CN117313923A - Scenic spot passenger flow prediction method, scenic spot passenger flow prediction system, storage medium and electronic equipment - Google Patents
Scenic spot passenger flow prediction method, scenic spot passenger flow prediction system, storage medium and electronic equipment Download PDFInfo
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Abstract
The invention discloses a scenic spot passenger flow prediction method, a system, a storage medium and electronic equipment, wherein the method comprises the steps of collecting passenger flow data, wherein the passenger flow data comprise scenic spot network camera data and/or scenic spot information platform data related to passenger flow; counting the passenger flow data to obtain statistical data; the statistical data is input into a passenger flow prediction model, the passenger flow prediction data is output, and/or the statistical data is input into a passenger flow correction model, and corrected current passenger flow data is output, so that the technical problem that passenger flow prediction in the related technology is completely dependent on subjective judgment and inaccurate passenger flow data prediction exists can be solved.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a scenic spot passenger flow prediction method, a scenic spot passenger flow prediction system, a storage medium and electronic equipment.
Background
At present, a scenic spot operator cannot accurately predict the current passenger flow volume of a scenic spot, only subjective and rough judgment can be carried out by naked eyes, so that the current passenger flow volume prediction has the problem of inaccuracy, and the scenic spot management work such as passenger flow guiding and the like is not easy to realize early warning and scheduling.
In view of the technical problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
The invention aims to overcome the technical defects and provide a scenic spot passenger flow prediction method, a scenic spot passenger flow prediction system, a storage medium and electronic equipment, so as to solve the technical problems that passenger flow prediction in the related art is completely dependent on subjective judgment and passenger flow data prediction is inaccurate.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
according to one aspect of the present invention, there is provided a scenic spot passenger flow prediction method, including:
collecting passenger flow data, wherein the passenger flow data comprises network camera data of scenic spots and/or data related to passenger flow in a scenic spot information platform;
counting the passenger flow data to obtain statistical data;
and inputting the statistical data into a passenger flow prediction model, outputting passenger flow prediction data, and/or inputting the statistical data into a passenger flow correction model, and outputting corrected current passenger flow data.
Optionally, the network camera data of the scenic spot includes:
regional passenger flow density data acquired from a density network camera; and
collecting regional passenger flow counting data from an AI network camera;
wherein the density network camera and the AI network camera are located in different areas of the scenic spot.
Optionally, the network camera data of the scenic spot further includes a point location monitoring image acquired from the network camera, and the method further includes:
and inputting the point location monitoring image into an AI model capable of analyzing the passenger flow data in the image to obtain the passenger flow data.
Optionally, the data related to the passenger flow in the scenic spot information platform includes: ticket data, scenic spot activity registration data, vehicle access data and WIFI access user data.
Optionally, the method further comprises:
and comparing the passenger flow prediction data with a preset passenger flow threshold, and generating an early warning prompt if the passenger flow prediction data exceeds the passenger flow threshold.
According to another aspect of the present invention, there is provided a scenic spot passenger flow prediction system, comprising:
the system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring passenger flow data, and the passenger flow data comprises network camera data of scenic spots and/or data related to passenger flow in a scenic spot information platform;
the data statistics unit is used for carrying out statistics on the passenger flow data to obtain statistics data;
the prediction correction unit is used for inputting the statistical data into a passenger flow prediction model and outputting passenger flow prediction data, and/or inputting the statistical data into a passenger flow correction model and outputting corrected current passenger flow data.
Optionally, the network camera data of the scenic spot includes:
regional passenger flow density data acquired from a density network camera; and
collecting regional passenger flow counting data from an AI network camera;
wherein the density network camera and the AI network camera are located in different areas of the scenic spot.
Optionally, the data related to the passenger flow in the scenic spot information platform includes: ticket data, scenic spot activity registration data, vehicle access data and WIFI access user data.
According to another aspect of the present invention, there is also provided an electronic apparatus including: a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, implements the steps of the method as described above.
According to another aspect of the present invention, there is also provided a computer-readable storage medium storing one or more programs executable by one or more processors to implement the steps in the above-described method.
According to the scenic spot passenger flow prediction method, the scenic spot passenger flow prediction system, the storage medium and the electronic equipment, the passenger flow of the scenic spot can be predicted according to objective data by collecting the network camera data of the scenic spot and/or the data related to the passenger flow in the scenic spot information platform and inputting the prediction model and/or the correction model for calculation after statistics, so that the technical problem that passenger flow prediction is completely dependent on subjective judgment and inaccurate in passenger flow data prediction in the related art can be solved.
Drawings
FIG. 1 is a schematic flow chart of a scenic spot passenger flow prediction method according to an embodiment of the present invention;
fig. 2 is a flow chart of a scenic spot passenger flow prediction method according to another embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a scenic spot passenger flow prediction system according to an embodiment of the present invention;
fig. 4 is a block diagram of a terminal that may implement a scenic spot passenger flow prediction method according to an embodiment of the present invention.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
In the related art, scenic spot passenger flow prediction completely depends on subjective judgment, and the technical problem of inaccurate passenger flow data prediction exists. Currently, there is no effective solution.
Based on the above problems, the invention provides a scenic spot passenger flow prediction method to solve the technical problems in the related art. The following is a detailed description.
Example 1
According to an embodiment of the present invention, there is provided a scenic spot passenger flow prediction method, and in combination with fig. 1, the method includes:
step S101, collecting passenger flow data, wherein the passenger flow data comprises network camera data of scenic spots and/or data related to passenger flow in a scenic spot information platform;
step S103, counting the passenger flow data to obtain statistical data;
step S105, inputting the statistical data into a passenger flow prediction model, outputting passenger flow prediction data, and/or inputting the statistical data into a passenger flow correction model, and outputting corrected current passenger flow data.
In step S101, the collected data may specifically include two types, one is network camera data of the scenic spot, and the other is data related to the passenger flow in the scenic spot information platform. Hereinafter, description will be made separately.
The network camera (i.e., IP camera, IPC for short) data of the scenic spot may be a density network camera, an AI network camera, or a general camera. The density network camera can collect the passenger flow density in the area to obtain the regional passenger flow density data. The AI network camera is internally provided with an AI algorithm model in the processing chip, and can input the acquired image into the AI algorithm model to obtain the number of passengers in the image, namely regional passenger flow technical data. For the common webcam, a point monitoring image can be acquired, so in step S101, the point monitoring image can be input into an AI algorithm model to analyze the passenger flow number data of the image.
As an example, a scenic spot may be divided into a high-error area and a low-error area, where there may be a large error when the number of people is calculated, so that the density network camera may be configured, and in a low-error area, such as a scenic spot entrance/exit area, the AI network camera may be configured, so that appropriate passenger flow data may be acquired according to the actual situation of the area, so as to improve accuracy of data statistics.
As one example, the data related to passenger flow in the scenic spot information platform includes one or more of ticketing data, scenic spot activity registration data, vehicle access data, and WIFI access user data. For example, for ticket data, the ticket data may be obtained from a ticketing system of the scenic spot information platform, such as obtaining the number of tickets purchased on the same day. For scenic spot activity registration data, the scenic spot information platform can release the activity information to the platform, and a user can access the scenic spot information platform through a mobile phone APP and the like to fill in the date, personnel information and the like of participating in the activity, and according to the information, the accurate statistics of the current passenger flow of the scenic spot is also facilitated. For vehicle access data, the parking lot barrier gate system can be provided with a license plate recognition instrument, the license plate number acquired by the license plate recognition instrument is stored in a scenic spot information platform as vehicle identity information, and after all the vehicle identity information is acquired, the number of vehicles in a parking lot of a current scenic spot can be obtained, so that the number of vehicles in the parking lot of the current scenic spot can be used as one of parameters for predicting the passenger flow of the current scenic spot. The WIFI is accessed to the user data, and the number of users currently accessing to the scenic spot WIFI can be obtained to be used as one of parameters for predicting the passenger flow of the scenic spot.
According to the embodiment, the network camera (IPC) data and the data collected by the scenic spot information platform are integrated and counted, so that the accuracy of current passenger flow prediction of the scenic spot is improved.
In step S103, statistics is performed on the passenger flow data to obtain statistical data, which may specifically include two steps.
Firstly, regional IPC data are counted, and in combination with the description of the foregoing embodiment, high and low error regions can be divided, the region is mainly composed of passenger flow counting network cameras, the density network cameras are auxiliary, for example, intelligent AI-IPC (i.e. AI network cameras) are installed at main entrances and exits in the region, the high error region, such as a dense region, is provided with density IPC, regional statistics groups are cooperatively formed by cooperating a plurality of counting cameras, the number of people entering and exiting the statistics groups is further output through a fence algorithm, and then the acquired data of the density network cameras are further combined, the multipoint sampling data are compared and calculated, and passenger flow data (for convenience of description, the passenger flow data are called as passenger flow data output by IPC) in the region are output.
And secondly, combining passenger flow data output by the IPC, and calculating passenger flow data in cooperation with passenger flow related data acquired by a scenic spot information platform, such as scenic spot activities, ticketing, WIFI and current day time point data of a parking lot.
Finally, in step S105, the statistical data is input into a passenger flow prediction model, and the current passenger flow prediction data is output. In the above step S103, preliminary statistics have been performed on the current passenger flow data, but in order to implement prediction of future passenger flow trend and/or modification of the current passenger flow data, in step S105, the statistics may be further predicted and modified by a passenger flow prediction algorithm and an AI model. For example, the statistical data is input into a passenger flow prediction model, passenger flow prediction data is output, and/or the statistical data is input into a passenger flow correction model, and corrected current passenger flow data is output. Of course, in some embodiments, the passenger flow correction algorithm and the passenger flow prediction algorithm may be integrated into a model or algorithm, so that after correcting the statistical data, the passenger flow prediction data is further output.
Further, after the passenger flow prediction data are obtained, the passenger flow prediction data can be compared with a preset passenger flow threshold, if the passenger flow threshold is not exceeded, no processing is performed, if the passenger flow threshold is exceeded, early warning prompt information is generated, the early warning prompt information can be sent to a management side terminal of an access platform or a scenic spot tourist terminal through a scenic spot information platform, or can be sent to scenic spots or traffic prompt equipment around the scenic spots in a broadcasting, display and other modes, so that the passenger flow of the scenic spots is dredged in advance, the passenger flow in the scenic spots is prevented from exceeding the upper limit acceptable by operation resources, and management confusion and potential safety hazards are avoided.
Referring to fig. 2, in one embodiment, the scenic spot passenger flow prediction method includes the following steps:
after the process is started, data acquisition is performed first, and specifically includes: the method comprises the steps of obtaining data from ticketing (systems), obtaining data from parking lots (systems) as an output result, obtaining data from vehicles in-out, obtaining data from density IPC as an output result, obtaining data from intelligent AI-IPC as an output result, obtaining data from common IPC as an output result, obtaining data from point location monitoring images as an output result, and further outputting the point location monitoring images to an AI model to obtain AI model analysis data. And acquiring the accessed user data from the scenic spot WIFI, and outputting the result as the accessed user data. And acquiring data from the regional activity (system), and outputting the result as activity registration data.
Substituting the data into a passenger flow multi-terminal cooperative intelligent statistical algorithm, which specifically comprises the following steps:
1. regional IPC data:
the method comprises the steps of dividing high-error areas and low-error areas aiming at open scenic spots, mainly taking passenger flow counting cameras as main areas and density cameras as auxiliary areas, installing intelligent AI-IPC (automatic identification and control) at main entrances and exits in the areas, installing density IPC at personnel-intensive areas in the areas with high errors, cooperatively forming an area statistics group by a plurality of counting cameras, outputting the number of people entering and exiting the statistics group through a fence algorithm, cooperating with the density cameras, comparing and calculating multi-point sampling data, and outputting passenger flow data in the areas.
2. Collaborative platform data:
and (3) comparing and analyzing passenger flow data output by the IPC, and calculating the passenger flow data through a smart statistical algorithm in cooperation with the current day time point data of scenic spot activities, ticketing, WIFI and parking lots.
Substituting the counted data into a passenger flow correction algorithm, specifically, estimating the accuracy of the current passenger flow and predicting and correcting the trend of the passenger flow by using the passenger flow prediction algorithm and the AI model estimation data.
The predicted and corrected data are substituted into an early warning mechanism, the predicted and corrected data can be compared with a passenger flow threshold, if the predicted and corrected data exceed the threshold, early warning reminding is carried out, passenger flow data are output, if the predicted and corrected data do not exceed the threshold, the passenger flow data are directly output, and finally the flow is ended.
According to the embodiment, through collecting the network camera data of the scenic spot and/or the data related to the passenger flow in the scenic spot information platform, the passenger flow of the scenic spot can be predicted according to objective data by inputting a prediction model and/or a correction model for calculation after statistics, so that the technical problem that the passenger flow data prediction is inaccurate due to the fact that the passenger flow prediction is completely dependent on subjective judgment in the related technology can be solved.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method described in the embodiments of the present application.
Example two
According to an embodiment of the present invention, there is provided a passenger flow prediction, and in combination with fig. 3, the apparatus includes:
a data acquisition unit 21, configured to acquire passenger flow data, where the passenger flow data includes network camera data of a scenic spot and/or data related to passenger flow in a scenic spot information platform;
a data statistics unit 23, configured to perform statistics on the passenger flow data to obtain statistics data;
the prediction correction unit 25 is configured to input the statistical data into a passenger flow prediction model, output passenger flow prediction data, and/or input the statistical data into a passenger flow correction model, and output corrected current passenger flow data.
According to the embodiment, through collecting the network camera data of the scenic spot and/or the data related to the passenger flow in the scenic spot information platform, the passenger flow of the scenic spot can be predicted according to objective data by inputting a prediction model and/or a correction model for calculation after statistics, so that the technical problem that the passenger flow data prediction is inaccurate due to the fact that the passenger flow prediction is completely dependent on subjective judgment in the related technology can be solved.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments, and this embodiment is not described herein.
It should be noted that the above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to what is disclosed in the above embodiments. It should be noted that, the above modules may be implemented in a corresponding hardware environment as part of the apparatus, and may be implemented in software, or may be implemented in hardware, where the hardware environment includes a network environment.
Fig. 4 is a block diagram of a terminal according to an embodiment of the present application, and as shown in fig. 4, the terminal may include: one or more (only one is shown) processors 101, memory 103, and transmission means 105, as shown in fig. 4, the terminal may further comprise input output devices 107.
The memory 103 may be used to store software programs and modules, such as program instructions/modules corresponding to the methods and apparatuses in the embodiments of the present application, and the processor 101 executes the software programs and modules stored in the memory 103, thereby performing various functional applications and data processing, that is, implementing the methods described above. Memory 103 may include high-speed random access memory, but may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 103 may further include memory remotely located with respect to processor 101, which may be connected to the terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 105 is used for receiving or transmitting data via a network, and can also be used for data transmission between the processor and the memory. Specific examples of the network described above may include wired networks and wireless networks. In one example, the transmission device 105 includes a network adapter (Network Interface Controller, NIC) that may be connected to other network devices and routers via a network cable to communicate with the internet or a local area network. In one example, the transmission device 105 is a Radio Frequency (RF) module for communicating with the internet wirelessly.
Wherein in particular the memory 103 is used for storing application programs.
The processor 101 may call an application stored in the memory 103 via the transmission means 105 to perform the following steps: collecting passenger flow data, wherein the passenger flow data comprises network camera data of scenic spots and/or data related to passenger flow in a scenic spot information platform; counting the passenger flow data to obtain statistical data; and inputting the statistical data into a passenger flow prediction model, outputting passenger flow prediction data, and/or inputting the statistical data into a passenger flow correction model, and outputting corrected current passenger flow data.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments, and this embodiment is not described herein.
It will be appreciated by those skilled in the art that the above-mentioned structure of the terminal is merely illustrative, and the terminal may be a smart phone (such as an Android mobile phone, an iOS mobile phone, etc.), a tablet computer, a palm computer, a mobile internet device (Mobile Internet Devices, MID), a PAD, etc. Fig. 4 is not limited to the structure of the electronic device. For example, the terminal may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in fig. 4, or have a different configuration than shown in fig. 4.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program for instructing a terminal device to execute in association with hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, read Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.
Embodiments of the present application also provide a storage medium. Alternatively, in the present embodiment, the above-described storage medium may be used for executing the program code of the above-described method.
Alternatively, in this embodiment, the storage medium may be located on at least one network device of the plurality of network devices in the network shown in the above embodiment.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: collecting passenger flow data, wherein the passenger flow data comprises network camera data of scenic spots and/or data related to passenger flow in a scenic spot information platform; counting the passenger flow data to obtain statistical data; and inputting the statistical data into a passenger flow prediction model, outputting passenger flow prediction data, and/or inputting the statistical data into a passenger flow correction model, and outputting corrected current passenger flow data.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments, and this embodiment is not described herein.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a usb disk, a Read Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
The integrated units in the above embodiments may be stored in the above-described computer-readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause one or more computer devices (which may be personal computers, servers or network devices, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in this application, the described embodiments of the apparatus are merely illustrative, such as the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, such as multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.
Claims (10)
1. The scenic spot passenger flow prediction method is characterized by comprising the following steps of:
collecting passenger flow data, wherein the passenger flow data comprises network camera data of scenic spots and/or data related to passenger flow in a scenic spot information platform;
counting the passenger flow data to obtain statistical data;
and inputting the statistical data into a passenger flow prediction model, outputting passenger flow prediction data, and/or inputting the statistical data into a passenger flow correction model, and outputting corrected current passenger flow data.
2. The method of claim 1, wherein the network camera data of the scenic spot comprises:
regional passenger flow density data acquired from a density network camera; and
collecting regional passenger flow counting data from an AI network camera;
wherein the density network camera and the AI network camera are located in different areas of the scenic spot.
3. The method of claim 2, wherein the webcam data of the scenic spot further includes a point location monitoring image acquired from a webcam, the method further comprising:
and inputting the point location monitoring image into an AI model capable of analyzing the passenger flow data in the image to obtain the passenger flow data.
4. The method of claim 1, wherein the scenic spot information platform data related to passenger flow comprises: ticket data, scenic spot activity registration data, vehicle access data and WIFI access user data.
5. The method according to claim 1, wherein the method further comprises:
and comparing the passenger flow prediction data with a preset passenger flow threshold, and generating an early warning prompt if the passenger flow prediction data exceeds the passenger flow threshold.
6. A scenic spot passenger flow prediction system is characterized by comprising
The system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring passenger flow data, and the passenger flow data comprises network camera data of scenic spots and/or data related to passenger flow in a scenic spot information platform;
the data statistics unit is used for carrying out statistics on the passenger flow data to obtain statistics data;
the prediction correction unit is used for inputting the statistical data into the passenger flow prediction model, outputting passenger flow prediction data, and/or inputting the statistical data into the passenger flow correction model, and outputting corrected current passenger flow data.
7. The system of claim 6, wherein the network camera data of the attraction comprises:
regional passenger flow density data acquired from a density network camera; and
collecting regional passenger flow counting data from an AI network camera;
wherein the density network camera and the AI network camera are located in different areas of the scenic spot.
8. The system of claim 7, wherein the scenic spot information platform data related to passenger flow comprises: ticket data, scenic spot activity registration data, vehicle access data and WIFI access user data.
9. An electronic device, comprising: a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, implements the steps of the method according to any of claims 1-5.
10. A computer readable storage medium storing one or more programs executable by one or more processors to implement the steps of the method of any of claims 1-5.
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| CN108305254A (en) * | 2018-03-21 | 2018-07-20 | 四川爱非客网络科技有限公司 | A kind of scenic spot guest flow statistics analysis system based on big data |
| CN108334995A (en) * | 2018-03-21 | 2018-07-27 | 四川爱非客网络科技有限公司 | A kind of scenic spot passenger flow estimation system based on big data |
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| CN111476691A (en) * | 2019-01-23 | 2020-07-31 | 上海新联纬讯科技发展股份有限公司 | Passenger flow prediction system and prediction method |
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2023
- 2023-09-14 CN CN202311186455.2A patent/CN117313923A/en active Pending
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| CN108305254A (en) * | 2018-03-21 | 2018-07-20 | 四川爱非客网络科技有限公司 | A kind of scenic spot guest flow statistics analysis system based on big data |
| CN108334995A (en) * | 2018-03-21 | 2018-07-27 | 四川爱非客网络科技有限公司 | A kind of scenic spot passenger flow estimation system based on big data |
| CN110300280A (en) * | 2018-03-22 | 2019-10-01 | 广西壮族自治区公众信息产业有限公司 | A kind of scenic spot passenger flow monitoring system |
| CN109727175A (en) * | 2018-12-25 | 2019-05-07 | 北京市颐和园管理处 | A kind of tourist attraction flow monitoring system for passenger |
| CN111476691A (en) * | 2019-01-23 | 2020-07-31 | 上海新联纬讯科技发展股份有限公司 | Passenger flow prediction system and prediction method |
| CN111126679A (en) * | 2019-12-10 | 2020-05-08 | 武汉烽火众智数字技术有限责任公司 | Open scenic spot passenger flow statistics and prediction method and system |
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