Passenger flow monitoring method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a passenger flow monitoring method and device, electronic equipment and a storage medium, and belongs to the technical field of data analysis. The method comprises the following steps: acquiring a current passenger flow volume, and gridding an active area of a city according to the current passenger flow volume to obtain grid data; carrying out attribute identification on the grid data to obtain a target area grid; obtaining a grid passenger flow volume according to the grid data and the current passenger flow volume; and calculating the current key area passenger flow through the target area grid and the grid passenger flow. According to the embodiment of the invention, real-time passenger flow monitoring of tourists and non-tourists can be carried out, the spatial distribution characteristics of the passenger flow are considered, scenic spot managers are helped to carry out passenger flow management and control in advance, measures such as flow limiting, peak shifting and travel reservation are effectively carried out, and the tourists are helped to reasonably arrange travel plans.

Description

Technical Field

[0001] The present application relates to the field of data analysis technology, and in particular to a passenger flow monitoring method and device, electronic equipment and storage medium. Background Art

[0002] With the rapid development of social economy, tourism, as an important part of people's material and spiritual life, has been booming. But at the same time, the potential safety hazards of scenic spot passenger flow cannot be ignored, especially during holidays, when the passenger flow has increased dramatically in a short period of time, and passenger flow safety accidents caused by over-accommodation of tourists in scenic spots are endless.

[0003] At present, most of the relevant research on scenic spot passenger flow prediction is still based on single-source passenger flow monitoring data. The monitoring methods are mainly concentrated on the passenger flow research of a single site or a single point. Most of them are based on time series research, ignoring the spatial distribution characteristics of passenger flow.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0005] The main purpose of the embodiments of the present application is to propose a passenger flow monitoring method and device, an electronic device and a storage medium, aiming to realize real-time passenger flow monitoring taking into account the spatial distribution characteristics of passenger flow.

[0006] To achieve the above object, an embodiment of the present application provides a passenger flow monitoring method, which includes the following steps:

[0007] Obtaining the current passenger flow, and gridding the movable area of ​​the city according to the current passenger flow to obtain grid data, wherein the current passenger flow includes the current number of tourists received and the current number of residents traveling;

[0008] Performing attribute identification on the grid data to obtain a target area grid, wherein the target area grid is associated with a key area;

[0009] Obtaining a grid passenger flow according to the grid data and the current passenger flow;

[0010] The current passenger flow in the key area is calculated by using the target area grid and the grid passenger flow, and the current passenger flow in the key area includes the number of tourists in the key area and the number of residents traveling in the key area.

[0011] In some embodiments, obtaining the current passenger flow includes:

[0012] Get the current number of tourists received;

[0013] The number of non-tourist residents is obtained, and the current number of traveling residents is determined according to the number of non-tourist residents.

[0014] In some embodiments, obtaining the current number of tourists received includes:

[0015] Get the tourist arrival rate and total number of tourists;

[0016] The current number of tourists received is obtained by performing a full calculation based on the total number of tourists and the tourist arrival rate.

[0017] In some embodiments, the method further comprises:

[0018] Obtaining historical data of the key area, and calculating monitoring accuracy based on the current passenger flow of the key area and the historical data;

[0019] Get the preset accuracy threshold;

[0020] If the monitoring accuracy is less than the preset accuracy threshold, the process returns to the step of performing attribute identification on the grid data to obtain the target area grid, until the monitoring accuracy is greater than or equal to the preset accuracy threshold.

[0021] In some embodiments, the method further comprises:

[0022] Acquire a corresponding grid area according to the grid data;

[0023] Calculate the grid passenger flow density according to the grid passenger flow and the grid area;

[0024] Obtain the stampede risk classification warning form;

[0025] If the grid passenger flow density exceeds the crowd density threshold of the stampede risk classification warning table, corresponding stampede warning information is generated according to the grid data, the grid passenger flow density and the crowd density threshold;

[0026] In response to the first instruction, the stampede warning information is displayed on the warning page.

[0027] In some embodiments, the method further comprises:

[0028] Acquire the corresponding maximum passenger flow of the grid according to the grid data;

[0029] Calculate the grid passenger flow saturation according to the grid passenger flow and the grid maximum passenger flow;

[0030] Obtain the congestion risk classification warning form;

[0031] If the grid passenger flow saturation exceeds the load density occupancy rate of the congestion risk classification warning table, corresponding congestion warning information is generated according to the grid data, the grid passenger flow density and the load density occupancy rate;

[0032] In response to the second instruction, the congestion warning information is displayed on the warning page.

[0033] In some embodiments, the grid passenger flow includes the number of grid residents traveling and the number of grid tourists received, and obtaining the grid passenger flow according to the grid data and the current passenger flow includes:

[0034] Obtain the number of residents traveling by the operator;

[0035] Obtaining the number of residents traveling by the grid operator according to the grid data;

[0036] The grid travel population is obtained according to the operator travel population, the grid operator travel population and the current travel population of the current passenger flow. The expression is as follows:

[0037]

[0038] To achieve the above object, another aspect of the embodiment of the present application provides a passenger flow monitoring device, the device comprising:

[0039] A gridding module is used to obtain the current passenger flow, grid the movable area of ​​the city according to the current passenger flow, and obtain grid data. The current passenger flow includes the current number of tourists received and the current number of residents traveling;

[0040] An identification module, used for performing attribute identification on the grid data to obtain a target area grid, wherein the target area grid is associated with a key area;

[0041] A grid passenger flow calculation module, used to obtain the grid passenger flow according to the grid data and the current passenger flow;

[0042] The key area passenger flow calculation module is used to calculate the current key area passenger flow through the target area grid and the grid passenger flow, wherein the current key area passenger flow includes the current key area tourist volume and the current key area traveling resident volume.

[0043] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method when executing the computer program.

[0044] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0045] The embodiments of the present application include at least the following beneficial effects: the present application provides a passenger flow monitoring method and device, an electronic device and a storage medium. The scheme can monitor the real-time passenger flow of tourists and non-tourists by acquiring the current passenger flow, which includes the current number of tourists received and the current number of residents traveling. The city's movable areas are gridded according to the current passenger flow to obtain grid data; the grid data is attributed to obtain the target area grid; the grid passenger flow is obtained according to the grid data and the current passenger flow, taking into account the spatial distribution characteristics of the passenger flow; the current passenger flow in key areas is calculated through the target area grid and the grid passenger flow, which helps scenic area managers to carry out passenger flow control in advance, effectively implement flow control, peak shifting, reservation travel and other measures, and at the same time helps tourists to reasonably arrange travel plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of a passenger flow monitoring method provided in an embodiment of the present application;

[0047] Figure 2 It is a flow chart of the steps of calculating monitoring accuracy of the passenger flow monitoring method provided in the embodiment of the present application;

[0048] Figure 3 is a flow chart of the trampling warning step of the passenger flow monitoring method provided in an embodiment of the present application;

[0049] Figure 4 yes Figure 1 Flow chart of step S103 in FIG.

[0050] Figure 5 is an optional flow chart of the passenger flow monitoring method provided in an embodiment of the present application;

[0051] Figure 6 is a schematic diagram of real-time passenger flow monitoring provided by an embodiment of the present application;

[0052] Figure 7 It is a schematic diagram of a stampede risk classification warning table provided in an embodiment of the present application;

[0053] Figure 8 It is a schematic diagram of a congestion risk classification warning table provided in an embodiment of the present application;

[0054] Fig. 9 is a structural schematic diagram of a passenger flow monitoring device provided in an embodiment of the present application;

[0055] Fig.10 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0057] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0058] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0060] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0061] 1) Geographic Information System (GIS) is a tool for capturing, storing, analyzing and managing geographic and spatial data. GIS combines elements of geography, statistics and information technology to enable users to view, understand and interpret spatial data to solve complex planning and management problems.

[0062] With the rapid development of social economy, tourism, as an important part of people's material and spiritual life, has been booming. But at the same time, the potential safety hazards of scenic spot passenger flow cannot be ignored, especially during holidays, when the passenger flow has increased dramatically in a short period of time, and passenger flow safety accidents caused by over-accommodation of tourists in scenic spots are endless.

[0063] At present, most of the relevant research on scenic spot passenger flow prediction is still based on single-source passenger flow monitoring data. The monitoring methods are mainly concentrated on the passenger flow research of a single site or a single point. Most of them are based on time series research, ignoring the spatial distribution characteristics of passenger flow.

[0064] In summary, the technical problems existing in the relevant technologies need to be improved.

[0065] In view of this, a passenger flow monitoring method, device, equipment and medium are provided in the embodiments of the present application. The scheme can monitor the real-time passenger flow of tourists and non-tourists by obtaining the current passenger flow, which includes the current number of tourists received and the current number of residents traveling. The city's movable areas are gridded according to the current passenger flow to obtain grid data; the grid data is attributed to obtain the target area grid; the grid passenger flow is obtained according to the grid data and the current passenger flow, taking into account the spatial distribution characteristics of the passenger flow; the current passenger flow in key areas is calculated through the target area grid and the grid passenger flow, which helps scenic area managers to carry out passenger flow control in advance, effectively implement flow control, peak shifting, reservation travel and other measures, and at the same time helps tourists to reasonably arrange travel plans.

[0066] The passenger flow monitoring method provided in the embodiment of the present application relates to the field of data analysis technology. The passenger flow monitoring method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server, or can be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The server can also be a node server in a blockchain network; the software can be an application that implements the passenger flow monitoring method, etc., but is not limited to the above forms.

[0067] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0068] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0069] Figure 1 is an optional flow chart of the passenger flow monitoring method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S104.

[0070] Step S101, obtaining the current passenger flow, and gridding the city's movable area according to the current passenger flow to obtain grid data.

[0071] Specifically, the current passenger flow includes the current number of tourists received and the current number of traveling residents.

[0072] Among them, the current number of tourists received and the current number of traveling residents are obtained.

[0073] Optionally, the current number of traveling residents can be determined by the number of non-traveling residents, wherein the number of non-traveling residents is residents who have not left their usual environment.

[0074] In some embodiments, the current number of traveling residents can be obtained through operator data or through the city transportation system.

[0075] Optionally, the current number of tourists received can be determined by the tourist arrival rate and the total number of tourists, or by positioning data, or by travel platform data.

[0076] In some embodiments, base station data, weather data, time data, temperature data, emergency data, etc. are obtained as a feature set as a factor affecting the current number of tourists received by the city. The current number of tourists received is affected by base station data, weather data, time data, temperature data, and emergency data.

[0077] Furthermore, a full calculation is made based on the total number of tourists and the tourist arrival rate to obtain the current number of tourists received.

[0078] In some embodiments, the city is divided into different grid units using GIS technology for urban planning to obtain grid data. It is understandable that each grid can be an area of ​​a fixed size, such as 100 meters × 100 meters, 500 meters × 500 meters, etc.

[0079] In this embodiment, the current passenger flow is obtained, and the movable area of ​​the city is gridded according to the current passenger flow to obtain grid data, and real-time passenger flow monitoring of foreign tourists and non-tourists is performed, which provides basic data for gridding. Gridding helps to process and analyze passenger flow.

[0080] Step S102, attribute identification is performed on the grid data to obtain the target area grid.

[0081] Specifically, the target area grid is associated with the key area.

[0082] In some embodiments, based on map data and field surveys, each grid is identified with attributes, and the grid attributes are associated and matched with specific parks, scenic spots, business districts, blocks, or transportation hubs.

[0083] This application does not make specific restrictions on key areas, and can be flexibly selected in combination with actual monitoring needs. For example, key areas can be manually determined, or areas where historical visitor data exceeds a visitor threshold can be set as key areas.

[0084] Optionally, key areas in the city are automatically identified based on geographic information and historical passenger flow data, and the target area grid is then determined.

[0085] In this embodiment, attribute identification is performed on the grid data to obtain the target area grid, and the grid is associated with the key area through the attribute identification, which is helpful for passenger flow monitoring in the key area.

[0086] Step S103, obtaining the grid passenger flow according to the grid data and the current passenger flow.

[0087] Specifically, grid passenger flow includes the number of grid traveling residents and the number of grid tourists received.

[0088] In some embodiments, the number of grid traveling residents and the number of grid tourists received are monitored according to the grid division rules of the grid data.

[0089] The passenger flow of each grid is calculated based on the data and passenger flow of the grid.

[0090] Optionally, the operator travel resident volume is obtained; the grid operator travel resident volume is obtained according to the grid data; the grid travel resident volume is obtained according to the operator travel resident volume, the grid operator travel resident volume and the current travel resident volume of the current passenger flow, and the expression is as follows:

[0091]

[0092] In this embodiment, the grid passenger flow is obtained according to the grid data and the current passenger flow, and the spatial distribution characteristics of the passenger flow are taken into consideration, which is conducive to real-time monitoring of the passenger flow dynamics of each grid.

[0093] Step S104, calculating the current key area passenger flow through the target area grid and the grid passenger flow.

[0094] Specifically, the current passenger flow in key areas includes the number of tourists in the current key areas and the number of residents traveling in the current key areas.

[0095] In some embodiments, corresponding target area grids are screened out according to the key areas, and the passenger flows of the corresponding target area grids are accumulated to obtain the total passenger flows of the corresponding target area grids, thereby obtaining the current passenger flows of the key areas.

[0096] Furthermore, the grid travel residents volume and grid tourist reception volume of the target area grid are obtained, and the grid travel residents volume of the target area grid is accumulated to obtain the current key area travel residents volume; the grid tourist reception volume of the target area grid is accumulated to obtain the current key area tourist volume.

[0097] Furthermore, historical data of key areas are obtained, and the monitoring accuracy is calculated based on the current passenger flow in the key areas and historical data.

[0098] In this embodiment, the current passenger flow in the key area is calculated through the target area grid and the grid passenger flow, so that the passenger flow monitoring of the key area is more precise, which helps the scenic area managers to implement measures such as flow control, peak shifting, and reservation travel, and also helps tourists to arrange travel plans reasonably.

[0099] Steps S101 to S104 shown in the embodiment of the present application can monitor the real-time passenger flow of tourists and non-tourists by acquiring the current passenger flow, which includes the current number of tourists received and the current number of residents traveling. The city's movable areas are gridded according to the current passenger flow to obtain grid data; the grid data is attributed to obtain the target area grid; the grid passenger flow is obtained according to the grid data and the current passenger flow, taking into account the spatial distribution characteristics of the passenger flow; the current passenger flow in key areas is calculated through the target area grid and the grid passenger flow, which helps scenic area managers to control passenger flow in advance, effectively implement flow control, peak shifting, and reservation travel measures, and is also conducive to tourists to arrange travel plans reasonably.

[0100] See also Figure 2 In some embodiments, the passenger flow monitoring method provided in the embodiment of the present application further includes a step of calculating monitoring accuracy. The step of calculating monitoring accuracy may include but is not limited to steps S201 to S203:

[0101] Step S201, obtaining historical data of key areas, and calculating monitoring accuracy based on current passenger flow in key areas and historical data.

[0102] Specifically, the historical data includes the internal flow of people in the scenic area, which is calculated based on the historical number of people entering the scenic area and the historical number of people leaving the scenic area.

[0103] In step S201 of some embodiments, the monitoring accuracy is calculated by the following expression:

[0104]

[0105] YY t =YY tt进 -YY tt出

[0106] Among them, μ is the monitoring accuracy, Y tj is the number of tourists in the scenic area at time T (i.e. the number of tourists in the current key area), Y tj居 is the number of residents traveling to the scenic area at time T (i.e., the number of residents traveling to the current key area), YY t is the flow of people staying in the scenic area at time T (i.e., the flow of people inside the scenic area), YY tt进 is the cumulative number of people who have entered the scenic spot since its opening to time T (i.e., the number of people who have left the scenic spot in history), YY tt出 It is the cumulative number of people who have left the scenic area from the time it opened to time T (i.e. the number of people who have entered the scenic area historically).

[0107] In this embodiment, historical data of key areas are obtained, and monitoring accuracy is calculated based on the current passenger flow in the key areas and the historical data, which is conducive to evaluating and improving the accuracy of the monitoring system.

[0108] It is understandable that, in theory, the closer the μ value is to 1, the better the real-time passenger flow monitoring effect will be.

[0109] Step S202, obtaining a preset accuracy threshold.

[0110] In step S202 of some embodiments, the preset accuracy threshold may be set by a user (ie, a scenic spot manager or a tourist) or may be automatically determined by the system based on historical data.

[0111] Step S203, if the monitoring accuracy is less than the preset accuracy threshold, return to the step of attribute identification of the grid data to obtain the target area grid, until the monitoring accuracy is greater than or equal to the preset accuracy threshold.

[0112] In step S203 of some embodiments, if the monitoring accuracy is less than the preset accuracy threshold, the passenger flow error in the current key area is large, and the grid data division rules need to be optimized. The step of attribute identification of the grid data is returned to obtain the target area grid until the monitoring accuracy is greater than or equal to the preset accuracy threshold.

[0113] Among them, grid division and attribute identification are continuously optimized based on passenger flow and historical data.

[0114] In this embodiment, if the monitoring accuracy is less than the preset accuracy threshold, the step of attribute identification of the grid data is returned to obtain the target area grid until the monitoring accuracy is greater than or equal to the preset accuracy threshold. This is conducive to realizing self-correcting passenger flow monitoring, continuously optimizing the passenger flow monitoring method, and improving the real-time monitoring effect of passenger flow.

[0115] See also Figure 3 In some embodiments, the passenger flow monitoring method provided in the embodiment of the present application further includes a trampling warning step, and the trampling warning step may include but is not limited to steps S301 to S305:

[0116] Step S301, obtaining the corresponding grid area according to the grid data.

[0117] In step S301 of some embodiments, the grid area of ​​each grid is obtained.

[0118] Optionally, the maximum passenger flow of each grid is obtained.

[0119] Step S302, calculating the grid passenger flow density according to the grid passenger flow and the grid area.

[0120] In step S302 of some embodiments, the grid passenger flow density and the grid passenger flow saturation are calculated according to the following expressions:

[0121]

[0122]

[0123] Among them, D i represents the grid passenger flow density, S i represents the grid passenger flow saturation, F i represents the grid passenger flow, A i represents the grid area, O i Indicates the maximum passenger flow of the grid.

[0124] Step S303, obtaining a stampede risk classification warning table.

[0125] Specifically, the stampede risk classification warning table includes the warning level, the corresponding crowd density threshold and the crowd flow status; the congestion risk classification warning table includes the warning level, the carrying density occupancy rate and the crowd flow status.

[0126] In step S303 of some embodiments, a crowd density threshold of a stampede risk classification warning table is obtained.

[0127] Optionally, obtain the load density occupancy rate of the congestion risk classification warning table.

[0128] Step S304: if the grid passenger flow density exceeds the crowd density threshold of the stampede risk classification warning table, corresponding stampede warning information is generated according to the grid data, the grid passenger flow density and the crowd density threshold.

[0129] Specifically, the stampede warning information includes the grid where the stampede warning occurs, the corresponding grid passenger flow density and the crowd density threshold; the congestion warning information includes the grid where the congestion warning occurs, the corresponding grid passenger flow density and the carrying density occupancy rate.

[0130] Optionally, if the grid passenger flow saturation exceeds the carrying density occupancy rate of the congestion risk classification warning table, corresponding congestion warning information is generated according to the grid data, the grid passenger flow density and the carrying density occupancy rate.

[0131] Step S305: In response to the first instruction, display the trampling warning information on the warning page.

[0132] Specifically, the first instruction is triggered when the grid passenger flow density exceeds the crowd density threshold, and is used to display the stampede warning information on the warning page. The stampede warning information includes the stampede warning level, crowd flow status, the grid where the stampede warning occurs, the corresponding grid passenger flow density, and the crowd density threshold.

[0133] In step S305 of some embodiments, the stampede warning level, crowd flow status, the grid where the stampede warning occurs, the corresponding grid passenger flow density, and the crowd density threshold are displayed on the warning page.

[0134] Optionally, in response to the second instruction, congestion warning information is displayed on the warning page. The second instruction is triggered when the grid passenger flow saturation exceeds the carrying density occupancy rate, and is used to display the congestion warning information on the warning page. The congestion warning information includes the congestion warning level, the crowd flow status, the grid where the congestion warning occurs, the corresponding grid passenger flow density, and the carrying density occupancy rate.

[0135] Furthermore, the warning page displays the congestion warning level, crowd flow status, the grid where the congestion warning occurs, the corresponding grid passenger flow density, and the load density occupancy rate.

[0136] See also Figure 4 In some embodiments, step S103 may include but is not limited to steps S401 to S403:

[0137] Step S401, obtaining the number of residents traveling by the operator.

[0138] Specifically, grid passenger flow includes the number of grid traveling residents and the number of grid tourists received.

[0139] In step S401 of some embodiments, operator data is obtained.

[0140] Optionally, residents who have not left their usual environment for 6 hours and have not left for more than 10 km are determined to be non-tourist residents.

[0141] Furthermore, the operator's non-travel residents are calculated based on the operator's data, and then the operator's travel residents are obtained.

[0142] Step S402, obtaining the number of residents traveling for the grid operator according to the grid data.

[0143] In step S402 of some embodiments, the base station corresponding to the grid is circled according to the grid division rule of the grid data, and then the number of traveling residents of the grid operator is obtained.

[0144] Step S403, obtaining the grid travel resident volume according to the operator travel resident volume, the grid operator travel resident volume and the current travel resident volume of the current passenger flow.

[0145] In step S403 of some embodiments, the number of grid travel residents is calculated according to the following expression:

[0146]

[0147] Figure 5 is an optional flow chart of the passenger flow monitoring method provided in the embodiment of the present application. Figure 5 The method may include but is not limited to steps S501 to S507:

[0148] Step S501, obtaining base station data, weather data, time data, temperature data, emergency data, etc. as a feature set as factors affecting the number of tourists received in the city.

[0149] Among them, base station data, weather data, time data, temperature data, and emergency data are obtained to build a feature set for analyzing and predicting the number of tourists received in the city.

[0150] For example, the real-time passenger flow monitoring diagram is as follows: Figure 6 shown.

[0151] Step S502, using the number of tourists received as the measurement caliber, calculate the number of tourists arriving at a certain city from a certain province / city.

[0152] The rule uses the arrival rate as an intermediate quantity and performs full quantity calculation. The expression is as follows:

[0153]

[0154] Among them, R i is the arrival rate from a province to a city (i.e., the tourist arrival rate), X i is the number of operators from a certain city to Tianshui City, W is the total number of operators in a certain province. Y is the number of tourists in a certain city at time T (i.e. the current number of tourists received), N i is the total population of a province (total number of tourists). 32 represents 32 provinces.

[0155] In this embodiment, the out-of-town tourists calculated based on the arrival rate and the residents based on the travel jointly define the local tourist volume, and the real-time passenger flow is scientifically monitored.

[0156] Step S503, calculating the number of residents traveling in the city.

[0157] Optionally, after monitoring the real-time passenger flow of the entire city at time T (i.e., the current passenger flow), spatial monitoring is performed on the distribution of real-time tourists, the city's movable areas are gridded, and the grid data is adjusted and optimized based on the camera data of key areas in the grid.

[0158] Among them, based on the urbanization grid, the city is divided into blocks as a whole, and the blocks in key areas are monitored in key areas.

[0159] Optionally, the signaling of local residents is monitored to find the number of non-tourist residents among them, and the number is taken as the real-time passenger flow of the current grid. The definition of residents satisfies: they have not left their usual environment for 6 hours and have not left for more than 10 km.

[0160] Furthermore, assuming that the ratio of the number of residents traveling with operators to the number of operators in the city is equal to the ratio of the number of residents traveling in the city to the number of residents in the city, the number of residents traveling in the city (i.e. the current number of residents traveling) can be estimated by calculating the proportion of residents traveling outside.

[0161]

[0162] Y 居 =r 居 ×N

[0163] Among them, r 居 is the proportion of residents traveling outside a city, X 居 Y is the number of residents who use operators in a city, and W is the total number of operators in a city. 居 The number of traveling residents in a city, N is the number of permanent residents in a city.

[0164] Step S504, calculating the number of residents traveling in the grid.

[0165] Among them, grid passenger flow includes the number of residents traveling in the grid and the number of tourists received in the grid.

[0166] According to the grid division rules, the base stations corresponding to the grid are selected to monitor the number of residents who enter the grid at a certain time. Based on the number of residents who have already calculated the number of residents who travel in the entire city, the data is batched into each grid.

[0167] Alternatively, assuming The batch weight expression of each grid is as follows:

[0168]

[0169] Y i居 =r i居 ×Y 居

[0170] Among them, r i居 is the proportion of the number of residents traveling in a certain grid to the total number of residents traveling in the whole city, that is, the batch weight of the number of residents traveling in the grid, X i居 is the number of residents traveling for a grid operator, W 居 Y is the number of residents traveling by operators in the city, 居 The number of residents traveling in a city.

[0171] Furthermore, coefficient adjustments will be required later, and the weights can be adjusted according to the actual cameras at the target location.

[0172] Step S505, use the grid to find key areas, identify the attributes of each network based on map data and field surveys, and associate and match the grid attributes with specific parks, scenic spots, business districts, blocks or transportation hubs.

[0173] For example, a scenic spot may occupy 10 grids, such as grid 1, grid 2, etc. When grids are associated with key areas, key areas cannot be divided only according to obvious boundary signs such as walls. The main traffic arteries around key areas, especially those that are prone to crowds and vehicles, are monitored together with key areas. At the same time, the aggregated passenger flow is used as the real-time passenger flow of key areas.

[0174] It can be understood that the grid attributes are associated and matched with specific parks, scenic spots, business districts, blocks or transportation hubs (i.e., key areas) to obtain the target area grid.

[0175] Step S506: Optimize and adjust the passenger flow in relevant key areas in real time.

[0176] Among them, the real-time number of tourists in key places is calculated by associating key areas with grids (updated every hour), and the monitoring objects meet the technical definition of tourists throughout the process. After calculating the real-time number of tourists and the number of traveling residents, after obtaining the historical data of key scenic spots and business districts at the target location, the two are compared to test the accuracy of real-time monitoring. Assume that the reception data of a scenic spot during the holiday period has been obtained, set as YY t ,but:

[0177]

[0178] YY t =YY tt进 -YY tt出

[0179] Among them, μ is the real-time monitoring accuracy of scenic spot signaling, Y tj is the number of tourists in the scenic area at time T, Y tj居 YY is the number of residents traveling to the scenic spot at time T. t is the number of people staying in the scenic area at time T, YY tt进 YY is the cumulative number of people who enter the scenic area from the time the scenic area opens to time T. tt出 is the cumulative number of people who leave the scenic area from the time the scenic area opens to time T. In theory, the closer the μ value is to 1, the better the real-time passenger flow monitoring effect is. When the μ value deviates far from 1, it is necessary to fine-tune the monitoring algorithm and the association method between the grid and the scenic area.

[0180] Step S507, calculating the passenger flow density and passenger flow saturation within the grid.

[0181] Optionally, the risk of congestion and stampede can be calculated based on the grid load and area, and scientific warning and treatment can be carried out.

[0182] It is understandable that the passenger flow density and passenger flow saturation in any grid can be calculated, and the passenger flow density and passenger flow saturation in the target area grid can also be calculated.

[0183] The expressions for calculating the passenger flow density and passenger flow saturation within the grid are as follows:

[0184]

[0185] Among them, D i and S i Indicates the density and saturation of passenger flow.

[0186] Furthermore, according to the calculated density and saturation, the stampede risk classification warning table and the congestion risk classification warning table are matched. The risk assessment value is obtained, and timely warning and processing are made. For example, the schematic diagram of the stampede risk classification warning table is as follows: Figure 7 As shown in the figure, the diagram of the congestion risk classification warning table is as follows Figure 8 As shown. Among them, the stampede risk classification warning table and the congestion risk classification warning table are associated with the grid whose passenger flow density and saturation are calculated. For example, the passenger flow density and passenger flow saturation in the target area grid corresponding to scenic spot A are calculated, and the stampede risk classification warning table and congestion risk classification warning table of scenic spot A are obtained, so as to issue an early warning.

[0187] The embodiment of the present application defines the passenger flow monitoring range, distinguishes between real-time monitoring of tourists and non-tourists, realizes real-time monitoring of key areas, completes the correlation research between urban grids and key areas, completes the real-time passenger flow and non-tourist resident calculation model research in specific areas, and generates saturation and density-related risks based on the historical carrying capacity and area of ​​key areas, and pushes timely warnings.

[0188] The present invention proposes a passenger flow monitoring algorithm based on multimodality, which is different from the traditional passenger flow monitoring algorithm. It mainly uses base station data, introduces influencing factors such as weather, time, temperature, and emergencies as multi-source data calculation and analysis, and uses the calculated and analyzed historical data to predict future data. At the same time, in order to improve the accuracy of historical passenger flow data, based on base station data, a passenger flow identification method based on location data is introduced to effectively identify tourist data. Thus, an algorithm model that can effectively monitor the passenger flow of scenic spots is constructed. It helps scenic area managers to carry out passenger flow control in advance, effectively implement measures such as flow restriction, peak shifting, and reservation travel, and at the same time helps tourists to reasonably arrange travel plans.

[0189] See also Fig. 9 The embodiment of the present application further provides a passenger flow monitoring device, which can implement the above passenger flow monitoring method, and the device includes:

[0190] The gridding module 901 is used to obtain the current passenger flow, grid the movable area of ​​the city according to the current passenger flow, and obtain grid data. The current passenger flow includes the current number of tourists received and the current number of residents traveling;

[0191] An identification module 902 is used to identify the attributes of the grid data to obtain a target area grid, and the target area grid is associated with the key area;

[0192] A grid passenger flow calculation module 903 is used to obtain the grid passenger flow according to the grid data and the current passenger flow;

[0193] The key area passenger flow calculation module 904 is used to calculate the current key area passenger flow through the target area grid and the grid passenger flow. The current key area passenger flow includes the current key area tourist volume and the current key area traveling resident volume.

[0194] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0195] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above passenger flow monitoring method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0196] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0197] See also Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0198] The processor 1001 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0199] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1002, and the processor 1001 calls and executes the passenger flow monitoring method of the embodiment of this application;

[0200] Input / output interface 1003, used to implement information input and output;

[0201] The communication interface 1004 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0202] A bus 1005 , which transmits information between various components of the device (e.g., the processor 1001 , the memory 1002 , the input / output interface 1003 , and the communication interface 1004 );

[0203] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0204] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned passenger flow monitoring method is implemented.

[0205] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0206] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0207] The passenger flow monitoring method, passenger flow monitoring device, electronic device and storage medium provided in the embodiments of the present application can monitor the passenger flow of tourists and non-tourists in real time by acquiring the current passenger flow, which includes the current number of tourists received and the current number of residents traveling. The active areas of the city are gridded according to the current passenger flow to obtain grid data; the grid data is attributed to obtain the target area grid; the grid passenger flow is obtained according to the grid data and the current passenger flow, taking into account the spatial distribution characteristics of the passenger flow; the current passenger flow in key areas is calculated through the target area grid and the grid passenger flow, which helps scenic area managers to carry out passenger flow control in advance, effectively implement flow control, peak shifting, reservation travel and other measures, and is also conducive to tourists to arrange travel plans reasonably.

[0208] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0209] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0210] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0211] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0212] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0213] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0214] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0215] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0216] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0217] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0218] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A passenger flow monitoring method, characterized in that: The method comprises the following steps: Obtaining the current passenger flow, and gridding the movable area of ​​the city according to the current passenger flow to obtain grid data, wherein the current passenger flow includes the current number of tourists received and the current number of residents traveling; Performing attribute identification on the grid data to obtain a target area grid, wherein the target area grid is associated with a key area; Obtaining a grid passenger flow according to the grid data and the current passenger flow; The current passenger flow in the key area is calculated by using the target area grid and the grid passenger flow, and the current passenger flow in the key area includes the number of tourists in the key area and the number of residents traveling in the key area.

2. The method according to claim 1, characterized in that The obtaining of the current passenger flow includes: Get the current number of tourists received; The number of non-tourist residents is obtained, and the current number of traveling residents is determined according to the number of non-tourist residents.

3. The method according to claim 2, characterized in that The obtaining of the current number of tourists received includes: Get the tourist arrival rate and total number of tourists; The current number of tourists received is obtained by performing a full calculation based on the total number of tourists and the tourist arrival rate.

4. The method according to claim 1, characterized in that: The method further comprises: Obtaining historical data of the key area, and calculating monitoring accuracy based on the current passenger flow of the key area and the historical data; Get the preset accuracy threshold; If the monitoring accuracy is less than the preset accuracy threshold, the process returns to the step of performing attribute identification on the grid data to obtain the target area grid, until the monitoring accuracy is greater than or equal to the preset accuracy threshold.

5. The method according to claim 1, characterized in that: The method further comprises: Acquire a corresponding grid area according to the grid data; Calculate the grid passenger flow density according to the grid passenger flow and the grid area; Obtain the stampede risk classification warning form; If the grid passenger flow density exceeds the crowd density threshold of the stampede risk classification warning table, corresponding stampede warning information is generated according to the grid data, the grid passenger flow density and the crowd density threshold; In response to the first instruction, the stampede warning information is displayed on the warning page.

6. The method according to claim 1, characterized in that The method further comprises: Acquire the corresponding maximum passenger flow of the grid according to the grid data; Calculate the grid passenger flow saturation according to the grid passenger flow and the grid maximum passenger flow; Obtain the congestion risk classification warning form; If the grid passenger flow saturation exceeds the load density occupancy rate of the congestion risk classification warning table, corresponding congestion warning information is generated according to the grid data, the grid passenger flow density and the load density occupancy rate; In response to the second instruction, the congestion warning information is displayed on the warning page.

7. The method according to claim 1, characterized in that The grid passenger flow includes the number of grid residents traveling and the number of grid tourists received. The grid passenger flow is obtained according to the grid data and the current passenger flow, including: Obtain the number of residents traveling by the operator; Obtaining the number of residents traveling by the grid operator according to the grid data; The grid travel population is obtained according to the operator travel population, the grid operator travel population and the current travel population of the current passenger flow. The expression is as follows:

8. A passenger flow monitoring device, characterized in that: The device comprises: A gridding module is used to obtain the current passenger flow, grid the movable area of ​​the city according to the current passenger flow, and obtain grid data. The current passenger flow includes the current number of tourists received and the current number of residents traveling; An identification module, used for performing attribute identification on the grid data to obtain a target area grid, wherein the target area grid is associated with a key area; A grid passenger flow calculation module, used to obtain the grid passenger flow according to the grid data and the current passenger flow; The key area passenger flow calculation module is used to calculate the current key area passenger flow through the target area grid and the grid passenger flow, wherein the current key area passenger flow includes the current key area tourist volume and the current key area traveling resident volume.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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