outbound traffic, the final monitoring value of the on-site passenger flow is obtained. This invention provides more comprehensive and accurate passenger flow monitoring and is easy to promote. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0059] Figure 1 The architecture diagram of the real-time passenger flow monitoring system is shown.

[0060] Figure 2 The diagram shows the structural composition of the road condition terminal in the real-time passenger flow monitoring system.

[0061] Figure 3 The diagram shows the structural composition of the server side in the real-time passenger flow monitoring system.

[0062] Figure 4 The diagram shows the structural composition of the monitoring terminal in the real-time passenger flow monitoring system.

[0063] Figure 5 The diagram shows the structural block diagram of the first person determination module in the road condition terminal.

[0064] Figure 6 The diagram shows the structural block diagram of the visitor calculation module in the server.

[0065] Figure 7 The diagram shows the structural composition of the passenger flow determination module in the server.

[0066] Figure 8 The diagram shows the structural composition of the contour recognition unit in the passenger flow determination module.

[0067] Figure 9 A flowchart of a method for real-time passenger flow monitoring is shown.

[0068] Figure 10The flowchart of the real-time passenger flow monitoring method is shown. Detailed Implementation

[0069] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0070] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0071] It should be understood that although the terms "first," "second," etc., may be used in embodiments of the invention to describe different modules, these modules should not be limited to these terms. These terms are only used to distinguish modules of the same type from each other. For example, without departing from the scope of embodiments of the invention, a first person determination module may also be referred to as a second person determination module, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Similarly, a second person determination module may also be referred to as a first person determination module. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0072] Example 1

[0073] Figure 1 The diagram illustrates the architecture of a real-time passenger flow monitoring system. In this embodiment of the invention, a real-time passenger flow monitoring system and method are provided, wherein system 10 specifically includes:

[0074] Traffic terminal 11 is used to obtain real-time vehicle information of non-public transportation vehicles and determine virtual visitors based on the vehicle information; wherein, the vehicle information includes the number of vehicles and the vehicle capacity; obtain the number of tickets of public transportation vehicles with the current location as the destination to determine the actual visitors; generate visit information based on the virtual visitors and the actual visitors, and send the visit information to the server;

[0075] The monitoring terminal 12 is used to acquire image information in the field area in real time, generate target images based on the image information, and send the target images to the server at regular intervals.

[0076] Server 10 is used to acquire the on-site location, determine the monitoring area based on the on-site location and a preset area radius, and acquire the traffic plans within the monitoring area with the on-site location as the destination; sequentially read the traffic plans, determine the nodes of the traffic plans, classify the nodes according to the distance from the nodes to the on-site location, and determine the visit probability based on the classification results of the nodes; receive visit information acquired by the traffic condition terminal, read the visit probability of the corresponding nodes according to the location of the traffic condition terminal, and calculate the number of visitors based on the visit probability and the visit information; receive the target image acquired by the monitoring terminal, determine the number of visitors in the on-site area based on the target image, and determine the on-site passenger flow based on the number of visitors, the number of visitors, and the outbound flow, wherein all the above data are data within a unit of time, and the outbound flow is acquired in real time from the on-site exit.

[0077] The technical solution of this invention is a three-terminal system. A traffic condition terminal, in conjunction with a server, acquires passenger flow outside the designated area. A monitoring terminal, also in conjunction with the server, acquires passenger flow within the designated area. Specifically, the traffic condition terminal obtains the number of people on the route to the site, and the server calculates the number of people outside the designated area based on this data. The monitoring terminal acquires real-time images of the designated area, and the server uses these images to determine the number of people. The traffic condition terminal is installed at specific nodes, meaning there is a one-to-one mapping between the addresses of different traffic condition terminals and nodes on the route. The monitoring terminal can be a camera within the designated area. Notably, to improve image recognition capabilities, the camera has temperature recognition capabilities.

[0078] Figure 2 The diagram shows the structural composition of the traffic condition terminal in the real-time passenger flow monitoring system. The traffic condition terminal 11 specifically includes:

[0079] The first personnel identification module 111 is used to acquire vehicle information of non-public transportation vehicles in real time and identify virtual visitors based on the vehicle information; wherein, the vehicle information includes the number of vehicles and the vehicle carrying capacity;

[0080] The second personnel identification module 112 is used to obtain the number of tickets for public transportation with the on-site location as the destination, and to identify the actual visitors;

[0081] The visitor information generation module 113 is used to generate visitor information based on the virtual visitor and the actual visitor, and send the visitor information to the server.

[0082] The above mainly makes a simple distinction between virtual visitors and actual visitors. Actual visitors are determined by their tickets, such as subway or bus tickets, and their destination is the actual location. Virtual visitors, on the other hand, may be vehicles heading to the actual location. It can be assumed that if they are on the route to the actual location, they have the intention to go there. It can also be assumed that the closer they are to the actual location, the clearer their intention is, meaning that the proportion of actual visitors among virtual visitors is larger.

[0083] Figure 3 The diagram shows the structural composition of the server in a real-time passenger flow monitoring system. The server 10 specifically includes:

[0084] The scheme acquisition module 101 is used to acquire the on-site location, determine the monitoring area based on the on-site location and a preset area radius, and acquire the travel schemes within the monitoring area with the on-site location as the destination.

[0085] The probability determination module 102 is used to sequentially read the access plan, determine the nodes of the access plan, classify the nodes according to the distance of the nodes to the site location, and determine the probability of visit based on the classification results of the nodes.

[0086] The visitor calculation module 103 is used to receive visitor information obtained from the traffic condition terminal, read the visitor probability of the corresponding node according to the location of the traffic condition terminal, and calculate the visitor based on the visitor probability and the visitor information.

[0087] The visitor flow determination module 104 is used to receive the target image obtained by the monitoring terminal, determine the number of visitors in the on-site area based on the target image, and determine the on-site visitor flow based on the number of visitors, the number of visitors, and the outflow of the area.

[0088] The data mentioned above are all data within a unit of time, and the outflow traffic is obtained in real time from the on-site exit.

[0089] The purpose of the scheme acquisition module is to obtain travel schemes. These schemes can be obtained using map services. Generally, the system uses the current location as the center and acquires travel schemes that may lead to the current location in a rotating order. However, to reduce computational complexity, the scheme acquisition module adopts a simpler approach: first, it defines a region, and then acquires travel schemes within that region that are destined for the current location. The starting point is typically the boundary of the region. This generates travel schemes that cover most road segments. Different road conditions are categorized into different levels based on their distance from the current location. It's conceivable that in travel schemes destined for the current location, road conditions closer to the current location have higher levels and a higher probability of arrival. The probability determination module's purpose is to perform probability differentiation. After acquiring arrival information from a road condition, it sends it to the server. The server obtains the arrival probability based on the location, calculates the number of visitors for that road condition based on the arrival information and probability, and then counts the total number of visitors from all road conditions to obtain the aforementioned number of visitors.

[0090] Figure 4 The diagram shows the structural composition of the monitoring terminal in a real-time passenger flow monitoring system. The monitoring terminal 12 specifically includes:

[0091] The selection module 121 is used to acquire image information within the field area in real time and select image information within a preset time period as a preset image.

[0092] The determining module 122 is used to determine a reference heat source in the preset image, wherein the reference heat source corresponds to a reference target;

[0093] The capture module 123 is used to capture the target heat source in the preset image, wherein the target heat source corresponds to a moving target;

[0094] The judgment module 124 is used to judge the separation distance between the reference heat source and the target heat source in the region image. When the separation distance is greater than a threshold, the preset image is confirmed as the target image, and the target image is sent to the server at regular intervals.

[0095] The above describes the processing of image information. To reduce the difficulty of recognition on the server side, not all image information collected by the monitoring terminal is uploaded to the server. Instead, heat source identification is performed on the image information first, and only dynamic images are used as target images. Of course, in some areas with a large flow of people, it is also feasible to directly use all image information collected by the monitoring terminal as target images.

[0096] Figure 5 The diagram shows the structural block diagram of the first personnel determination module in the road condition terminal. The first personnel determination module 111 specifically includes:

[0097] Classification unit 1111 is used to obtain the types of non-public transportation vehicles in real time and determine the vehicle carrying capacity based on the vehicle types.

[0098] The total load capacity calculation unit 1112 is used to obtain the number of vehicles and calculate the total load capacity based on the number of vehicles and the corresponding vehicle load capacity.

[0099] The first execution unit 1113 is used to determine the virtual visitors based on the preset load factor and the total capacity.

[0100] To determine the virtual visitors on the road, the carrying capacity of different types of non-public transportation vehicles at various road conditions is assessed. Since the occupancy rates of different vehicle types vary, a large amount of sample data can be used to determine the occupancy rates of different vehicle types, thereby calculating the virtual visitors. Specifically, the calculation of the virtual visitor H at a given road condition can be expressed by the following formula:

[0101] Where k represents the number of types of non-public transportation vehicles on the road, m i n represents the number of non-public transportation vehicles of the same type. i ρ represents the carrying capacity of vehicles of the same type that are not public transportation vehicles. i This represents the occupancy rate of vehicles of the same type that are not public transportation vehicles.

[0102] Figure 6 The diagram shows the structural block diagram of the visitor calculation module in the server. The visitor calculation module 103 specifically includes:

[0103] The location determination unit 1031 is used to sequentially receive the incoming visit information obtained from the traffic condition terminal and obtain its location information;

[0104] The node determination unit 1032 is used to determine the traffic plan where the road condition terminal is located based on the location information, and to determine the node based on the distance between the location information and the on-site location, and to read the corresponding visit probability.

[0105] The second execution unit 1033 is used to obtain virtual visitors from the visit information, generate predicted visitors based on the virtual visitors and the visit probability, and generate visitors based on the predicted visitors and the actual visitors.

[0106] The probability of a visit is related to the node level; that is, the closer the road condition is to the actual location, the higher its probability of a visit. The core step in the above is the calculation process in the second execution unit, which is explained using a formula for clarity:

[0107] Where Z represents the visitors at each end of the different access routes to the site, n represents the number of ends of the different access routes to the site location, and N represents the number of ends of the different access routes to the site location. i ω represents the virtual visitor on the i-th traffic terminal. i P represents the probability value of the visit corresponding to the i-th traffic condition terminal. i Let m represent the actual visitors at the i-th traffic condition terminal, k represent the number of non-public transportation vehicles at the i-th traffic condition terminal, and m represent the number of visitors at the i-th traffic condition terminal. i n represents the number of vehicles belonging to the same type of non-public transportation on the i-th road condition end. i ρ represents the carrying capacity of the same type of non-public transport vehicles on the i-th road condition end. i This represents the occupancy rate of the same type of non-public transportation vehicles on the i-th road condition end.

[0108] As can be seen from the above formula, the probability of a visit does not affect the actual number of visitors. The actual number of visitors is determined by the number of tickets sold on each road condition platform for public transportation with the destination address being the on-site location. Public transportation includes buses, subways, etc.

[0109] Figure 7 The diagram shows the structural composition of the passenger flow determination module in the server. The passenger flow determination module 104 specifically includes:

[0110] The contour recognition unit 1041 is used to receive the target image obtained by the monitoring terminal, perform contour recognition on the target image, and obtain the feature contour.

[0111] The feature extraction unit 1042 is used to generate a feature region based on the feature contour, sequentially traverse the pixels of the feature region, and generate feature values ​​based on the color values ​​of the pixels.

[0112] The marking unit 1043 is used to determine the hair region based on the feature value, and to mark the feature region when the feature region is a hair region;

[0113] The third execution unit 1044 is used to obtain the number of tags as the number of visitors.

[0114] In an image containing a large number of people, the most convenient way to obtain the number of people is to judge based on the number of heat sources. However, this places high demands on the image acquisition equipment. Therefore, it is also possible to obtain the number of people by analyzing the image. In the technical solution of this invention, the hair area is the most suitable recognition area because the hair area is a large area of ​​black. In image language, black is actually a summary of multiple parameters. For example, in RGB mode, black is determined by three values. Obviously, three values ​​are not convenient to operate. Therefore, one step in the above content is to generate feature values ​​based on the color values ​​of the pixels. Among them, the most common feature value generation method is grayscale conversion.

[0115] As described above, the first personnel determination module 111 can calculate the virtual visitors under the same road condition, the visitor calculation module 103 can calculate the visitors at each road condition end on different traffic routes to the site location, the passenger flow determination module 104 can calculate the number of visitors in the site area, and the outbound flow can be obtained by real-time statistics of the number of people at the site exit. Specifically, the calculation process of the site passenger flow N can be expressed by the following formula:

[0116] Where n represents the number of road condition ends on different access routes to the site location, N i ω represents the virtual visitor on the i-th traffic terminal. i P represents the probability value of the visit corresponding to the i-th traffic condition terminal. i Let m represent the actual visitors at the i-th traffic condition terminal, k represent the number of non-public transportation vehicles at the i-th traffic condition terminal, and m represent the number of visitors at the i-th traffic condition terminal. i n represents the number of non-public transport vehicles of the same type on the i-th road condition end. i ρ represents the carrying capacity of the same type of non-public transport vehicles on the i-th road condition end. i Q represents the occupancy rate of the same type of non-public transportation vehicles on the i-th road condition end, Q represents the number of visitors in the on-site area, and T represents the outbound traffic flow counted at the on-site exit.

[0117] Figure 8 A block diagram of the contour recognition unit in the passenger flow determination module is shown. The contour recognition unit 1041 specifically includes:

[0118] Color value confirmation subunit 10411 is used to traverse the pixels in the region image and obtain the color value of the pixel;

[0119] The marking subunit 10412 is used to confirm the tolerance and sequentially read the color values ​​of adjacent pixels, determine the size of the color value difference between adjacent pixels and the tolerance. If the color value difference between adjacent pixels is greater than the tolerance, the pixel is marked. If the color value difference between adjacent pixels is less than the tolerance, the next adjacent pixel is read.

[0120] Connection subunit 10413 is used to generate feature contours based on marked pixels.

[0121] The above describes the contour recognition process, which is somewhat similar to the magic wand tool in Photoshop, but will not be elaborated on in detail.

[0122] It should be noted that the real-time passenger flow monitoring system described above is suitable for use in scenic spots, train stations, large shopping malls, large conference venues, sports event venues, and any other large-scale event venues that require accurate monitoring of passenger flow.

[0123] Example 2

[0124] Figure 9 A flowchart of a method for real-time passenger flow monitoring is shown. In this embodiment of the invention, a method for real-time passenger flow monitoring is provided, the method comprising:

[0125] Step S200: Obtain the on-site location, determine the monitoring area based on the on-site location and the preset area radius, and obtain the travel plan within the monitoring area with the on-site location as the destination;

[0126] Step S400: Read the access plan sequentially, determine the nodes of the access plan, classify the nodes according to the distance of the nodes to the site location, and determine the probability of visit based on the classification results of the nodes;

[0127] Step S600: Receive visitor information obtained from the traffic condition terminal, read the visitor probability of the corresponding node according to the location of the traffic condition terminal, and calculate the visitor based on the visitor probability and the visitor information;

[0128] Step S800: Receive the target image obtained by the monitoring terminal, determine the number of visitors in the on-site area based on the target image, and determine the on-site passenger flow based on the number of visitors, the number of visitors, and the outbound flow.

[0129] The data mentioned above are all data within a unit of time, and the outflow traffic is obtained in real time from the on-site exit.

[0130] Figure 10The flowchart of a real-time passenger flow monitoring method is shown. The steps of receiving visitor information obtained from the traffic condition terminal, reading the visitor probability of the corresponding node based on the location of the traffic condition terminal, and calculating the number of visitors based on the visitor probability and the visitor information specifically include:

[0131] Step S601: Receive incoming visit information obtained from the traffic condition terminal in sequence, and obtain its location information;

[0132] Step S603: Determine the traffic plan where the road condition terminal is located based on the location information, and determine the node based on the distance between the location information and the on-site location, and read the corresponding visit probability;

[0133] Step S605: Obtain the virtual visitors from the visit information, generate the predicted visitors based on the virtual visitors and the visit probability, and generate the actual visitors based on the predicted visitors and the actual visitors;

[0134] The probability of a visit is related to the node level; the closer the road condition is to the location on-site, the greater the probability of a visit.

[0135] It should be noted that the real-time passenger flow monitoring method described above is applicable to use in scenic spots, train stations, large shopping malls, large conference venues, sports event venues, and any other large-scale event venues that require accurate passenger flow monitoring.

[0136] The functions of the above-mentioned real-time passenger flow monitoring method are all performed by computer equipment, which includes one or more processors and one or more memories. The one or more memories store at least one piece of program code, which is loaded and executed by the one or more processors to realize the functions of the real-time passenger flow monitoring method.

[0137] The processor fetches instructions from memory one by one, analyzes the instructions, and then performs the corresponding operations according to the instructions, generating a series of control commands to enable the various parts of the computer to act automatically, continuously, and in a coordinated manner, forming an organic whole. This enables the input of programs and data, as well as the calculation and output of results. The arithmetic or logical operations generated in this process are all performed by the arithmetic unit. The memory includes a read-only memory (ROM), which is used to store computer programs. The memory is protected by an external protection device.

[0138] For example, a computer program can be divided into one or more modules, one or more of which are stored in memory and executed by a processor to perform the present invention. The one or more modules can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a central processing platform.

[0139] Those skilled in the art will understand that the above description is merely an example and does not constitute a limitation on the central processing platform. It may include more or fewer components than described above, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0140] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the central processing platform, connecting various parts of the user terminal via various interfaces and lines.

[0141] The aforementioned memory can be used to store computer programs and / or modules. The aforementioned processor implements various functions of the aforementioned central processing platform by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as information collection template display function, product information publishing function, etc.); the data storage area may store data created based on the use of the berth status display system (such as product information collection templates corresponding to different product types, product information that different product providers need to publish, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0142] If the modules / units integrated into the central processing platform are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the modules / units in the systems of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the functions of the various system embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0143] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0144] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A real-time passenger flow monitoring system, characterized in that, The system specifically includes: The traffic information terminal is used to obtain vehicle information in real time and determine virtual visitors based on the vehicle information of non-public transportation vehicles. The vehicle information includes the number of vehicles and the vehicle capacity. The terminal also obtains the number of tickets for public transportation vehicles with the current location as the destination to determine the actual visitors. Based on the virtual visitors and the actual visitors, the terminal generates visit information and sends the visit information to the server. The monitoring terminal is used to acquire image information in the field area in real time, generate target images based on the image information, and send the target images to the server at regular intervals. The server is used to acquire the on-site location, determine the monitoring area based on the on-site location and a preset area radius, acquire the traffic plans within the monitoring area with the on-site location as the destination, read the traffic plans sequentially, identify the nodes in the traffic plans, classify the nodes according to the distance from the nodes to the on-site location, determine the probability of personnel visits based on the classification results, receive visitor information acquired by the traffic condition terminal, read the visitor probability of the corresponding node according to the location of the traffic condition terminal, calculate the visitors based on the visitor probability and the visitor information, receive the target image acquired by the monitoring terminal, determine the number of visitors in the on-site area based on the target image, and determine the on-site passenger flow based on the number of visitors, the visitors, and the outbound flow. All of the above data are data within a unit of time. The specific calculation process for the on-site passenger flow N can be expressed by the following formula: Where n represents the number of road condition ends on different access routes to the site location, N i ω represents the virtual visitor on the i-th traffic terminal. i P represents the probability value of the visit corresponding to the i-th traffic condition terminal. i Let m represent the actual visitors at the i-th traffic condition terminal, k represent the number of non-public transportation vehicle types at the i-th traffic condition terminal, and m represent the number of visitors at the i-th traffic condition terminal. i n represents the number of non-public transport vehicles of the same type on the i-th road condition end. i ρ represents the carrying capacity of the same type of non-public transport vehicles on the i-th road condition end. i Q represents the occupancy rate of the same type of non-public transportation vehicles on the i-th road condition end, Q represents the number of visitors in the on-site area, and T represents the outbound traffic flow counted at the on-site exit.

2. The real-time passenger flow monitoring system according to claim 1, characterized in that, The road condition terminal specifically includes: The first personnel identification module is used to acquire vehicle information of non-public transportation vehicles in real time and identify virtual visitors based on the vehicle information, wherein the vehicle information includes the number of vehicles and the vehicle carrying capacity. The second personnel identification module is used to obtain the number of tickets for public transportation with the on-site location as the destination, and to identify the actual visitors; The visitor information generation module is used to generate visitor information based on the virtual visitor and the actual visitor, and send the visitor information to the server.

3. The real-time passenger flow monitoring system according to claim 1, characterized in that, The server specifically includes: The scheme acquisition module is used to acquire the on-site location, determine the monitoring area based on the on-site location and a preset area radius, and acquire the travel schemes within the monitoring area with the on-site location as the destination. The probability determination module is used to sequentially read the access plan, determine the nodes of the access plan, classify the nodes according to the distance of the nodes to the site location, and determine the probability of visit based on the classification results of the nodes. The visitor calculation module is used to receive visitor information obtained from the traffic condition terminal, read the visitor probability of the corresponding node according to the location of the traffic condition terminal, and calculate the visitor based on the visitor probability and the visitor information. The visitor flow determination module is used to receive the target image obtained by the monitoring terminal, determine the number of visitors in the on-site area based on the target image, and determine the on-site visitor flow based on the number of visitors, the number of visitors, and the outflow of the area. The data mentioned above are all data within a unit of time, and the outflow traffic is obtained in real time from the on-site exit.

4. The real-time passenger flow monitoring system according to claim 1, characterized in that, The monitoring terminal specifically includes: The selection module is used to acquire image information within the field area in real time and select image information within a preset time period as a preset image. The determining module is used to determine a reference heat source in the preset image, wherein the reference heat source corresponds to a reference target; The capture module is used to capture the target heat source in the preset image, the target heat source corresponding to the moving target; The judgment module is used to determine the separation distance between the reference heat source and the target heat source in the region image. When the separation distance is greater than a threshold, the preset image is confirmed as the target image, and the target image is sent to the server at regular intervals.

5. The real-time passenger flow monitoring system according to claim 2, characterized in that, The first personnel determination module specifically includes: The classification unit is used to obtain the types of non-public transportation vehicles in real time and determine the vehicle carrying capacity based on the vehicle types. The total load capacity calculation unit is used to obtain the number of vehicles and calculate the total load capacity based on the number of vehicles and the corresponding vehicle load capacity. The first execution unit is used to determine virtual visitors based on the preset load factor and the total capacity.

6. The real-time passenger flow monitoring system according to claim 3, characterized in that, The visitor calculation module specifically includes: The location determination unit is used to sequentially receive the incoming visit information obtained from the traffic condition terminal and obtain its location information; The node determination unit is used to determine the traffic plan where the road condition terminal is located based on the location information, and to determine the node based on the distance between the location information and the on-site location, and to read the corresponding visit probability. The second execution unit is used to obtain virtual visitors from the visit information, generate predicted visitors based on the virtual visitors and the visit probability, and generate visitors based on the predicted visitors and the actual visitors. The probability of a visit is related to the node level; the closer the road condition is to the location on-site, the greater the probability of a visit.

7. The real-time passenger flow monitoring system according to claim 3, characterized in that, The passenger flow determination module specifically includes: A contour recognition unit is used to receive a target image acquired by a monitoring terminal, perform contour recognition on the target image, and obtain a feature contour. The feature extraction unit is used to generate a feature region based on the feature contour, sequentially traverse the pixels of the feature region, and generate feature values ​​based on the color values ​​of the pixels. A marking unit is used to determine a hair region based on the feature value, and to mark the feature region when the feature region is a hair region; The third execution unit is used to obtain the number of tags, which is used as the number of visitors.

8. The real-time passenger flow monitoring system according to claim 7, characterized in that, The contour recognition unit specifically includes: The color value confirmation subunit is used to traverse the pixels in the region image and obtain the color value of the pixel; The marking subunit is used to confirm the tolerance and read the color values ​​of adjacent pixels in sequence, and determine the size of the color value difference between adjacent pixels and the tolerance. If the color value difference between adjacent pixels is greater than the tolerance, the pixel is marked. If the color value difference between adjacent pixels is less than the tolerance, the next adjacent pixel is read. Connecting subunits are used to generate feature contours based on labeled pixels.

9. A method for real-time passenger flow monitoring, implemented based on the real-time passenger flow monitoring system according to any one of claims 1-8, characterized in that, The method is applied to the server side, and the method specifically includes: The system acquires the on-site location, determines the monitoring area based on the on-site location and a preset area radius, and acquires the travel routes within the monitoring area with the on-site location as the destination. The passage plan is read sequentially, the nodes of the passage plan are determined, the nodes are classified according to the distance of the nodes to the site location, and the probability of visit is determined according to the classification results of the nodes. Receive visitor information obtained from the traffic condition terminal, read the visitor probability of the corresponding node according to the location of the traffic condition terminal, and calculate the visitor based on the visitor probability and the visitor information; The system receives target images acquired by the monitoring terminal, determines the number of visitors in the on-site area based on the target images, and determines the on-site passenger flow based on the number of visitors, the number of visitors, and the outflow of traffic. The data mentioned above are all data within a unit of time, and the outflow traffic is obtained in real time from the on-site exit.

10. The method for real-time monitoring of passenger flow according to claim 9, characterized in that, The steps of receiving visitor information from the traffic condition terminal, reading the visitor probability of the corresponding node based on the location of the traffic condition terminal, and calculating the visitor based on the visitor probability and the visitor information specifically include: It sequentially receives incoming visitor information obtained from the traffic condition terminal and obtains their location information; The traffic plan for the road condition terminal is determined based on the location information, and the node is determined based on the distance between the location information and the actual location, and the corresponding probability of arrival is read. Obtain virtual visitors from the visitor information, generate predicted visitors based on the virtual visitors and the visit probability, and generate actual visitors based on the predicted visitors and the actual visitors; The probability of a visit is related to the node level; the closer the road condition is to the location on-site, the greater the probability of a visit.

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