Scenic area visitor flow control method, device and equipment and storage medium

Abstract

The invention provides a scenic spot visitor flow control method and device, equipment and a storage medium. The method comprises the following steps: acquiring traffic flow data of each traffic device of a scenic spot at a target moment; wherein the traffic flow data is the total flow of people entering the scenic spot through traffic equipment at each moment before a target moment within a preset time period, and the target moment is determined according to an actual prediction moment; according to a preset first relational expression, the traffic flow data of each traffic device, the traffic flow data coefficient corresponding to the target moment and a preset second relational expression, determining a pedestrian flow time distribution curve expression of the to-be-predicted time; determining a human traffic prediction value of the to-be-predicted region corresponding to each moment in the to-be-predicted time based on the human traffic time distribution curve expression; and performing human traffic control according to the human traffic prediction value of the to-be-predicted area corresponding to each moment in the to-be-predicted time. According to the method, the precision of scenic spot visitor flow control is improved.

Description

Technical Field

[0001] The present application relates to the field of smart tourism technology, and in particular to a method, device, equipment and storage medium for controlling the flow of people in a scenic area. Background Art

[0002] With the improvement of living standards, people's demand for tourism is increasing, which leads to crowd congestion, lack of supply of corresponding materials or other dangerous situations in scenic spots. Therefore, it is necessary to predict the flow of people in scenic spots to assist scenic spot managers in reasonably guiding and managing the flow of people in scenic spots.

[0003] In the related technology, radial basis neural network and hybrid optimization algorithm are used to predict the flow of tourists in scenic spots. Specifically, the historical flow of tourists and characteristic variables of various scenic spots in a certain place are collected. The characteristic variables mainly include historical data of weather, holidays, seasons, and economic indexes; a three-layer radial basis neural network model is established, and the hidden layer center and variance are first determined by the k-means algorithm, and then the model is optimized by artificial fish swarm and particle swarm algorithms; the predicted data for the next few days is input into the optimized model to predict the number of tourists in each scenic spot in a certain place in the next few days, which can help scenic spot managers make decisions, but the accuracy of scenic spot flow control is low. Summary of the invention

[0004] The present application provides a method, device, equipment and storage medium for controlling the flow of people in a scenic spot, so as to solve the technical problem of low precision in controlling the flow of people in a scenic spot.

[0005] In a first aspect, the present application provides a method for controlling the flow of people in a scenic spot, comprising:

[0006] Obtaining traffic flow data of each transportation device in the scenic area at the target time; wherein the traffic flow data is the total flow of people entering the scenic area through the transportation devices at each time before the target time during the preset period, and the target time is determined according to the actual predicted time;

[0007] According to the preset first relational expression, the traffic flow data of each traffic device, the traffic flow data coefficient corresponding to the target time, and the preset second relational expression, the expression of the time distribution curve of the flow of people at the time to be predicted is determined; wherein the preset first relational expression represents the correlation between the fitting parameters and the traffic flow data of each traffic device, the traffic flow data coefficient corresponding to each moment in the preset time period is predetermined, and the preset second relational expression is used to represent the correlation between each moment in the preset time period and the flow of people in the area to be predicted;

[0008] Determine the predicted value of the flow of people in the area to be predicted corresponding to each moment within the predicted time based on the expression of the time distribution curve of the flow of people;

[0009] Crowd flow control is performed based on the predicted value of the crowd flow in the predicted area corresponding to each moment within the predicted time.

[0010] In a possible implementation, according to a preset first relational expression, traffic flow data of each traffic device, a traffic flow data coefficient corresponding to the target time, and a preset second relational expression, an expression of a time distribution curve of a flow of people at a time to be predicted is determined, including:

[0011] Input the traffic flow data of each traffic device and the traffic flow data coefficient corresponding to the target time into a preset first relationship formula to obtain the target fitting parameter corresponding to the time to be predicted;

[0012] The target fitting parameters corresponding to the time to be predicted are input into the preset second relational expression to obtain the expression of the time distribution curve of the passenger flow at the time to be predicted.

[0013] In a possible implementation, determining the predicted value of the flow of people in the area to be predicted corresponding to each moment within the predicted time based on the expression of the time distribution curve of the flow of people includes:

[0014] Obtain target traffic information of each traffic device, where the target traffic information is information obtained by translating the source traffic information on the time axis in the direction of increasing time by L hours, and the source traffic information is the flow of people carried by the traffic device at each time before the target time within a preset time period, and L is an integer greater than 0;

[0015] According to the target traffic information of each traffic equipment and the expression of the time distribution curve of the passenger flow, the predicted value of the passenger flow in the area to be predicted corresponding to each moment within the predicted time is determined.

[0016] In one possible implementation, L is 3.

[0017] In a possible implementation, the predicted value of the passenger flow in the area to be predicted corresponding to each moment in the predicted time is determined according to the target traffic information of each traffic device and the expression of the passenger flow time distribution curve, including:

[0018] Performing weighted averaging on the target traffic information of each traffic device based on a first preset weight to obtain a first weighted average result;

[0019] Performing weighted averaging on the first weighted average result and the pedestrian flow time distribution curve expression based on a second preset weight to obtain a second weighted average result;

[0020] The predicted value of the flow of people in the area to be predicted corresponding to each moment within the time to be predicted is determined according to the second weighted average result.

[0021] In a possible implementation, before determining the pedestrian flow time distribution curve expression for the time to be predicted according to the preset first relationship, the traffic flow data of each traffic device, the traffic flow data coefficient corresponding to the target time, and the preset second relationship, the method further includes:

[0022] Obtain the historical time distribution information of the daily flow of people and historical traffic information of the area to be predicted in the scenic area within the target time period; wherein the target time period is determined according to the time to be predicted, the historical flow of people time distribution information includes each moment in the preset time period and the flow of people corresponding to each moment, and the historical traffic information is the flow of people carried by each traffic equipment at each moment;

[0023] Fitting the daily historical passenger flow time distribution information respectively to obtain the daily preset second relationship formula; determining the daily fitting parameters respectively according to the daily preset second relationship formula;

[0024] Determine the historical traffic flow data of each traffic facility in the scenic area at each time of the day according to the historical traffic information of each day;

[0025] The preset first relationship is determined according to the fitting parameters of each day and the historical traffic flow data of each traffic equipment in the scenic area at each time of each day.

[0026] In a possible implementation, the flow of people is controlled according to the predicted value of the flow of people in the area to be predicted corresponding to each moment within the time to be predicted, including:

[0027] When the predicted passenger flow value reaches a preset threshold, the operating speed of the traffic equipment is reduced.

[0028] In a second aspect, the present application provides a scenic spot crowd flow control device, the device comprising: an acquisition module, a first determination module, a second determination module and a control module, wherein:

[0029] An acquisition module is used to acquire the traffic flow data of each traffic equipment in the scenic area at the target time; wherein the traffic flow data is the total flow of people entering the scenic area through the traffic equipment at each time before the target time within a preset period, and the target time is determined according to the actual predicted time;

[0030] The first determination module is used to determine the expression of the time distribution curve of the flow of people at the time to be predicted according to the preset first relationship, the traffic flow data of each traffic device, the traffic flow data coefficient corresponding to the target time, and the preset second relationship; wherein the preset first relationship represents the correlation between the fitting parameters and the traffic flow data of each traffic device, the traffic flow data coefficient corresponding to each moment in the preset time period is predetermined, and the preset second relationship is used to represent the correlation between each moment in the preset time period and the flow of people in the area to be predicted;

[0031] The second determination module is used to determine the predicted value of the flow of people in the area to be predicted corresponding to each moment in the predicted time based on the expression of the time distribution curve of the flow of people;

[0032] The control module is used to control the flow of people according to the predicted value of the flow of people in the predicted area corresponding to each moment within the predicted time.

[0033] In a possible implementation manner, the first determining module is specifically configured to:

[0034] Input the traffic flow data of each traffic device and the traffic flow data coefficient corresponding to the target time into a preset first relationship formula to obtain the target fitting parameter corresponding to the time to be predicted;

[0035] The target fitting parameters corresponding to the time to be predicted are input into the preset second relational expression to obtain the expression of the time distribution curve of the passenger flow at the time to be predicted.

[0036] In a possible implementation manner, the second determining module is specifically configured to:

[0037] Obtain target traffic information of each traffic device, where the target traffic information is information obtained by translating the source traffic information on the time axis in the direction of increasing time by L hours, and the source traffic information is the flow of people carried by the traffic device at each time before the target time within a preset time period, and L is an integer greater than 0;

[0038] According to the target traffic information of each traffic equipment and the expression of the time distribution curve of the passenger flow, the predicted value of the passenger flow in the area to be predicted corresponding to each moment within the predicted time is determined.

[0039] In one possible implementation, L is 3.

[0040] In a possible implementation manner, the second determining module is specifically configured to:

[0041] Performing weighted averaging on the target traffic information of each traffic device based on a first preset weight to obtain a first weighted average result;

[0042] Performing weighted averaging on the first weighted average result and the pedestrian flow time distribution curve expression based on a second preset weight to obtain a second weighted average result;

[0043] The predicted value of the flow of people in the area to be predicted corresponding to each moment within the time to be predicted is determined according to the second weighted average result.

[0044] In a possible implementation, the device further includes:

[0045] The acquisition module is used to obtain the historical time distribution information of the flow of people and the historical traffic information of the area to be predicted in the scenic area every day within the target time period; wherein the target time period is determined according to the time to be predicted, the historical flow of people time distribution information includes each moment in the preset time period and the flow of people corresponding to each moment, and the historical traffic information is the flow of people carried by each traffic equipment at each moment;

[0046] A fitting module is used to fit the daily historical passenger flow time distribution information to obtain a preset second relationship formula for each day; and to determine the fitting parameters for each day according to the preset second relationship formula for each day;

[0047] The third determination module is used to determine the historical traffic flow data of each traffic equipment in the scenic area at each time of the day according to the historical traffic information of each day;

[0048] The fourth determination module is used to determine the preset first relationship according to the fitting parameters of each day and the historical traffic flow data of each traffic equipment in the scenic area at each time of each day.

[0049] In a possible implementation, the control module is specifically configured to:

[0050] When the predicted passenger flow value reaches a preset threshold, the operating speed of the traffic equipment is reduced.

[0051] In a third aspect, the present application provides an electronic device, including: a processor and a memory;

[0052] Memory stores computer-executable instructions;

[0053] The processor executes the computer execution instructions stored in the memory, so that the processor executes the scenic spot crowd flow control method as described in the first aspect.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by a processor, they are used to implement the scenic spot passenger flow control method described in the first aspect.

[0055] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for controlling the flow of people in a scenic spot as described in the first aspect.

[0056] In a sixth aspect, the present application provides a chip having a computer program stored thereon. When the computer program is executed by the chip, the method for controlling the flow of people in a scenic spot as described in the first aspect is implemented.

[0057] In a possible implementation manner, the chip is a chip in a chip module.

[0058] The present application provides a method, device, equipment and storage medium for controlling the flow of people in a scenic area. The method obtains traffic flow data of each traffic equipment in the scenic area at a target time; wherein the traffic flow data is the total flow of people entering the scenic area through the traffic equipment at each time within a preset time period and before the target time, and the target time is determined according to the actual predicted time; according to a preset first relationship, the traffic flow data of each traffic equipment, the traffic flow data coefficient corresponding to the target time and a preset second relationship, the expression of the time distribution curve of the flow of people at the time to be predicted is determined; wherein the preset first relationship characterizes the correlation between the fitting parameters and the traffic flow data of each traffic equipment, the traffic flow data coefficient corresponding to each time in the preset time period is predetermined, and the preset second relationship is used to characterize the correlation between each time in the preset time period and the flow of people in the area to be predicted; based on the expression of the time distribution curve of the flow of people, the predicted value of the flow of people in the area to be predicted corresponding to each time within the time to be predicted is determined; and the flow of people is controlled according to the predicted value of the flow of people in the area to be predicted corresponding to each time within the time to be predicted. The traffic flow data of the traffic equipment and other relevant preset relationships are fully considered to obtain the accurate pedestrian flow prediction value of the to-be-predicted area corresponding to each moment within the to-be-predicted time, and then the pedestrian flow is controlled based on the pedestrian flow prediction value of the to-be-predicted area corresponding to each moment. Compared with the general control of pedestrian flow based on the total pedestrian flow prediction value of one day, the present application has higher accuracy in pedestrian flow control based on the detailed pedestrian flow prediction value of the to-be-predicted area corresponding to each moment. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0060] Figure 1 A schematic diagram of a scenario applicable to the embodiments of the present application;

[0061] Figure 2 A flow chart of a method for controlling the flow of people in a scenic spot provided in an embodiment of the present application Figure 1 ;

[0062] Figure 3 A flow chart of a method for controlling the flow of people in a scenic spot provided in an embodiment of the present application Figure 2 ;

[0063] Figure 4a Schematic diagram of the result of the crowd flow prediction of a scenic spot to be predicted provided in the embodiment of the present application Figure 1 ;

[0064] Figure 4b Schematic diagram of the result of the crowd flow prediction of a scenic spot to be predicted provided in the embodiment of the present application Figure 2 ;

[0065] Figure 5 A schematic diagram of the structure of a scenic spot crowd flow control device provided in an embodiment of the present application;

[0066] Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0067] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0068] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0070] With the improvement of living standards, people's demand for tourism is increasing, which leads to crowd congestion, lack of supply of corresponding materials or other dangerous situations in scenic spots. Therefore, it is necessary to predict the flow of people in scenic spots to assist scenic spot managers in reasonably guiding and managing the flow of people in scenic spots.

[0071] In some implementations, mathematical modeling is performed on the problem of crowd density, which is converted into a time series processing and prediction problem. The classic autoregressive integrated moving average model (ARIMA) and gray model (GM) (also called gray model or gray prediction model) are introduced to model the crowd sequence. However, this method uses the crowd density data of a scenic spot at the hour for three consecutive months, which requires too much data and results in high labor and time costs.

[0072] In other implementations, a combined prediction model based on empirical mode decomposition (EMD) and least squares support vector machines (LSSVM) is used to adaptively decompose the original scenic spot passenger flow into a finite number of stable intrinsic mode functions (IMFs) and residual term functions based on the nonlinear and non-stationary characteristics of the scenic spot passenger flow. This effectively reduces data complexity. However, this method predicts the total number of scenic spot passenger flow on each day over multiple days, and cannot provide passenger flow prediction values ​​at different time points within a day, and thus cannot control the scenic spot passenger flow based on the passenger flow prediction values ​​at different time points within a day, resulting in low accuracy in scenic spot passenger flow control.

[0073] In other implementations, radial basis neural networks and hybrid optimization algorithms are used to predict the flow of people in scenic spots. Specifically, the historical flow of people and characteristic variables of each scenic spot in a certain place are collected, and the characteristic variables mainly include historical data of weather, holidays, seasons, and economic indexes; a three-layer radial basis neural network model is established, and the hidden layer center and variance are first determined by the k-means algorithm, and then the model is optimized by artificial fish swarm and particle swarm algorithms; the predicted data for the next few days is input into the optimized model to predict the total number of tourists in each scenic spot in a certain place in the next few days, which helps the scenic spot managers make decisions, but it is impossible to predict the flow of people at different time points in a day, and then it is impossible to control the flow of people in the scenic spot according to the predicted values ​​of the flow of people at different time points in a day, which makes the control accuracy of the flow of people in the scenic spot low.

[0074] In view of this, an embodiment of the present application provides a method for controlling the flow of people in a scenic area. The method combines the effective flow data of people transported by various transportation equipment in the scenic area before the actual prediction time, a pre-established regression equation and a preset flow polynomial expression to determine a flow distribution curve expression for the time to be predicted. Based on the flow distribution curve expression, the flow of people at each moment in the time to be predicted is accurately predicted, so that the flow of people is controlled in advance based on the predicted value of the flow of people at each moment. In this way, the flow of people can be dynamically controlled by the predicted value of the flow of people at each moment, thereby achieving the effect of improving the accuracy of flow control in the scenic area.

[0075] For example, Figure 1 Schematic diagram of a scenario applicable to the embodiment of the present application is shown. Figure 1 As shown, the scenario to which the embodiment of the present application is applicable may include a system 101, a server 102, and a display unit 103 corresponding to a scenic spot.

[0076] The system 101 corresponding to the scenic spot can be used to provide the data or information required for the scenic spot flow prediction in the embodiment of the present application, such as traffic flow data, historical flow time distribution information and historical traffic information, etc. Exemplarily, the system 101 corresponding to the scenic spot can be, for example, a management system or a scenic spot database of the scenic spot.

[0077] Among them, the server 102 can predict the flow of people in the predicted area at each time within the predicted time according to the scenic spot flow control method provided in the embodiment of the present application. The server 102 can also send the predicted value of the flow of people in the predicted area at each time within the predicted time to the display unit 103 for display.

[0078] Exemplarily, the method for controlling the flow of people in a scenic area may include: obtaining traffic flow data of each traffic equipment in the scenic area at a target time; wherein the traffic flow data is the total flow of people entering the scenic area through the traffic equipment at each time within a preset time period and before the target time, and the target time is determined according to the actual predicted time; determining a time distribution curve expression of the flow of people at a time to be predicted according to a preset first relationship, traffic flow data of each traffic equipment, a traffic flow data coefficient corresponding to the target time, and a preset second relationship; wherein the preset first relationship characterizes the correlation between the fitting parameters and the traffic flow data of each traffic equipment, the traffic flow data coefficient corresponding to each time in the preset time period is predetermined, and the preset second relationship is used to characterize the correlation between each time in the preset time period and the flow of people in the area to be predicted; determining the predicted value of the flow of people in the area to be predicted corresponding to each time within the time to be predicted based on the time distribution curve expression of the flow of people; and performing flow control according to the predicted value of the flow of people in the area to be predicted corresponding to each time within the time to be predicted. The traffic flow data of the traffic equipment and other relevant preset relationships are fully considered to obtain the accurate pedestrian flow prediction value of the to-be-predicted area corresponding to each moment within the to-be-predicted time, and then the pedestrian flow is controlled based on the pedestrian flow prediction value of the to-be-predicted area corresponding to each moment. Compared with the general control of pedestrian flow based on the total pedestrian flow prediction value of one day, the embodiment of the present application has higher accuracy in controlling pedestrian flow based on the detailed pedestrian flow prediction value of the to-be-predicted area corresponding to each moment.

[0079] In the embodiments of the present application, the server 102 may be, for example, an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms, but the embodiments of the present application are not limited to this.

[0080] The display unit 103 can be used to display the predicted value of the flow of people at each time in the predicted area within the predicted time. For example, the display unit can be a large visual screen in the scenic area, a scenic area client, etc., so that the scenic area management personnel can make scientific decisions and reasonable resource allocation according to the predicted value of the flow of people at different times, and also provide reference opinions for tourists to travel, avoid congestion, and thus improve the quality and satisfaction of tourists.

[0081] In addition, the scenario architecture described in the embodiments of the present application is intended to more clearly illustrate the technical solutions of the embodiments of the present application, and does not constitute a limitation on the technical solutions provided in the embodiments of the present application. A person of ordinary skill in the art can appreciate that with the evolution of the architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0082] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0083] Figure 2 A flow chart of a method for controlling the flow of people in a scenic spot provided in an embodiment of the present application Figure 1 The execution subject of this method can be Figure 1 The server 102 shown in the figure may be specifically executed by a subject according to the actual application scenario. Figure 2 As shown, the method may include:

[0084] S201. Obtain traffic flow data of each transportation equipment in the scenic area at the target time; wherein the traffic flow data is the total flow of people entering the scenic area through the transportation equipment at each time within a preset time period and before the target time, and the target time is determined based on the actual predicted time.

[0085] In the embodiment of the present application, the actual prediction time can be understood as the time to obtain traffic flow data. The time to obtain traffic flow data can be set according to the actual scenario, and the embodiment of the present application does not specifically limit this. The actual prediction time can be a non-hourly time. In this case, the target time can be an hourly time adjacent to the actual prediction time and before the actual prediction time. For example, if the actual prediction time is 9:30 am, the target time can be 9 am. The actual prediction time can also be an hourly time. In this case, the target time can be the same as the actual prediction time.

[0086] In the embodiment of the present application, the preset time period can be understood as the time period corresponding to the valid data, and the preset time period can be set according to the actual scene. For example, based on the actual scene of the scenic spot, the number of people entering the scenic spot too early or too late can be ignored, so the preset time period can be from 7 am to 7 pm.

[0087] In the embodiment of the present application, the transportation equipment may be a means of transportation used to carry tourists in and out of the scenic area, and the transportation equipment may be, for example, a cableway, a sightseeing car, etc. The system corresponding to the scenic area may obtain the flow of people entering the scenic area and the flow of people leaving the scenic area through the transportation equipment at each time, and obtain the total flow of people entering the scenic area by subtracting the flow of people leaving the scenic area from the flow of people entering the scenic area.

[0088] In a possible implementation, the server can periodically obtain the traffic flow data of each traffic device in the scenic area at the target time from the system corresponding to the scenic area according to a preset period. The preset period can be set according to the actual scenario, and the embodiment of the present application does not specifically limit this. For example, the preset period can be 1 hour, that is, the server can obtain traffic flow data every 1 hour, and then predict the flow of people based on the obtained traffic flow data. The specific implementation of the prediction will be described in detail in the subsequent steps and will not be repeated here.

[0089] S202. Determine the expression of the time distribution curve of the flow of people at the time to be predicted according to the preset first relationship, the traffic flow data of each traffic equipment, the traffic flow data coefficient corresponding to the target time and the preset second relationship; wherein the preset first relationship characterizes the correlation between the fitting parameters and the traffic flow data of each traffic equipment, the traffic flow data coefficient corresponding to each moment in the preset time period is predetermined, and the preset second relationship is used to characterize the correlation between each moment in the preset time period and the flow of people in the area to be predicted.

[0090] In the embodiment of the present application, the traffic flow data coefficient can be understood as a coefficient in the preset first relational expression used to reflect the correlation between the fitting parameter and the traffic flow data of each traffic device. Traffic flow data at different times correspond to traffic flow data coefficients at different times, and traffic flow data of different traffic devices correspond to different traffic flow data coefficients. Among them, the fitting parameter can be understood as a coefficient in the preset second relational expression used to reflect the correlation between the flow of people at each time in the preset period and the area to be predicted.

[0091] In the embodiment of the present application, the time to be predicted can be understood as 24 hours a day of the date to be predicted, but based on the actual scenario of the scenic spot, the number of people entering the scenic spot too early or too late can be ignored. Therefore, the time to be predicted can be a preset time period of the date to be predicted, and the actual prediction time can be any time within the date to be predicted.

[0092] In the embodiment of the present application, the time distribution curve expression of the flow of people at the time to be predicted can be used to characterize the correlation between the predicted flow of people at each moment in the time to be predicted and the predicted value of the area to be predicted corresponding to each moment. The area to be predicted can be the central scenic spot area of ​​the scenic spot.

[0093] In a possible implementation, the traffic flow data of each traffic equipment corresponding to the target time and the traffic flow data coefficient corresponding to the target time are input into a preset first relationship to obtain an output result, and the output result is input into a preset second relationship to obtain an expression of the time distribution curve of the passenger flow at the time to be predicted.

[0094] S203: Determine the predicted value of the flow of people in the area to be predicted corresponding to each moment within the time to be predicted based on the expression of the time distribution curve of the flow of people.

[0095] In a possible implementation, each moment within the time to be predicted is input into the expression of the time distribution curve of the flow of people, and the predicted value of the flow of people in the area to be predicted corresponding to each moment is obtained.

[0096] In another possible implementation, the expression of the time distribution curve of passenger flow is optimized based on the passenger flow at each moment within the preset time period and before the target moment within the time to be predicted, and each moment within the time to be predicted is input into the optimized time distribution curve expression of passenger flow to obtain the passenger flow prediction value of the area to be predicted corresponding to each moment.

[0097] S204, controlling the flow of people according to the predicted value of the flow of people in the area to be predicted corresponding to each moment within the time to be predicted.

[0098] In a possible implementation, the server can control the running speed of the traffic equipment based on the predicted value of the flow of people in the area to be predicted at each time, thereby realizing the control of the flow of people in the area to be predicted in the scenic area. In other possible implementations, the server can output instruction information based on the predicted value of the flow of people, and the scenic area management personnel can adjust the staff in advance for each time according to the instruction information, and guide tourists in time, thereby realizing the control of the flow of people in the area to be predicted in the scenic area.

[0099] In an embodiment of the present application, traffic flow data of each traffic equipment in the scenic area at a target time is obtained; wherein the traffic flow data is the total flow of people entering the scenic area through the traffic equipment at each time within a preset time period and before the target time, and the target time is determined according to the actual predicted time; according to a preset first relationship, the traffic flow data of each traffic equipment, the traffic flow data coefficient corresponding to the target time and a preset second relationship, an expression of a time distribution curve of the flow of people at a time to be predicted is determined; wherein the preset first relationship characterizes the correlation between the fitting parameters and the traffic flow data of each traffic equipment, the traffic flow data coefficient corresponding to each time in the preset time period is predetermined, and the preset second relationship characterizes the correlation between each time in the preset time period and the flow of people in the area to be predicted; based on the expression of the time distribution curve of the flow of people, the predicted value of the flow of people in the area to be predicted corresponding to each time within the time to be predicted is determined; and the flow of people is controlled according to the predicted value of the flow of people in the area to be predicted corresponding to each time within the time to be predicted. The traffic flow data of the traffic equipment and other relevant preset relationships are fully considered to obtain the accurate pedestrian flow prediction value of the to-be-predicted area corresponding to each moment within the to-be-predicted time, and then the pedestrian flow is controlled based on the pedestrian flow prediction value of the to-be-predicted area corresponding to each moment. Compared with the general control of pedestrian flow based on the total pedestrian flow prediction value of one day, the embodiment of the present application has higher accuracy in controlling pedestrian flow based on the detailed pedestrian flow prediction value of the to-be-predicted area corresponding to each moment.

[0100] On the basis of the above embodiments, in order to more clearly describe the technical solutions provided by the embodiments of the present application, for example, please refer to Figure 3 . Figure 3 The flow chart of a method for controlling the flow of people in a scenic spot provided by an embodiment of the present application is shown as follows Figure 2 The execution subject of this method can be Figure 1 The server 102 shown in the figure may be specifically executed by a subject according to the actual application scenario. Figure 3 As shown, the method may include:

[0101] S301. Obtain the historical time distribution information of the daily passenger flow and historical traffic information of the area to be predicted in the scenic area during the target time period; wherein the target time period is determined based on the time to be predicted, the historical passenger flow time distribution information includes each moment in the preset time period and the passenger flow corresponding to each moment, and the historical traffic information is the passenger flow carried by each transportation equipment at each moment.

[0102] Optionally, the target time period may be a preset period of N days adjacent to the time to be predicted and before the time to be predicted, and N may be set according to the actual scenario. In order to make the traffic flow data coefficient determined subsequently more accurate, and thus make the final pedestrian flow prediction value more accurate, N may be set to 5. For example, assuming that the time to be predicted is from 7 a.m. to 7 p.m. on November 1, the target time period may be from 7 a.m. to 7 p.m. every day between October 27 and October 31.

[0103] Optionally, the target time period may also be a preset period from N1 days adjacent to the time to be predicted and before the time to be predicted in the previous year to N2 days adjacent to the time to be predicted and after the time to be predicted in the previous year. For example, assuming that the time to be predicted is from 7 am to 7 pm on November 1, 2023, the target time period may be from 7 am to 7 pm every day between October 30 and November 3, 2022.

[0104] Optionally, the target time period can also be a preset period of N days adjacent to the time to be predicted and before the time to be predicted, and a preset period of N1 days adjacent to the time to be predicted and before the time to be predicted in the previous year to N2 days adjacent to the time to be predicted and after the time to be predicted in the previous year.

[0105] The embodiment of the present application only needs to obtain historical data of a few days before the time to be predicted or a few days before and after the time to be predicted last year (such as historical pedestrian flow time distribution information and historical traffic information) to perform subsequent pedestrian flow prediction for the time to be predicted. Compared with the existing data that must obtain long-term historical data, the embodiment of the present application can reduce the cost of data acquisition.

[0106] In the embodiment of the present application, the time distribution information of the historical flow of people in the target time period reflects the discrete sequence of the change of the flow of people over time in the preset time period of each day in the target time period. For example, the time distribution information of the historical flow of people can be expressed as A d,t , d∈{1,2,…,M}, t∈{1,2,…,T}, where d represents the day of the target time period, M can be set according to the actual scenario, and for example, M can be less than or equal to 5; t represents the hourly time of the preset time period of each day in the target time period. For example, assuming that the preset time period is from 7 am to 7 pm, then 7 am corresponds to the first hourly time, and 12 noon corresponds to the sixth hourly time; A d,t It indicates the flow of people in the area to be predicted at the tth hour within the preset period of the dth day of the target time period.

[0107] In the embodiment of the present application, the historical traffic information of each day in the target time period reflects the discrete sequence of the change of the flow of people over time in the preset time period of each day in the target time period. For example, the historical traffic information can be expressed as C j,d,t , d∈{1,2,…,M}, j∈{1,2,…,J}, where j corresponds to the jth transportation equipment in the scenic area, and J is the total number of transportation equipment in the scenic area; the meanings of d and t are the same as those in A above. d,t d and t in are the same and will not be repeated here; j,d,t It represents the passenger flow carried by the j-th transportation device at the t-th hour on the d-th day.

[0108] It should be understood that when a certain transportation device at a certain time carries tourists away from the scenic spot, the corresponding flow of people C at that time j,d,t Is a negative value.

[0109] S302, fitting the daily historical passenger flow time distribution information respectively to obtain the daily preset second relationship formula; and determining the daily fitting parameters respectively according to the daily preset second relationship formula.

[0110] In the embodiment of the present application, the preset second relational expression can be understood as a fitting curve of the historical human flow time distribution information. For example, the historical human flow time distribution information can be expressed as A d,t , respectively fit the daily historical flow time distribution information, and obtain the preset second relationship for each day

[0111]

[0112] Wherein, t0 is each moment of the preset time period, P is the polynomial order, and P can be 5 or 6; a i are the fitting parameters of each order, A d,t The corresponding fitted values.

[0113] For each day in the target time period, the preset second relationship is used to further determine the fitting parameters corresponding to each day. For example, the fitting parameters corresponding to each day satisfy the formula:

[0114]

[0115]

[0116] At this point, the fitting parameters a of each order corresponding to each day in the target time period are obtained. i .

[0117] It can be understood that each day in the target time period corresponds to the preset second relational expression Different fitting parameters are also different. For example, if the target time period is 2 days, there are two sets of fitting parameters corresponding to the target time period.

[0118] S303, determining the historical traffic flow data of each traffic equipment in the scenic area at each time of the day according to the daily historical traffic information; determining the preset first relationship according to the daily fitting parameters and the historical traffic flow data of each traffic equipment in the scenic area at each time of the day.

[0119] The historical traffic flow data of each traffic equipment in the scenic area at each time is respectively the sum of the historical traffic information of each traffic equipment before each time in the preset period.

[0120] For example, assuming that there are three cableways in the scenic area, the historical traffic information of the three cableways can be represented as C 1,d,t , C 2,d,t , C 3,d,t For example, assuming that the preset time period is from 7 am to 7 pm, at 9 am (corresponding to t=3) on each day of the target time period (e.g., the first day of the target time period), the historical traffic flow data of cableway 1 is C 1,1,1 , C 1,1,2 , C 1,1,3 The historical traffic flow data of cableway 2 is C 2,1,1 , C 2,1,2 , C 2,1,3 The historical traffic flow data of cableway 3 is C 3,1,1 , C 3,1,2 , C 3,1,3 sum.

[0121] Assuming that the target time period includes M days, use the least squares method to make the fitting parameter a for the M days i The historical traffic information of each traffic device at time t in the M days is regressed to obtain the traffic flow data coefficient corresponding to time t in the target time period and store it in the server. Assuming that the preset time period is 7 am to 7 pm, 13 traffic flow data coefficients from 7 am to 7 pm can be obtained. When the expression of the time distribution curve of the flow of people at the time to be predicted is determined later, the traffic flow data system corresponding to the corresponding time can be obtained. If the target time is 9 o'clock, the server can obtain and combine the traffic flow data coefficient corresponding to 9 o'clock to determine the expression of the time distribution curve of the flow of people at the time to be predicted.

[0122] For example, the fitting parameters of each day in the target time period and the historical traffic flow data of each traffic equipment in the scenic area at a certain time every day meet the following conditions:

[0123]

[0124] Among them, f jis the jth traffic flow data, c ij f j The corresponding traffic flow data coefficient. Based on the historical passenger flow time distribution information of M days, the least squares method is used to make a i f j The regression of the traffic flow data coefficient c corresponding to the moment is obtained. ij ′ . ij Substitute into the above equation Get the preset first relation

[0125] S304, obtaining traffic flow data of each transportation equipment in the scenic area at the target time; wherein the traffic flow data is the total flow of people entering the scenic area through the transportation equipment at each time within a preset time period and before the target time, and the target time is determined based on the actual predicted time.

[0126] This step is similar or identical to the above step S201 and will not be described in detail here.

[0127] S305. Input the traffic flow data of each traffic equipment and the traffic flow data coefficient corresponding to the target time into the preset first relationship formula to obtain the target fitting parameters corresponding to the time to be predicted; input the target fitting parameters corresponding to the time to be predicted into the preset second relationship formula to obtain the expression of the time distribution curve of the passenger flow at the time to be predicted.

[0128] For example, assuming that the target time is 10 a.m., the traffic flow data coefficient corresponding to 10 a.m. and the traffic flow data of each traffic equipment in the scenic area at 10 a.m. are input into the preset first relational expression to obtain a set of fitting parameters, which are the target fitting parameters. The target fitting parameters are the fitting parameters of each order in the expression of the time distribution curve of the flow of people at the predicted time determined at 10 a.m. The target fitting parameters can be updated based on the next corresponding traffic flow data coefficient and the traffic flow data of each traffic equipment in the scenic area at the next target time.

[0129] For example, the preset second relational expression can be

[0130]

[0131] in, and t0 are variables, then the expression of the time distribution curve of the flow of people at the time to be predicted can be

[0132]

[0133] Among them, a i ′ is the target fitting parameter.

[0134] In the embodiment of the present application, the expression of the time distribution curve of the flow of people at the time to be predicted can be determined only based on the traffic flow data of each traffic equipment and the traffic flow data coefficient corresponding to the target time and the predetermined preset first relationship and preset second relationship. The calculation is simple, which facilitates the subsequent implementation of the flow of people control at different times according to the expression of the time distribution curve of the flow of people, thereby improving the accuracy of the flow of people control.

[0135] S306. Obtain target traffic information of each traffic device. The target traffic information is the information obtained by translating the source traffic information on the time axis in the direction of increasing time by L hours. The source traffic information is the passenger flow carried by the traffic device at each moment before the target moment within a preset time period, and L is an integer greater than 0. Determine the passenger flow prediction value of the to-be-predicted area corresponding to each moment within the to-be-predicted time according to the target traffic information of each traffic device and the expression of the passenger flow time distribution curve.

[0136] The translation time L can be set according to the actual scenario. In order to make the final pedestrian flow prediction value of the area to be predicted more accurate, L can be 3.

[0137] In a possible implementation, after the source traffic information is translated L hours in the direction of increasing time on the time axis, the empty part in front is filled with 0.

[0138] For example, for any traffic device, assuming the target time is 9 o'clock and the preset time period is from 7 am to 7 pm, the source traffic information includes the passenger flow K1 carried by the traffic device at 7 o'clock, the passenger flow K2 carried at 8 o'clock, and the passenger flow K3 carried at 9 o'clock. Assuming L is 3, the target traffic information corresponding to the source traffic information can include the passenger flow at 6 times from 7 o'clock to 12 o'clock, which are 0, 0, 0, K1, K2, and K3 respectively.

[0139] Furthermore, in a possible implementation, the target traffic information of each traffic device and the expression of the pedestrian flow time distribution curve are combined with different weights to determine the pedestrian flow prediction value of the to-be-predicted area corresponding to each moment within the to-be-predicted time.

[0140] In an embodiment of the present application, the peak change of passenger flow is simulated by translating the passenger flow carried by traffic equipment at each moment before a target moment within a preset time period on a time axis, and the target traffic information obtained after the translation is combined with the expression of the passenger flow time distribution curve to determine the passenger flow prediction value of the to-be-predicted area corresponding to each moment within the to-be-predicted time, so as to further improve the accuracy of the passenger flow prediction value of the to-be-predicted area corresponding to each moment, thereby improving the passenger flow control accuracy of the to-be-predicted area of ​​the scenic spot.

[0141] In a possible implementation, the above steps may include determining the predicted value of the passenger flow in the area to be predicted corresponding to each moment within the predicted time according to the target traffic information of each traffic device and the expression of the passenger flow time distribution curve, which may include:

[0142] Based on the first preset weight, the target traffic information of each traffic equipment is weighted averaged to obtain a first weighted average result; based on the second preset weight, the first weighted average result and the expression of the pedestrian flow time distribution curve are weighted averaged to obtain a second weighted average result; based on the second weighted average result, the pedestrian flow prediction value of the to-be-predicted area corresponding to each moment within the to-be-predicted time is determined.

[0143] In the embodiment of the present application, the first preset weight and the second preset weight can be set according to the actual scenario. The first preset weight may include at least one first preset sub-weight, and the number of the first preset sub-weights is the same as the number of traffic equipment. For example, assuming that the traffic equipment of a scenic spot is 3 cableways, in order to make the predicted value of the flow of people in the predicted area corresponding to each moment within the predicted time more accurate, the first preset sub-weights corresponding to the three cableways are 0.1, 0.8, and 0.1, respectively, that is, the three cableways are weighted averaged based on the first preset sub-weights 0.1, 0.8, and 0.1 to obtain the first weighted average result. The larger the first preset sub-weight corresponding to the cableway, the greater the impact of the traffic flow data of the cableway on the flow of people in the predicted area. The second preset weight may include two second preset sub-weights, and the two second preset sub-weights are the weights of the first weighted average result and the expression of the time distribution curve of the flow of people, respectively. Exemplarily, the second preset sub-weight of the first weighted average result can be 0.8, and the second preset sub-weight of the expression of the time distribution curve of the flow of people can be 0.2, that is, the first weighted average result and the expression of the time distribution curve of the flow of people are weighted averaged based on the second preset sub-weights of 0.8 and 0.2 to obtain the second weighted average result. The second weighted average result introduces the target traffic information, so the second weighted average result can be understood as a more accurate expression of the time distribution curve of the flow of people, so that the predicted value of the flow of people in the area to be predicted corresponding to each moment in the time to be predicted determined according to the second weighted average result is also more accurate, thereby improving the accuracy of the control of the flow of people.

[0144] S307: When the predicted value of passenger flow reaches a preset threshold, reduce the operating speed of the traffic equipment.

[0145] Among them, the preset threshold can be set according to the actual scenario.

[0146] For example, when the server predicts that the flow of people in the predicted area will reach a preset threshold at 12 o'clock, the server can control the transportation equipment carrying tourists into the scenic area to reduce the running speed to slow down the speed at which tourists enter the predicted area, thereby achieving the purpose of controlling the flow of people.

[0147] In the embodiment of the present application, the traffic flow data of the traffic equipment and other relevant preset relationships are fully considered, and the target traffic information is introduced to obtain the second weighted average result, so as to obtain more accurate pedestrian flow prediction values ​​of the to-be-predicted area corresponding to each moment within the to-be-predicted time, and then the operating speed of the traffic equipment is controlled based on the pedestrian flow prediction values ​​of the to-be-predicted area corresponding to each moment, so as to achieve the purpose of controlling the pedestrian flow. Compared with the general control of pedestrian flow based on the total pedestrian flow prediction value of one day, the embodiment of the present application has higher accuracy in controlling pedestrian flow based on the detailed pedestrian flow prediction values ​​of the to-be-predicted area corresponding to each moment.

[0148] The accuracy of the prediction results of the embodiments of the present application is exemplified below based on the crowd flow prediction results of the to-be-predicted area of ​​a scenic spot.

[0149] For example, Figure 4a The figure shows the result of the prediction of the flow of people in a scenic area to be predicted provided by the embodiment of the present application. Figure 1 . Figure 4b The figure shows the result of the prediction of the flow of people in a scenic area to be predicted provided by the embodiment of the present application. Figure 2 . Figure 4a The area to be predicted in is the hot spot area 1 of the scenic spot. Figure 4b The area to be predicted in is the hot spot area 2 of the scenic spot. Figure 4a and 4b As shown, the scenic area includes three cableways: A, B, and C. Figure 4a and 4b The x-axis of the curve graph is the time in the preset period (7 am to 7 pm), and the y-axis is the flow of people in the hot spot area corresponding to each time. Figure 4a The curve graph corresponding to the hot spot area 1 at 8 o'clock on April 4 is used as an example for explanation. The curve graph includes the real time distribution of passenger flow from 7 am to 7 pm on April 4, and the passenger flow time distribution result from 7 am to 7 pm on April 4 predicted by the method of the embodiment of the present application using the traffic flow data corresponding to the three cableways A, B, and C from 7 am to 8 am on April 4; Figure 4b The curve graph corresponding to the hot spot area 2 at 9 o'clock on April 5 is used as an example to illustrate. The curve graph includes the actual time distribution of passenger flow from 7 am to 7 pm on April 5, and the passenger flow time distribution results from 7 am to 7 pm on April 5 predicted by using the traffic flow data corresponding to the three cableways A, B, and C from 7 am to 9 o'clock on April 5; Figure 4aThe curve graph corresponding to the hot spot area 1 at 10 o'clock on April 6 is used as an example for explanation. The curve graph includes the actual time distribution of passenger flow from 7 am to 7 pm on April 6, and the time distribution of passenger flow from 7 am to 7 pm on April 6 obtained by using the traffic flow data corresponding to the three cableways A, B, and C from 7 am to 10 o'clock on April 6. The other curve graphs are similar and will not be repeated here.

[0150] The error of the predicted value of the flow of people relative to the actual value of the flow of people can satisfy the error formula:

[0151]

[0152] Among them, y real , t is the actual value of the flow of people in the hot spot area at time t, y pred , t is the predicted value of the flow of people corresponding to the hot spot area at time t, and Error is the error of the predicted value of the flow of people relative to the actual value of the flow of people.

[0153] Based on the above error formula and the above Figure 4a and Figure 4b The curve graph in Figure 4a and Figure 4b The error values ​​of each passenger flow prediction result are shown in Table 1.

[0154] Table 1

[0155]

[0156] It can be seen from Table 1 that the error of the predicted value of the flow of people in the embodiment of the present application can be as low as about 0.4, and Figure 4a and Figure 4b It can be seen that the predicted peak flow period and the actual peak flow period are also very close.

[0157] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0158] It should be further noted that, although the various steps in the flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0159] Figure 5 This is a schematic diagram of the structure of a scenic spot crowd flow control device provided in an embodiment of the present application. Figure 5 The device 50 includes an acquisition module 501, a first determination module 502, a second determination module 503 and a control module 504, wherein:

[0160] The acquisition module 501 is used to acquire the traffic flow data of each traffic equipment in the scenic area at the target time; wherein the traffic flow data is the total flow of people entering the scenic area through the traffic equipment at each time before the target time within a preset period, and the target time is determined according to the actual predicted time;

[0161] The first determination module 502 is used to determine the expression of the time distribution curve of the flow of people at the time to be predicted according to the preset first relationship, the traffic flow data of each traffic device, the traffic flow data coefficient corresponding to the target time, and the preset second relationship; wherein the preset first relationship represents the correlation between the fitting parameters and the traffic flow data of each traffic device, the traffic flow data coefficient corresponding to each moment in the preset time period is predetermined, and the preset second relationship is used to represent the correlation between each moment in the preset time period and the flow of people in the area to be predicted;

[0162] The second determination module 503 is used to determine the predicted value of the flow of people in the area to be predicted corresponding to each moment in the predicted time based on the expression of the time distribution curve of the flow of people;

[0163] The control module 504 is used to control the flow of people according to the predicted value of the flow of people in the predicted area corresponding to each moment within the predicted time.

[0164] In a possible implementation, the first determining module 502 is specifically configured to:

[0165] Input the traffic flow data of each traffic device and the traffic flow data coefficient corresponding to the target time into a preset first relationship formula to obtain the target fitting parameter corresponding to the time to be predicted;

[0166] The target fitting parameters corresponding to the time to be predicted are input into the preset second relational expression to obtain the expression of the time distribution curve of the passenger flow at the time to be predicted.

[0167] In a possible implementation, the second determining module 503 is specifically configured to:

[0168] Obtain target traffic information of each traffic device, where the target traffic information is information obtained by translating the source traffic information on the time axis in the direction of increasing time by L hours, and the source traffic information is the flow of people carried by the traffic device at each time before the target time within a preset time period, and L is an integer greater than 0;

[0169] According to the target traffic information of each traffic equipment and the expression of the time distribution curve of the passenger flow, the predicted value of the passenger flow in the area to be predicted corresponding to each moment within the predicted time is determined.

[0170] In one possible implementation, L is 3.

[0171] In a possible implementation, the second determining module 503 is specifically configured to:

[0172] Performing weighted averaging on the target traffic information of each traffic device based on a first preset weight to obtain a first weighted average result;

[0173] Performing weighted averaging on the first weighted average result and the pedestrian flow time distribution curve expression based on a second preset weight to obtain a second weighted average result;

[0174] The predicted value of the flow of people in the area to be predicted corresponding to each moment within the time to be predicted is determined according to the second weighted average result.

[0175] In a possible implementation, the device further includes:

[0176] The acquisition module is used to obtain the historical time distribution information of the flow of people and the historical traffic information of the area to be predicted in the scenic area every day within the target time period; wherein the target time period is determined according to the time to be predicted, the historical flow of people time distribution information includes each moment in the preset time period and the flow of people corresponding to each moment, and the historical traffic information is the flow of people carried by each traffic equipment at each moment;

[0177] A fitting module is used to fit the daily historical passenger flow time distribution information to obtain a preset second relationship formula for each day; and to determine the fitting parameters for each day according to the preset second relationship formula for each day;

[0178] The third determination module is used to determine the historical traffic flow data of each traffic equipment in the scenic area at each time of the day according to the historical traffic information of each day;

[0179] The fourth determination module is used to determine the preset first relationship according to the fitting parameters of each day and the historical traffic flow data of each traffic equipment in the scenic area at each time of each day.

[0180] In a possible implementation, the control module 504 is specifically configured to:

[0181] When the predicted passenger flow value reaches a preset threshold, the operating speed of the traffic equipment is reduced.

[0182] The scenic spot crowd flow control device provided in the embodiment of the present application can be used to execute the above-mentioned method embodiment. Its implementation principle and technical effect are similar, and the embodiment of the present application will not be repeated here.

[0183] It should be understood that the above-mentioned device embodiments are only illustrative, and the device of the present application can also be implemented in other ways. For example, the division of units / modules in the above-mentioned embodiments is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0184] An embodiment of the present application provides an electronic device, Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0185] like Figure 6 As shown, the electronic device 60 includes: a processor 601 and a memory 602; the memory 602 stores computer-executable instructions; the processor 601 executes the computer-executable instructions stored in the memory 602, so that the electronic device 60 executes the above method.

[0186] When the memory 602 is independently provided, the electronic device 60 further includes a bus 603 for connecting the memory 602 and the processor 601 .

[0187] Figure 6 The electronic device 60 shown in the embodiment can execute the steps in the above method embodiment, and its implementation principles and beneficial effects are similar, which will not be described in detail here.

[0188] The embodiment of the present application provides a chip. The chip includes a processor, and the processor is used to call a computer program in a memory to execute the technical solution in the above embodiment. Its implementation principle and technical effect are similar to those of the above related embodiments, and will not be repeated here.

[0189] The embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program. The above method is implemented when the computer program is executed by the processor. The method described in the above embodiment can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. If implemented in software, the function can be stored as one or more instructions or codes on a computer-readable medium or transmitted on a computer-readable medium. Computer-readable media can include computer storage media and communication media, and can also include any medium that can transfer a computer program from one place to another. The storage medium can be any target medium that can be accessed by a computer.

[0190] In one possible implementation, a computer readable medium may include RAM, ROM, compact disc read-only memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that is intended to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer. Moreover, any connection is appropriately referred to as a computer readable medium. For example, if the software is transmitted from a website, server or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wireless technology (such as infrared, radio and microwave), the coaxial cable, fiber optic cable, twisted pair, DSL or wireless technology such as infrared, radio and microwave are included in the definition of medium. Disk and optical disk as used herein include optical disk, laser disk, optical disk, digital versatile disk (DVD), floppy disk and Blu-ray disk, where disks usually reproduce data magnetically, while optical disks reproduce data optically using lasers. Combinations of the above should also be included in the scope of computer readable media.

[0191] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed, the computer executes the above method.

[0192] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable device to generate a machine, so that the instructions executed by the processing unit of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0193] The above specific implementation methods further illustrate the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling the flow of people in a scenic area, characterized in that: The method comprises: Obtaining traffic flow data of each traffic equipment in the scenic area at the target time; wherein the traffic flow data is the total flow of people entering the scenic area through the traffic equipment at each time before the target time within a preset period, and the target time is determined according to the actual predicted time; According to the preset first relational expression, the traffic flow data of each traffic device, the traffic flow data coefficient corresponding to the target time and the preset second relational expression, the expression of the time distribution curve of the flow of people at the time to be predicted is determined; wherein the preset first relational expression represents the correlation between the fitting parameters and the traffic flow data of each traffic device, the traffic flow data coefficient corresponding to each moment in the preset time period is predetermined, and the preset second relational expression is used to represent the correlation between each moment in the preset time period and the flow of people in the area to be predicted; Determine the predicted value of the flow of people in the area to be predicted corresponding to each moment in the time to be predicted based on the expression of the flow of people time distribution curve; Crowd flow control is performed according to the crowd flow prediction value of the to-be-predicted area corresponding to each moment within the to-be-predicted time.

2. The method according to claim 1, characterized in that: The method of determining the pedestrian flow time distribution curve expression for the predicted time according to the preset first relationship, the traffic flow data of each traffic equipment, the traffic flow data coefficient corresponding to the target time, and the preset second relationship includes: Input the traffic flow data of each traffic device and the traffic flow data coefficient corresponding to the target time into the preset first relationship to obtain the target fitting parameter corresponding to the time to be predicted; The target fitting parameter corresponding to the time to be predicted is input into the preset second relational expression to obtain the expression of the time distribution curve of the passenger flow at the time to be predicted.

3. The method according to claim 2, characterized in that The method of determining the predicted value of the flow of people in the area to be predicted corresponding to each moment in the time to be predicted based on the flow of people time distribution curve expression includes: Obtaining target traffic information of each traffic device, wherein the target traffic information is information obtained by translating the source traffic information on the time axis in a direction of increasing time by L hours, the source traffic information is the flow of people carried by the traffic device at each time before the target time within the preset time period, and L is an integer greater than 0; Determine the predicted value of the passenger flow in the area to be predicted corresponding to each moment within the predicted time according to the target traffic information of each traffic device and the expression of the passenger flow time distribution curve.

4. The method according to claim 3, characterized in that The L is 3.

5. The method according to claim 4, characterized in that The method of determining the predicted value of the flow of people in the area to be predicted corresponding to each moment in the predicted time according to the target traffic information of each traffic device and the expression of the time distribution curve of the flow of people includes: Performing weighted averaging on the target traffic information of each traffic device based on a first preset weight to obtain a first weighted average result; Performing weighted averaging on the first weighted average result and the pedestrian flow time distribution curve expression based on a second preset weight to obtain a second weighted average result; The pedestrian flow prediction value of the to-be-predicted area corresponding to each moment within the to-be-predicted time is determined according to the second weighted average result.

6. The method according to any one of claims 1 to 5, characterized in that Before determining the pedestrian flow time distribution curve expression for the predicted time according to the preset first relationship, the traffic flow data of each traffic equipment, the traffic flow data coefficient corresponding to the target time and the preset second relationship, the method further includes: Obtaining the historical time distribution information of the flow of people and the historical traffic information of the area to be predicted in the scenic area every day within the target time period; wherein the target time period is determined according to the time to be predicted, the historical time distribution information of the flow of people includes each moment within the preset time period and the flow of people corresponding to each moment, and the historical traffic information is the flow of people carried by each traffic equipment at each moment; Fitting the daily historical flow of people time distribution information respectively to obtain a preset second relationship expression for each day; determining the fitting parameters for each day according to the preset second relationship expression for each day; Determine the historical traffic flow data of each traffic equipment in the scenic area at each time of the day according to the historical traffic information of each day; The preset first relationship is determined according to the fitting parameters of each day and the historical traffic flow data of each traffic equipment in the scenic area at each time of each day.

7. The method according to claim 1, characterized in that The controlling of the flow of people according to the predicted value of the flow of people in the area to be predicted corresponding to each moment within the time to be predicted comprises: When the predicted value of the passenger flow reaches a preset threshold, the operating speed of the traffic equipment is reduced.

8. A scenic spot crowd flow control device, characterized in that: include: an acquisition module, a first determination module, a second determination module and a control module, wherein: The acquisition module is used to acquire the traffic flow data of each traffic equipment in the scenic area at the target time; wherein the traffic flow data is the total flow of people entering the scenic area through the traffic equipment at each time before the target time within a preset period, and the target time is determined according to the actual predicted time; The first determination module is used to determine the expression of the time distribution curve of the flow of people at the time to be predicted according to the preset first relationship, the traffic flow data of each traffic device, the traffic flow data coefficient corresponding to the target time and the preset second relationship; wherein the preset first relationship characterizes the correlation between the fitting parameters and the traffic flow data of each traffic device, the traffic flow data coefficient corresponding to each moment in the preset time period is predetermined, and the preset second relationship is used to characterize the correlation between each moment in the preset time period and the flow of people in the area to be predicted; The second determination module is used to determine the predicted value of the flow of people in the area to be predicted corresponding to each moment in the time to be predicted based on the expression of the time distribution curve of the flow of people; The control module is used to control the flow of people according to the predicted value of the flow of people in the predicted area corresponding to each moment within the predicted time.

9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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