CN111126679A - Open scenic spot passenger flow statistics and prediction method and system - Google Patents
Open scenic spot passenger flow statistics and prediction method and system Download PDFInfo
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Abstract
The invention discloses a method for counting and predicting passenger flow in an open scenic spot, which comprises the steps of collecting security inspection gate data and mobile phone base station data of the open scenic spot; processing security check gate data and mobile phone base station data to obtain open scenic spot passenger flow; taking the open scenic spot passenger flow volume and the corresponding time point as historical data, and constructing a scenic spot passenger flow prediction model; bringing the future preset time point into a scenic spot passenger flow prediction model, and predicting the future preset time point open scenic spot passenger flow; and visually displaying the passenger flow of the scenic spot. The invention collects the security check gate data and the mobile phone base station data in the scenic spot, is not influenced by the factors of wide scenic spot people flow target identification area, people flow trend, mixture of different types of targets such as people and vehicles and the like, and obtains more accurate passenger flow. The invention uses the scenic spot passenger flow volume and the corresponding time point as historical data to construct a scenic spot passenger flow prediction model, and solves the problems that the existing open scenic spot passenger flow volume is uncontrollable and safety accidents are easily caused.
Description
Technical Field
The invention relates to the field of travel management, in particular to a method and a system for counting and predicting passenger flow in an open scenic spot.
Background
Conventionally, the traditional tourist attraction collects entrance tickets, has high internal expense and is subject to scaling, most of tourists in the paid tourist attraction only go once, and the playing experience and income of the tourists are influenced. Therefore, the open scenic spot like the Hangzhou West lake and the Wuhan east lake green channel is more and more popular with tourists, and for the open scenic spot, the tourists are not required to collect tickets at a specific place and closed to force the tourists to shop, so that more space is selected for the tourists, the consumption mind of the tourists is relaxed, and the shopping willingness of the tourists is stronger. Open scenic spots not only increase more playing experiences for tourists, but also increase the income of scenic spots.
However, due to the factors of a plurality of entrances and exits of the scenic spot, a wide pedestrian flow target identification area, uncontrollable pedestrian flow trend, mixed different types of targets such as people, vehicles and the like, the open scenic spot always has the problem of low accuracy in the statistics of the passenger flow volume of the scenic spot; in addition, the number of entrances and exits of the open scenic spots is too many, and the passenger flow volume of some scenic spots is greatly different in different seasons, so that the problems of uncontrollable scenic spot passenger flow volume and easy safety accidents are caused, therefore, the passenger flow volume of the open scenic spots needs to be predicted, and early warning is carried out in time when the passenger flow volume of the scenic spots is too much.
Disclosure of Invention
In view of the above, the present invention has been developed to provide a method and system for open scenic spot traffic statistics and prediction that overcomes, or at least partially addresses, the above-mentioned problems.
In order to solve the technical problem, the embodiment of the application discloses the following technical scheme:
a method of open scenic spot passenger flow statistics and prediction, comprising:
collecting data of front-end equipment in an open scenic spot; the front-end equipment data comprises security check gate data and mobile phone base station data;
processing the security check gate data and the mobile phone base station data to obtain the passenger flow in the open scenic spot;
taking the open scenic spot passenger flow volume and the corresponding time point as historical data, and constructing a scenic spot passenger flow prediction model;
bringing the future preset time point into a prediction model to predict the open scenic spot passenger flow at the future preset time point;
and visually displaying the passenger flow of the open scenic spot.
Correspondingly, on the other hand, the invention also discloses an open scenic spot passenger flow statistics and prediction system, which comprises: the system comprises a front-end equipment data acquisition module, a data processing module, a passenger flow prediction module and a visual display module; wherein the content of the first and second substances,
the output end of the front-end equipment data acquisition module is connected with the data processing module, acquires front-end equipment data and sends the front-end equipment data to the data processing module;
the input end of the data processing module is connected with the front-end equipment data acquisition module, the output end of the data processing module is connected with the passenger flow volume prediction module, the data processing module receives and processes the front-end equipment data to obtain the passenger flow volume of the open scenic spot, and the passenger flow volume and the corresponding time point are sent to the passenger flow volume prediction module;
the passenger flow volume prediction module has an input end connected with the data processing module and an output end connected with the visual display module, receives the passenger flow volume and the corresponding time, takes the open scenic spot time point as the input of the multiple linear regression model, takes the passenger flow volume of the open scenic spot corresponding to the time point as the output of the multiple linear regression model, and constructs the multiple linear regression model of the open scenic spot time point and the open scenic spot passenger flow volume; the system is also used for receiving a future preset time point instruction, and inputting the preset time point as an open scenic spot time point and an open scenic spot passenger flow multiple linear regression model to obtain the future preset time point open scenic spot passenger flow;
the input end of the visual display module is connected with the passenger flow prediction module, receives the future preset time point open scenic spot passenger flow transmitted by the passenger flow prediction module, and visually displays the passenger flow.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least: the invention collects the security inspection gate data and the mobile phone base station data in the scenic spot and processes the data to obtain the passenger flow in the open scenic spot. The data is not influenced by factors such as wide pedestrian flow target identification area, uncontrollable pedestrian flow trend, mixture of different targets such as people and vehicles and the like in the open scenic spot, and the obtained passenger flow volume of the open scenic spot is more accurate. In addition, the invention uses the open scenic spot passenger flow volume and the corresponding time point as historical data to construct a scenic spot passenger flow prediction model; the future preset time point is brought into the prediction model to predict the future preset time point open scenic spot passenger flow, and the problems that the existing open scenic spot passenger flow is uncontrollable and safety accidents are easily caused are solved.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an open scenic spot passenger flow statistics and prediction method according to an embodiment of the present invention;
fig. 2 is a structural diagram of an open scenic spot passenger flow statistics and prediction system in the second embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to solve the problems that the open scenic spot passenger flow volume statistics is inaccurate and the scenic spot passenger flow volume is not predicted in the prior art, the embodiment of the invention provides a method and a system for counting and predicting the open scenic spot passenger flow volume.
Example one
As shown in fig. 1, the present invention also discloses a method for statistics and prediction of passenger flow in an open scenic spot, which comprises:
s100, collecting data of front-end equipment in an open scenic spot; the front-end equipment data comprises security check gate data and mobile phone base station data. Specifically, when the tourist swipes the identity card through the security check gate, the security check gate acquires the identity card information of the tourist and is networked with relevant departments to obtain at least information of the name, the gender, the telephone number, the origin and the like of the tourist; when the mobile phone of the tourist communicates with the nearby base station, the identity information of the tourist, which is obtained by the relevant operator, at least comprises the information of the name, the sex, the telephone number, the source and the like of the tourist.
In some preferred embodiments, the front-end device further includes a scenic spot face recognition camera, and the face features of the tourist are collected by the face recognition camera and matched with the face database to obtain the tourist identity information, where the tourist information at least includes information such as the name, sex, telephone number, and origin of the tourist.
In some preferred embodiments, before performing step S100, the front-end device of the scenic spot may further perform area division, so that the front-end device collects all areas of the open scenic spot. It can be understood that the open scenic spot is generally large in area, and if the front-end device only collects the passenger flow data within a certain small range of the scenic spot, the collected passenger flow data is obviously large in error, so that the front-end device is divided into regions at first, all the front-end device collecting regions cover the whole scenic spot, and the passenger flow data collecting accuracy is higher.
S200, security check gate data and mobile phone base station data are processed to obtain passenger flow in the open scenic spot.
Specifically, the method for processing security gate data and mobile phone base station data to obtain the passenger flow volume in the open scenic spot comprises the following steps: establishing digital identities for each visitor for identity recognition in the scenic spot by utilizing the visitor identity information acquired by the security check gate; similarly, the digital identity for identity recognition in the scenic spot is established for each tourist by utilizing the tourist information acquired by the mobile phone base station; and comparing the security check gate data with the mobile phone base station data, taking the name, the source, the identification number, the gender and the mobile phone number of the tourist as judgment conditions, removing repeated data, combining the information of the tourist, establishing a unique digital identity for each tourist, and forming a tourist track, wherein the tourist track comprises the tourist park entrance time, the tourist exit time, the tourist playing area, the regional staying time and the like.
S300, taking the open scenic spot passenger flow volume and the corresponding time point as historical data, and constructing a scenic spot passenger flow prediction model; specifically, the open scenic spot time points are used as the input of the multiple linear regression model, the open scenic spot passenger flow corresponding to the scenic spot time points is used as the output of the multiple linear regression model, and the multiple linear regression model corresponding to the open scenic spot time points and the open scenic spot passenger flow is constructed.
For example, the preset multiple linear regression model is y β 0+ β 1x1, where y is historical open-scene traffic data, x1 is time corresponding to historical scene traffic, the open-scene time and the corresponding traffic are taken into the model, the multiple linear regression model parameter set { β 0, β 1} corresponding to the historical temperature data of the isolating switch is determined, and when β 0, β 1 parameters are determined, the multiple linear regression model corresponding to the open-scene time and the open-scene traffic is determined.
In some embodiments, the scenic spot open time period, the guest playing time, and the sunset time may also be used as inputs of a multiple linear regression model, and the open scenic spot passenger flow corresponding to the time may be used as an output of the multiple linear regression model to construct the multiple linear regression model.
For example, the preset multiple linear regression model is y, β 0+ β 1x1+ β 02x2+ β 13x3+ β 24x4, wherein y is historical open-scene passenger flow data, x1 is a corresponding time point of historical scene passenger flow, x2 is open-scene time, x3 is guest playing time, and x4 is sunset time, the multiple times and the corresponding passenger flow are taken into the model, the multiple linear regression model parameter set { β 30, β 41, β 2, β 3, β 4} corresponding to historical temperature data of the isolating switch is determined, and when β 0, β 1, β 2, β 3, β 4 parameters are determined, the multiple linear regression model corresponding to the open-scene time point and the open-scene passenger flow is determined.
S400, the future preset time point is substituted into the multivariate linear regression model determined by the parameters of S300, and the open scenic spot passenger flow of the future preset time point is predicted.
For example, when the future preset point x1 generation multiple linear regression model y is β 0+ β 1x1, and the scenic spot passenger volume y1. corresponding to the future preset time is obtained, and the scenic spot open time, the guest playing time, and the sunset time are used as the input of the multiple linear regression model, the prediction method is the same as the above method, and details are not repeated again.
S500, visually displaying the passenger flow of the open scenic spot. In some embodiments, the way of visually displaying the open scenic spot passenger flow is a GIS thermodynamic diagram or/and a passenger flow line diagram.
When the visual display mode is a GIS thermodynamic diagram, the map of the scenic spot is divided by utilizing longitude and latitude, the position information of the front-end equipment is imported into the GIS map, and the passenger flow data collected by each front-end equipment of the current scenic spot and the passenger flow prediction data of the future preset time are received. A GIS map is prestored with a plurality of groups of people flow threshold values, and different threshold values correspond to different visual colors. For example, when there are 3 sets of people flow threshold values, 50, 100, and 200 respectively, and the corresponding colors are dark yellow, light red, and dark red, if the first set of data people flow information is 210 people, the first set of thermodynamic diagrams is dark red, and the second set of data people flow information is 120 people, the first set of thermodynamic diagrams is light red, and the third set of thermodynamic diagrams is dark yellow when the 3 rd set of data people flow information is 60 people. And (4) carrying the position data and the pedestrian flow data of the front-end equipment into a GIS map to obtain the pedestrian flow thermodynamic diagram. It can be understood that the GIS map can display the current scenic spot passenger flow volume by receiving the current passenger flow volume data, receive the future preset time point passenger flow volume data obtained by the scenic spot passenger flow volume prediction model calculation, and display the future preset time point passenger flow volume. The preset threshold and the corresponding color can be customized according to the requirement, which is not required in this embodiment.
The method comprises the steps of collecting security inspection gate data and mobile phone base station data in a scenic spot, and processing the data to obtain the passenger flow in the open scenic spot. The data is not influenced by factors such as wide pedestrian flow target identification area, uncontrollable pedestrian flow trend, mixture of different targets such as people and vehicles and the like in the open scenic spot, and the obtained passenger flow volume of the open scenic spot is more accurate. In addition, the invention uses the open scenic spot passenger flow volume and the corresponding time point as historical data to construct a scenic spot passenger flow prediction model; the future preset time point is brought into the prediction model to predict the future preset time point open scenic spot passenger flow, and the problems that the existing open scenic spot passenger flow is uncontrollable and safety accidents are easily caused are solved.
Example two
The invention also discloses an open scenic spot passenger flow statistics and prediction system, which comprises: the system comprises a front-end equipment data acquisition module 1, a data processing module 2, a passenger flow prediction module 3 and a visual display module 4; wherein the content of the first and second substances,
the output end of the front-end equipment data acquisition module 1 is connected with the data processing module 2, acquires front-end equipment data and sends the front-end equipment data to the data processing module 2; the front-end equipment data acquired by the front-end equipment data acquisition module 1 comprise security check gate data and mobile phone base station data. Specifically, when the tourist swipes the identity card through the security check gate, the security check gate acquires the identity card information of the tourist and is networked with relevant departments to obtain at least information of the name, the gender, the telephone number, the origin and the like of the tourist; when the mobile phone of the tourist communicates with the nearby base station, the identity information of the tourist, which is obtained by the relevant operator, at least comprises the information of the name, the sex, the telephone number, the source and the like of the tourist.
The input end of the data processing module 2 is connected with the front-end equipment data acquisition module 1, the output end of the data processing module is connected with the passenger flow volume prediction module 3, the data processing module receives the front-end equipment data, processes the data to obtain the passenger flow volume of the open scenic spot, and sends the passenger flow volume and the corresponding time point to the passenger flow volume prediction module 3.
The data processing module 2 establishes digital identities for each tourist to identify in the scenic spot by utilizing the tourist identity information acquired by the security check gate; similarly, the digital identity for identity recognition in the scenic spot is established for each tourist by utilizing the tourist information acquired by the mobile phone base station; and comparing the security check gate data with the mobile phone base station data, taking the name, the source, the identification number, the gender and the mobile phone number of the tourist as judgment conditions, removing repeated data, combining the information of the tourist, establishing a unique digital identity for each tourist, and forming a tourist track, wherein the tourist track comprises the tourist park entrance time, the tourist exit time, the tourist playing area, the regional staying time and the like.
The input end of the passenger flow prediction module 3 is connected with the data processing module 2, the output end of the passenger flow prediction module is connected with the visual display module 4, the passenger flow and the corresponding time are received, the open scenic spot time point is used as the input of the multiple linear regression model, the passenger flow corresponding to the time point is used as the output of the multiple linear regression model, and the multiple linear regression model corresponding to the open scenic spot time point and the open scenic spot passenger flow is constructed; and the system is also used for receiving a future preset time point instruction, and inputting the preset time point as an open scenic spot time point and an open scenic spot passenger flow multiple linear regression model to obtain the future preset time point open scenic spot passenger flow.
For example, the method for constructing the multiple linear regression model corresponding to the open scenic spot time point and the open scenic spot passenger flow volume is that the preset multiple linear regression model is that y is β 0+ β 1x1, wherein y is historical passenger flow volume data of the open scenic spot, x1 is the time point corresponding to the historical passenger flow volume of the scenic spot, the open scenic spot time point and the corresponding passenger flow volume are taken into the model, the multiple linear regression model parameter set { β 0, β 1} corresponding to the historical temperature data of the isolating switch is determined, and when the β 0 parameter and the β 1 parameter are determined, the multiple linear regression model corresponding to the open scenic spot time point and the open scenic spot passenger flow volume is determined.
The method for obtaining the open-type scenic spot passenger flow at the future preset time point comprises the steps of enabling the future preset time point x1 to be a multi-element linear regression model y which is β 0+ β 1x1, and obtaining the scenic spot passenger flow y1 corresponding to the future preset time.
The input end of the visual display module 4 is connected with the passenger flow prediction module 3, receives the passenger flow of the future preset time point open scenic spot transmitted by the passenger flow prediction module 3, and visually displays the passenger flow.
In some embodiments, the visualization display module 4 may be a GIS thermodynamic diagram module or/and a traffic line graph module, and when the visualization display module 4 may be the GIS thermodynamic diagram module, the GIS thermodynamic diagram module performs area division on a scenic spot map by using longitude and latitude, guides position information of front-end devices into the GIS map, and receives passenger traffic data acquired by each front-end device in a current scenic spot and passenger traffic prediction data of a preset time in the future. A GIS map is prestored with a plurality of groups of people flow threshold values, and different threshold values correspond to different visual colors. The specific pedestrian volume threshold and the color relationship are already described in the first embodiment, and are not described again. It should be noted that the GIS thermodynamic diagram module receives current passenger flow data to show current scenic spot passenger flow, receives future preset time point passenger flow data calculated by the scenic spot passenger flow prediction model, and can also show future preset time point passenger flow.
In this embodiment, the front-end device data acquisition module 1 acquires security check gate data and mobile phone base station data, and processes the data to obtain the passenger flow volume in the open scenic spot. The data is not influenced by factors such as wide pedestrian flow target identification area, uncontrollable pedestrian flow trend, mixture of different targets such as people and vehicles and the like in the open scenic spot, and the obtained passenger flow volume of the open scenic spot is more accurate. In addition, the invention uses the open scenic spot passenger flow volume and the corresponding time point as historical data to construct a scenic spot passenger flow prediction model; the future preset time point is brought into the prediction model to predict the future preset time point open scenic spot passenger flow, and the problems that the existing open scenic spot passenger flow is uncontrollable and safety accidents are easily caused are solved.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches, based on design preferences. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a guest terminal. Of course, the processor and the storage medium may reside as discrete components in a guest terminal.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Claims (10)
1. An open scenic spot passenger flow statistics and prediction method, comprising:
collecting data of front-end equipment in an open scenic spot; the front-end equipment data comprises security check gate data and mobile phone base station data;
processing security check gate data and mobile phone base station data to obtain open scenic spot passenger flow;
taking the open scenic spot passenger flow volume and the corresponding time point as historical data, and constructing a scenic spot passenger flow prediction model;
bringing the future preset time point into a scenic spot passenger flow prediction model, and predicting the future preset time point open scenic spot passenger flow;
and visually displaying the passenger flow of the open scenic spot.
2. The method of claim 1, wherein the security gate data comprises identification card information of the visitor, the identification card information of the visitor at least comprises name, gender, telephone number, origin; the mobile phone base station data comprises tourist identity information, and the tourist identity information at least comprises tourist name, sex, telephone number and source.
3. The method of claim 1, wherein the processing of security gate data and cell phone base station data comprises: establishing digital identities for each tourist for identity recognition in the scenic spot by utilizing the tourist identity card information acquired by the security check gate; establishing digital identities for each visitor for identity recognition in the scenic spot by utilizing the visitor information acquired by the mobile phone base station; and comparing the gate data with the base station data, and removing repeated data by taking the name, the gender, the telephone number and the source as judgment conditions to obtain the passenger flow in the scenic spot.
4. The method as claimed in claim 1, wherein before collecting the data of the front-end device of the open scenic spot, the front-end device of the scenic spot is divided into regions so that the front-end device can completely cover the open scenic spot.
5. The method of claim 1, wherein the front-end device data further comprises traffic camera data, and the traffic camera data is used to identify the identity information of the visitor, and the identity information is merged with the security gate data and the mobile phone base station data for processing to obtain the traffic of the scenic spot.
6. The method for open scenic spot passenger flow statistics and prediction as claimed in claim 1, wherein the method for constructing a scenic spot passenger flow prediction model using the open scenic spot passenger flow volume and the corresponding time point as historical data comprises: and taking the open scenic spot time points as the input of the multiple linear regression model, taking the open scenic spot passenger flow corresponding to the scenic spot time points as the output of the multiple linear regression model, and constructing the multiple linear regression model of the open scenic spot time points and the corresponding open scenic spot passenger flow.
7. The method as claimed in claim 6, wherein the multiple linear regression model is constructed by taking the open time of the scenic spot, the playing time of the guest, and the sunset time as the input of the multiple linear regression model, and taking the amount of the guest in the open scenic spot corresponding to the time as the output of the multiple linear regression model.
8. The method of open-scene passenger flow statistics and prediction of claim 1,
the method for visually displaying the passenger flow of the open scenic spot is a GIS thermodynamic diagram or/and a passenger flow line diagram.
9. An open-scene passenger flow statistics and prediction system for use in claims 1-8, comprising: the system comprises a front-end equipment data acquisition module, a data processing module, a passenger flow prediction module and a visual display module; wherein the content of the first and second substances,
the output end of the front-end equipment data acquisition module is connected with the data processing module, acquires front-end equipment data and sends the front-end equipment data to the data processing module;
the input end of the data processing module is connected with the front-end equipment data acquisition module, the output end of the data processing module is connected with the passenger flow volume prediction module, the data processing module receives and processes the front-end equipment data to obtain the passenger flow volume of the open scenic spot, and the passenger flow volume and the corresponding time point are sent to the passenger flow volume prediction module;
the input end of the passenger flow prediction module is connected with the data processing module, the output end of the passenger flow prediction module is connected with the visual display module, the passenger flow and the corresponding time are received, the open scenic spot time point is used as the input of the multiple linear regression model, the passenger flow corresponding to the time point is used as the output of the multiple linear regression model, and the multiple linear regression model of the open scenic spot time point and the corresponding open scenic spot passenger flow is constructed; the system is also used for receiving a future preset time point instruction, and inputting the preset time point as an open scenic spot time point and an open scenic spot passenger flow multiple linear regression model to obtain the future preset time point open scenic spot passenger flow;
the input end of the visual display module is connected with the passenger flow prediction module, receives the future preset time point open scenic spot passenger flow transmitted by the passenger flow prediction module, and visually displays the passenger flow.
10. The system of claim 9, wherein the front-end device data collected by the front-end device data collection module comprises security gate data and cell phone base station data.
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| CN201911255319.8A CN111126679A (en) | 2019-12-10 | 2019-12-10 | Open scenic spot passenger flow statistics and prediction method and system |
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| CN110956089A (en) * | 2019-11-04 | 2020-04-03 | 李苗裔 | Historical block walking performance measuring method based on ICT technology |
| CN111487912A (en) * | 2020-05-12 | 2020-08-04 | 机科发展科技股份有限公司 | Central intelligent control system in venue |
| CN112365638A (en) * | 2020-11-16 | 2021-02-12 | 成都中科大旗软件股份有限公司 | Scenic spot passenger flow early warning system |
| CN113706701A (en) * | 2021-08-09 | 2021-11-26 | 北京三快在线科技有限公司 | Method and device for generating waste thermodynamic diagram, electronic equipment and readable storage medium |
| CN114529034A (en) * | 2021-12-28 | 2022-05-24 | 浙江中测新图地理信息技术有限公司 | Intelligent scheduling method for scenic spot pleasure boats based on real-time passenger flow volume |
| CN117313923A (en) * | 2023-09-14 | 2023-12-29 | 青岛大数据科技发展有限公司 | Scenic spot passenger flow prediction method, scenic spot passenger flow prediction system, storage medium and electronic equipment |
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