CN114548604A - Passenger flow dynamic guiding method, system, electronic equipment and storage medium - Google Patents

Passenger flow dynamic guiding method, system, electronic equipment and storage medium Download PDF

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CN114548604A
CN114548604A CN202210436229.4A CN202210436229A CN114548604A CN 114548604 A CN114548604 A CN 114548604A CN 202210436229 A CN202210436229 A CN 202210436229A CN 114548604 A CN114548604 A CN 114548604A
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passenger
passenger flow
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陈振武
梁晨
黄志军
刘祥
张稷
崔成博
马剑
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Shenzhen Urban Transport Planning Center Co Ltd
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Abstract

The invention provides a method and a system for dynamically guiding passenger flow, electronic equipment and a storage medium, and belongs to the technical field of passenger flow guiding. Firstly, counting the number of passengers through a number acquisition module; secondly, the position information of the passenger is obtained through a positioning module; thirdly, the cloud processor receives the number of passengers and the position information of the passengers, predicts the number of the passengers at a transfer point, a stair entrance and an elevator entrance through deep learning, and obtains a passenger flow distribution result according to the optimal distribution model of the system; thirdly, obtaining a recommended route according to the passenger flow distribution result; and finally, displaying the recommended route by a display screen or voice playing or guide arrow or variable guide arrow mode. The method and the device can well solve the technical problems that the field of passenger flow evacuation is large in limitation, the target setting is single, and the method and the device cannot be suitable for complex scenes in the prior art. The method and the device are simultaneously applied to normal and evacuation scenes.

Description

Passenger flow dynamic guiding method, system, electronic equipment and storage medium
Technical Field
The present invention relates to a method for dynamically guiding passenger flow, and in particular, to a method, a system, an electronic device, and a storage medium for dynamically guiding passenger flow, which belong to the technical field of passenger flow guidance.
Background
The comprehensive transportation hub is an important component of a comprehensive transportation system, integrates various transportation mode information, equipment and organization management, attracts a large amount of passenger and goods flow, and drives and supports the development of regional economy. The construction of the comprehensive transportation hub continuously strengthens the integrated connection of hubs mainly based on passenger transport, closely connects facilities such as urban rail transit, ground public transportation, suburban railways, private transportation and the like with main railways, intercity railways, main highways, airports and the like, strives to realize zero-distance transfer, optimizes the transfer process and shortens the transfer distance.
Along with the rapid development of the comprehensive transportation hub, the travel and transfer of passengers are more convenient and rapid, and due to the fact that the comprehensive transportation hub is connected with various transportation modes, in addition, the passenger flow is large in the peak period, the passengers are easy to block, and the safety problem is caused. In addition, the passenger routing can also affect the transfer distance, particularly when an emergency occurs, the routing behavior directly affects the evacuation capacity, and the existing single guide mark cannot play an effective guide role, so that the passenger flow congestion degree in the peak period can be effectively reduced by designing a dynamic guide mark, and the evacuation capacity of a junction under the emergency is improved.
The patent with publication number CN113807026A discloses a subway station passenger flow line optimization and dynamic guidance sign system and a design method thereof, relating to the field of subway passenger flow evacuation. The system comprises a passenger identification and positioning unit, a random user balanced passenger flow distribution unit based on passenger crowdedness, and a real-time passenger position display and path recommendation unit in a station. The design method comprises the following steps: extracting pictures of each place in the subway station by monitoring equipment in the station; the positions of passengers in the picture are extracted by using image processing to obtain the passenger distribution condition in the whole subway station; balancing a passenger flow distribution model by a random user based on the passenger crowdedness degree to obtain an optimized passenger flow distribution result; and displaying the real-time passenger flow distribution condition and the optimized recommended path on a dynamic guiding sign in the station. The invention can be used for recommending the routes of activities in the subway station such as passenger entering, exiting, transferring, gate selecting and the like in the normal operation mode, and can also realize the function of recommending evacuation routes in the escape process of passengers in emergencies.
When passenger identification and positioning are realized, a method of combining a Gaussian mixture model and a convolutional neural network is used. Firstly, extracting passenger graphs in a video by using a Gaussian mixture model, and reading position information of all complete passenger graphs in the video; secondly, predicting and identifying the position of the next frame of the blocked passenger graph in the video by using a convolutional neural network algorithm to obtain continuous passenger position information; and finally, converting the positions of the passengers in the video into the positions in the real scene, thereby obtaining the real-time position information and the track of each passenger in the subway station.
This patent suffers from the following disadvantages:
firstly, the method is only suitable for specific passenger flow guiding scenes in the subway station, cannot be suitable for other scenes, and has certain limitation.
Secondly, when passenger identification and positioning are realized, although the information positions of all passenger graphs in the video can be read by using a Gaussian mixture model, certain errors exist when the position of the next frame is predicted and identified for the passenger graph which is shielded in the video by using a convolutional neural network algorithm. In addition, only the position information of the passengers is obtained, and the number of passengers is not counted.
And finally, a random user balanced passenger flow distribution model based on the passenger crowding degree is adopted, and the state of the system is optimized only from the perspective of individual users and not from the perspective of the whole system.
Disclosure of Invention
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. It should be understood that this summary is not an exhaustive overview of the invention. It is not intended to determine the key or critical elements of the present invention, nor is it intended to limit the scope of the present invention. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later.
In view of this, in order to solve the technical problems in the prior art that the field of passenger flow evacuation is limited greatly, the target setting is single, and the method cannot be applied to complex scenes, the invention provides a method and a system for dynamically guiding passenger flow, an electronic device and a storage medium.
The first scheme is as follows: a passenger flow dynamic guiding method comprises the following steps:
s1, counting the number of passengers;
s2, obtaining position information of passengers;
s3, the cloud processor receives the passenger number and the passenger position information, and predicts the passenger number of the transfer point, the stair entrance and the elevator entrance through deep learning;
s4, obtaining a passenger flow distribution result according to the system optimal distribution model;
s5, obtaining a recommended route according to a passenger flow distribution result;
and S6, displaying the recommended route.
Preferably, the specific method for constructing the optimal distribution model of the system is as follows:
building a walking distance for a passenger
Figure 698466DEST_PATH_IMAGE001
And time of walking
Figure 757427DEST_PATH_IMAGE002
Linear optimal objective function Z:
Figure 108774DEST_PATH_IMAGE003
and (3) constructing a constraint condition:
Figure 54733DEST_PATH_IMAGE004
Figure 997281DEST_PATH_IMAGE005
wherein:
Figure 904057DEST_PATH_IMAGE006
Figure 668882DEST_PATH_IMAGE007
wherein the content of the first and second substances,ifor the number of the place of departure,i=1,2,3…；jin order to be the destination number,j=1,2,3…；kis as followskNumbering the shortest of the strips;lselecting a number for the path;
Figure 141452DEST_PATH_IMAGE008
is as followsiWhether the individual passenger selectskShortest path to destinationjSelecting 1, otherwise, 0;
Figure 458164DEST_PATH_IMAGE009
is a weight and takes a value of
Figure 383394DEST_PATH_IMAGE010
The decision maker is regulated according to the attribute of the emergency and the threatened severity of the hub;
Figure 201178DEST_PATH_IMAGE011
selecting the sum of the total distances of the shortest paths for all passengers;
Figure 997095DEST_PATH_IMAGE012
the time of arrival at the destination is distributed to all passengers according to the optimal distribution model of the system;
Figure 281446DEST_PATH_IMAGE013
is selected for if path
Figure 548435DEST_PATH_IMAGE014
Including the pathlThe value is 1, otherwise, the value is 0;
Figure 310854DEST_PATH_IMAGE015
is a path
Figure 961278DEST_PATH_IMAGE014
Total length of (d);
Figure 416530DEST_PATH_IMAGE016
indicating the first in the hubiAn individual passenger;
Figure 909829DEST_PATH_IMAGE017
routing passengerslThe transit time of (a);
Figure 475939DEST_PATH_IMAGE018
routing passengerslThe minimum possible time of;
Figure 980870DEST_PATH_IMAGE019
is a destinationjThe capacity of (c).
Preferably, the specific method for predicting the number of passengers at the transfer point, the landing and the elevator landing by deep learning comprises the following steps: the method comprises the following steps:
a. acquiring time sequence data of the number of passengers and the position information of the passengers at the existing transfer points, the stairway openings and the elevator openings to form a database;
b. training in a deep learning system using a database;
c. predicting the number of passengers and position information at the point after 5 minutes by using a trained deep learning system according to the current transfer point, the current stairway opening, the current number of passengers at the elevator entrance and the current position information of the passengers;
specifically, the deep learning system employs a long short term memory network (LSTM) in a Recurrent Neural Network (RNN).
The existing transfer points, the stairway openings, the passenger numbers at the elevator openings and the time sequence data of the passenger position information are used, the relevant information of the current time node is used as input data, and the LSTM is used for splicing training with the passenger position information of the next time node.
There are three main stages inside the LSTM:
1. forget the stage. This stage is mainly the selective forgetting of the input coming from the previous node.
2. The memory stage is selected. This stage selectively "remembers" the input nodes.
3. And (5) an output stage. This stage is to output the present stage, i.e., the number of passengers at the transfer point, the entrance of the stairway, the entrance of the elevator, and the position information of the passengers corresponding to the point after 5 minutes, based on the result of the previous stage.
Preferably, the number of passengers is counted through a number acquisition module; the specific method for acquiring the position information of the passenger is to acquire the position information of the passenger through a positioning module.
Preferably, the specific method for presenting the recommended route is to present the recommended route by means of a display screen or voice playing or a guide arrow or a variable guide arrow.
Preferably, the optimal allocation model of the system comprises two factors of the walking distance and the walking time of the passenger.
Preferably, the number of people collection module is a laser counter.
Preferably, the passenger flow dynamic guiding method is applied to vehicle path planning or parking lot path planning,
or the evacuation and transfer of subway people stream,
or the people stream of the passenger station is evacuated and transferred,
or in an emergency situation of an office building scenario, the destination is set as a hub exit or a safe location, and the passenger flow distribution result is obtained through the system optimal distribution model described in S4, so as to obtain a recommended rapid evacuation route.
The second scheme is that the passenger flow dynamic guiding system comprises a passenger information acquisition device, an electronic guiding device, a communication unit and a cloud processor;
the passenger information acquisition device comprises a laser counter and a GPS positioning unit, wherein the laser counter is used for counting the number of passengers, and the GPS positioning unit is used for positioning the information of the positions of the passengers;
the electronic guidance display is used for displaying a currently recommended transfer route;
the communication unit is used for wireless communication processing of the information acquisition device, the electronic guide display and the cloud processor;
the cloud processor is used for receiving the number of passengers acquired by the laser counter, obtaining a transfer route recommended by the system and transmitting the recommended route to the electronic guide display.
The third scheme is as follows: an electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of a method for dynamic guidance of passenger flow according to aspect one when executing the computer program.
And the scheme is as follows: a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method for dynamic guidance of passenger flow according to aspect one.
The invention has the following beneficial effects:
(1) the invention can be used for pedestrian path selection, such as pedestrian evacuation in public places in emergency; the method can also be used for path planning of vehicles, such as path planning of parking lots.
(2) The invention can be applied to normal state and evacuation scene at the same time.
(3) Compared with single target setting, the multi-target model has higher practicability, and can avoid other problems such as congestion and the like caused by the setting of a single target in complex road network scene application.
In conclusion, the method and the device can well solve the technical problems that the field of passenger flow evacuation is large in limitation, the target setting is single, and the method and the device cannot be suitable for complex scenes in the prior art.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic process flow diagram;
FIG. 2 is a schematic diagram of a system architecture;
fig. 3 is a schematic diagram of path selection.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Embodiment 1, this embodiment is described with reference to fig. 1 to 3, and a method for dynamically guiding a passenger flow includes the steps of:
s1, counting the number of passengers through a number collecting module;
specifically, the number acquisition module is a laser counter.
S2, acquiring the position information of the passenger through a positioning module;
s3, the cloud processor receives the number of passengers and the position information of the passengers, and predicts the number of the passengers at a transfer point, a stair entrance and an elevator entrance through deep learning; the method comprises the following steps:
a. acquiring time sequence data of the number of passengers and the position information of the passengers at the existing transfer points, the stairway openings and the elevator openings to form a database;
b. training in a deep learning system using a database;
c. predicting the number of passengers and position information at the point after 5 minutes by using a trained deep learning system according to the number of passengers and the position information of the passengers at the current transfer point, the stair entrance and the elevator entrance;
in this embodiment, another method for obtaining the people density at the transfer point, the landing and the elevator landing through deep learning is provided, and given an image, the CNN is used to estimate the number of people, and there are two general schemes: firstly, inputting an image and outputting the estimated number of people in the area; and secondly, outputting a crowd density map (more people per square meter), and then calculating the total number of people through integration.
And S4, obtaining a passenger flow distribution result according to the system optimal distribution model.
Specifically, the optimal distribution model of the system comprises two factors of the walking distance and the walking time of the passenger.
In real life, when a passenger transfers, the transfer distance and the transfer time are generally considered, and therefore, the traveling distance of the passenger is set
Figure 951231DEST_PATH_IMAGE001
And time of walking
Figure 338350DEST_PATH_IMAGE002
And (4) minimizing. Building a walking distance for a passenger
Figure 911414DEST_PATH_IMAGE001
And time of walking
Figure 67589DEST_PATH_IMAGE002
The model is solved and the optimal path is found. In emergency, the decision maker determines the weight according to the property of the emergency and the threatened severity of the junction
Figure 458119DEST_PATH_IMAGE020
And (4) constructing a rapid evacuation model for guiding passengers in the hub to safely and rapidly evacuate to a safe area.
The specific method for constructing the optimal distribution model of the system comprises the following steps:
building a walking distance for a passenger
Figure 66955DEST_PATH_IMAGE001
And time of walking
Figure 709289DEST_PATH_IMAGE002
Linear optimal objective function Z:
Figure 719970DEST_PATH_IMAGE021
and (3) constructing a constraint condition:
Figure 530669DEST_PATH_IMAGE022
Figure 626801DEST_PATH_IMAGE023
wherein:
Figure 541667DEST_PATH_IMAGE024
Figure 672434DEST_PATH_IMAGE025
wherein the content of the first and second substances,ifor the number of the place of departure,i=1,2,3…；jin order to be the destination number,j=1,2,3…；kis as followskNumbering the shortest of the strips;lselecting a number for the path;
Figure 404767DEST_PATH_IMAGE026
is as followsiWhether the individual passenger selectskShortest path to destinationjSelecting 1, otherwise, 0;
Figure 988195DEST_PATH_IMAGE009
is a weight and takes a value of
Figure 706752DEST_PATH_IMAGE010
The decision maker is regulated according to the attribute of the emergency and the threatened severity of the hub;
Figure 692026DEST_PATH_IMAGE027
selecting the sum of the total distances of the shortest paths for all passengers;
Figure 80413DEST_PATH_IMAGE028
the time of arrival at the destination is distributed to all passengers according to the optimal distribution model of the system;
Figure 744613DEST_PATH_IMAGE029
is selected for if path
Figure 532440DEST_PATH_IMAGE030
Including the pathlThe value is 1, otherwise, the value is 0;
Figure 106641DEST_PATH_IMAGE031
is a path
Figure 321721DEST_PATH_IMAGE032
Total length of (d);
Figure 614163DEST_PATH_IMAGE016
indicating the first in the hubiAn individual passenger;
Figure 471260DEST_PATH_IMAGE017
routing passengerslThe transit time of (a);
Figure 8290DEST_PATH_IMAGE033
routing passengerslThe minimum possible time of (c);
Figure 659851DEST_PATH_IMAGE019
is a destinationjThe capacity of (c).
S5, obtaining a recommended route according to a passenger flow distribution result;
and S6, displaying the recommended route, wherein the specific method is to display the recommended route in a display screen or voice playing or guide arrow or variable guide arrow mode.
Specifically, the passenger flow dynamic guidance method can be applied to vehicle path planning;
specifically, the passenger flow dynamic guiding method can be applied to parking lot path planning;
specifically, the passenger flow dynamic guiding method can be applied to people flow evacuation and transfer of subways;
specifically, the passenger flow dynamic guiding method can be applied to people flow evacuation and transfer in a passenger station;
specifically, the passenger flow dynamic guiding method can be applied to emergency situations in office building scenes;
and setting the destination as a hub exit or a safe position, and obtaining a passenger flow distribution result through the system optimal distribution model in S4 so as to obtain a quick evacuation recommended route.
Embodiment 2, this embodiment is described with reference to fig. 2, and a passenger flow dynamic guidance system includes a passenger information acquisition device, an electronic guidance device, a communication unit, and a cloud processor;
the passenger information acquisition device comprises a laser counter and a GPS positioning unit, wherein the laser counter is used for counting the number of passengers, and the GPS positioning unit is used for positioning the information of the positions of the passengers;
the electronic guide display is used for displaying a currently recommended transfer route;
the communication unit is used for wireless communication processing of the information acquisition device, the electronic guide display and the cloud processor;
the cloud processor is used for receiving the number of passengers acquired by the laser counter, obtaining a transfer route recommended by the system and transmitting the recommended route to the electronic guide display.
The invention can be applied to the dynamic guidance of passenger flow in the comprehensive transportation hub in the evacuation scene. Under the situation, the problem of rapid evacuation of passengers is mainly solved, in the model, the departure place is the current passenger position, and the destination is the exit or the safety position of the junction, and in an emergency situation, the passengers can select an escape path through a recommended route provided by an electronic guide display, so that the time waste and blind following caused by unfamiliarity of the passengers with the site environment are reduced, and the evacuation efficiency of the junction in the emergency situation is improved.
The invention simultaneously considers two factors of the walking distance and the walking time of passengers, can avoid the problem of congestion caused by the setting of a single target in the application of a complex road network scene, is more practical and has higher practicability than a simple model.
The invention can be used for pedestrian path selection, such as pedestrian evacuation in public places in emergency; the method can also be used for path planning of vehicles, such as path planning of parking lots.
In embodiment 3, the computer device of the present invention may be a device including a processor, a memory, and the like, for example, a single chip microcomputer including a central processing unit, and the like. And the processor is used for implementing the steps of the recommendation method capable of modifying the relationship-driven recommendation data based on the CREO software when executing the computer program stored in the memory.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Embodiment 4, computer-readable storage Medium embodiment
The computer readable storage medium of the present invention may be any form of storage medium that can be read by a processor of a computer device, including but not limited to non-volatile memory, ferroelectric memory, etc., and the computer readable storage medium has stored thereon a computer program that, when the computer program stored in the memory is read and executed by the processor of the computer device, can implement the above-mentioned steps of the CREO-based software that can modify the modeling method of the relationship-driven modeling data.
The computer program comprises computer program code which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense, and the scope of the present invention is defined by the appended claims.

Claims (10)

1. A passenger flow dynamic guiding method is characterized by comprising the following steps:
s1, counting the number of passengers;
s2, obtaining position information of passengers;
s3, the cloud processor receives the number of passengers and the position information of the passengers, and predicts the number of the passengers at a transfer point, a stair entrance and an elevator entrance through deep learning;
s4, obtaining a passenger flow distribution result according to the system optimal distribution model;
s5, obtaining a recommended route according to a passenger flow distribution result;
and S6, displaying the recommended route.
2. The method for dynamically guiding passenger flow according to claim 1, wherein the specific method for constructing the system optimal distribution model is as follows:
building a walking distance for a passenger
Figure 708965DEST_PATH_IMAGE001
And time of walking
Figure 597024DEST_PATH_IMAGE002
Linear optimal objective function Z:
Figure 992234DEST_PATH_IMAGE003
and (3) constructing a constraint condition:
Figure 9868DEST_PATH_IMAGE004
Figure 691385DEST_PATH_IMAGE005
wherein:
Figure 958418DEST_PATH_IMAGE006
Figure 626160DEST_PATH_IMAGE007
wherein the content of the first and second substances,ifor the number of the place of departure,i=1,2,3…；jin order to be the destination number,j=1,2,3…；kis as followskNumbering the shortest of the strips;lselecting a number for the path;
Figure 29460DEST_PATH_IMAGE008
is as followsiWhether the individual passenger selectskShortest path to destinationjSelecting 1, otherwise, 0;
Figure 632610DEST_PATH_IMAGE009
is a weight and takes a value of
Figure 855781DEST_PATH_IMAGE010
The decision maker is regulated according to the attribute of the emergency and the threatened severity of the hub;
Figure 592793DEST_PATH_IMAGE011
selecting the sum of the total distances of the shortest paths for all passengers;
Figure 709654DEST_PATH_IMAGE012
the time of arrival at the destination is distributed to all passengers according to the optimal distribution model of the system；
Figure 139498DEST_PATH_IMAGE013
Is selected for if path
Figure 584386DEST_PATH_IMAGE014
Including pathlThe value is 1, otherwise, the value is 0;
Figure 967832DEST_PATH_IMAGE015
is a path
Figure 611303DEST_PATH_IMAGE014
Total length of (d);
Figure 274365DEST_PATH_IMAGE016
indicating the first in the hubiAn individual passenger;
Figure 737707DEST_PATH_IMAGE017
routing passengerslThe transit time of (a);
Figure 550943DEST_PATH_IMAGE018
routing passengerslThe minimum possible time of;
Figure 48920DEST_PATH_IMAGE019
is a destinationjThe capacity of (c).
3. The method for dynamically guiding passenger flow according to claim 2, wherein the specific method for predicting the number of passengers at a transfer point, a landing and an elevator landing by deep learning is as follows: the method comprises the following steps:
a. acquiring time sequence data of the number of passengers and the position information of the passengers at the existing transfer points, the stairway openings and the elevator openings to form a database;
b. training in a deep learning system using a database;
c. and predicting the number and position information of passengers at the point after 5 minutes by using the trained deep learning system according to the number and position information of the passengers at the current transfer point, the stair entrance and the elevator entrance.
4. The dynamic passenger flow guiding method according to claim 3, wherein the specific method for counting the number of passengers is to count the number of passengers by a number collecting module; the specific method for acquiring the position information of the passenger is to acquire the position information of the passenger through a positioning module.
5. The method as claimed in claim 4, wherein the recommended route is presented by displaying the recommended route through a display screen or voice playing or a guidance arrow or a variable guidance arrow.
6. The method as claimed in claim 5, wherein the people number collecting module is a laser counter.
7. The dynamic passenger flow guidance method according to claim 6, wherein the dynamic passenger flow guidance method is applied to vehicle path planning or parking lot path planning,
or the evacuation and transfer of subway people stream,
or the people stream of the passenger station is evacuated and transferred,
or emergency situations in office building scenarios;
and setting the destination as a hub exit or a safe position, and obtaining a passenger flow distribution result through the system optimal distribution model in S4 so as to obtain a quick evacuation recommended route.
8. A passenger flow dynamic guiding system in a comprehensive transportation junction is characterized by comprising a passenger information acquisition device, an electronic guiding device, a communication unit and a cloud processor;
the passenger information acquisition device comprises a laser counter and a GPS positioning unit, wherein the laser counter is used for counting the number of passengers, and the GPS positioning unit is used for positioning the information of the positions of the passengers;
the electronic guidance display is used for displaying a currently recommended transfer route;
the communication unit is used for wireless communication processing of the information acquisition device, the electronic guide display and the cloud processor;
the cloud processor is used for receiving the number of passengers acquired by the laser counter, obtaining a transfer route recommended by the system and transmitting the recommended route to the electronic guide display.
9. An electronic device, comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of a method for dynamic guidance of passenger flow according to any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for dynamic guidance of passenger flow according to any one of claims 1 to 6.
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