CN114529037A - Scheme for acquiring scenic spot pedestrian volume in real time - Google Patents
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Abstract
The invention discloses a scheme for acquiring scenic spot pedestrian volume in real time, which acquires data such as road congestion conditions around scenic spots, scenic spot pedestrian volume, scenic spot comments and the like in real time by using operator data and a public interface to acquire the data, displays the data to tourists and scenic spot personnel through portable terminals such as mobile phones, pads and notebook computers, and can provide corresponding data support and intelligent route planning for the tourists; the problems of how to obtain the scenic spot pedestrian volume, the road condition, the scenic spot comments and the like in real time are solved, and the requirements of tourists are met conveniently and quickly; meanwhile, the scenic spot can conveniently adjust corresponding personnel or system according to the data.
Description
Technical Field
The invention relates to the field of intelligent tourism, in particular to a scheme for acquiring the pedestrian volume in a scenic spot in real time.
Background
With the continuous development of national economy, the tourism industry is developed more and more. However, in the process of touring a tourist on a holiday, the problems that a touring route is difficult to plan, a plurality of scenic spots are difficult to decide, traffic jam before entering the scenic spots, people flow too intensively after entering the scenic spots and the like often exist, and the pleasantly relaxing touring experience is influenced. In the aspect of scenic spots, the situation that the number of tourists is not accurately controlled exists, the tourists are difficult to give early warning in time when the number of the tourists is large, corresponding arrangement is carried out, potential safety hazards are avoided, and the problems that resources and manpower are wasted due to the fact that the tourists are small in number and cannot be adjusted in time are caused are solved.
The acquisition of the flow of people in scenic spots is mainly as follows:
1. and performing comparative prediction by means of data of the past year. This approach is not accurate and is no longer applicable as travel becomes more convenient or other unforeseen factors.
2. By installing a gate, a camera or a thermal induction device. This approach is costly for the scenic spot and the guest can only see the corresponding data provided by the scenic spot by the guest.
If the pedestrian volume of the scenic spot can be conveniently acquired in real time, the pedestrian volume of a short period of time in the future can be predicted, and the problems of tourists and the scenic spot can be well solved.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art, and provide a scheme for acquiring the flow of people in the scenic spot in real time, wherein data such as the road congestion condition around the scenic spot, the flow of people in the scenic spot, and the comments on the scenic spot are acquired in real time by using operator data and a public interface to acquire the data, and then displayed to tourists and scenic spot personnel through portable terminals such as mobile phones, pads and notebook computers, so that corresponding data support and intelligent route planning can be provided for the tourists; the problems of how to obtain scenic spot people flow, road conditions, scenic spot comments and the like in real time are solved, and the requirements of tourists are met conveniently and quickly; meanwhile, the scenic spot can conveniently adjust corresponding personnel or system according to the data.
The invention provides the following technical scheme:
the invention provides a scheme for acquiring the pedestrian flow of scenic spots in real time, which comprises the following steps:
step 1: the position location technology is realized by the data of an operator, such as comparing different signal strengths of PING communication between a mobile phone and a plurality of base stations, so that the real-time pedestrian volume of a designated scenic spot is obtained;
step 2: acquiring and storing the real-time congestion condition of the road near the scenic spot through an API (application program interface) provided by the Baidu map;
and 3, step 3: obtaining the fare of the scenic spot and the latest and later-year contemporaneous articles or comments by obtaining the data of each large platform and each social platform such as microblog and small red book about the scenic spot;
and 4, step 4: integrating the data of the 3 steps into a database for certain processing to obtain the pedestrian flow and the congestion index of the scenic spot, predicting the next pedestrian flow according to a deep learning mode, and synchronizing the next pedestrian flow to the software/application of the portable terminal of the user;
and 5, step 5: finally, a reference scheme of a higher-quality tour route is provided for the user and corresponding data support is provided for the scenic spot through the real-time scenic spot pedestrian volume, road conditions, the congestion condition of the scenic spot when the user arrives, the ticket prices of the scenic spots, the distance time between the scenic spots, the evaluation of the scenic spot and the like;
the predictions about the scenic zone congestion index at step 4 and the traffic of people for a period of time in the future include the following:
scenic spot congestion index: firstly, acquiring scene maximum bearing capacity Data Max1 disclosed on the internet or provided by a scene as a primary judgment standard, then acquiring real-time scene people flow Data in step 1, acquiring an average value of people flow every 1 hour according to the business hours of the scene, recording the average value into a database, and finally obtaining a people flow Data set Data1 of each time period of a scene working day, a people flow Data set Data2 of each time period of a weekend and a people flow Data3 of each time period of a holiday in a big Data mode; according to the comparison between the peak value Max2 in the Data1, Data2 and Data3 and the maximum bearing capacity Max1, the maximum value Max of Max2 and Max1 is used as the actual bearable people number in the scenic spot, and the scenic spot congestion index is the actual scenic spot people flow/Max; meanwhile, the peak value Max3 of each of the Data1, Data2 and Data3 can also be used as the processing condition of acquiring the maximum number of people in the scenic spot under three different conditions of working day, weekend and holiday, and the resource and manpower input for the processing condition can also be used as one Data related to the experience of the tourists, and the actual processing capacity index of the scenic spot under the current condition is the actual scenic spot people flow/Max 3;
and (3) forecasting the scenic spot pedestrian volume in a future period of time: acquiring congestion data RData and current weather data WData of roads near a scenic spot in each period when the roads enter the scenic spot through a public interface provided by a Baidu map or a Gade map, recording the congestion data RData and the current weather data WData into a database, and acquiring people number change, namely people number Increase Increate, of the scenic spot under the data; we expect that the goal is-to predict how many people will enter the scenic spot in the next period of time, we need to introduce a machine learning algorithm model to achieve this goal; in machine learning, the task of predicting continuous variables (e.g., height, weight, number of entries per hour, etc.) is called "regression"; the method adopts a linear regression method, and predicts the number of people entering the scenic region per hour by using confirmed variables (continuous variables: normalization and feature scaling are required to be considered for the continuous variables; the continuous variables in the project comprise (1) congestion data RData (2) time periods, scenic region business time periods and n hours before starting the garden (n is determined by scenic region hot degree) (3) weather data WData); establishing a model in a machine learning mode, and predicting the data of the scenic spot pedestrian volume in a short period of time in the future by inputting the current weather and time through the model;
through the content, the function of acquiring the scenic spot pedestrian volume in real time is achieved, the scenic spot congestion index is obtained, the visualization function similar to the traffic software road congestion condition can be provided for tourists, and the tourists can check the scenic spot pedestrian volume when arriving according to the time of the tourists through the function of predicting the scenic spot pedestrian volume in a future period, so that the scenic spot is selected to be preferred.
Compared with the prior art, the invention has the following beneficial effects:
1. data such as road congestion conditions around scenic spots, scenic spot pedestrian volume, scenic spot comments and the like can be acquired in real time by using operator data and a data acquisition mode of a public interface, and then displayed to users through portable terminals such as mobile phones, pads and notebook computers, corresponding data support such as scenic spot congestion indexes and scenic spot actual processing capacity indexes is provided for tourists, and more intelligent route planning is provided for the tourists through prediction of the scenic spot pedestrian volume in a period of time in the future;
2. the scheme can well solve the problem of pain of tourists and scenic spots in the aspect of tourism, and can provide ticketing services, travel services and more other auxiliary functions for the scenic spots.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a principal flow diagram of the invention;
FIG. 2 is a schematic illustration of the scenic spot congestion index at step 4 of the present invention;
FIG. 3 is a schematic diagram of the prediction of scenic spot traffic over a future period of time at step 4 of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation. Wherein like reference numerals refer to like parts throughout.
Example 1
As shown in fig. 1-3, the present invention provides a scheme for acquiring the traffic of people in scenic spots in real time, which comprises the following steps:
step 1: the position location technology is realized by the data of an operator, such as comparing different signal strengths of PING communication between a mobile phone and a plurality of base stations, so that the real-time pedestrian volume of a designated scenic spot is obtained;
step 2: acquiring and storing the real-time congestion condition of the road near the scenic spot through an API (application program interface) provided by the Baidu map;
and 3, step 3: obtaining the fare of the scenic spot and the latest and later-year contemporaneous articles or comments by obtaining the data of each large platform and each social platform such as microblog and small red book about the scenic spot;
and 4, step 4: integrating the data of the 3 steps into a database for certain processing to obtain the pedestrian flow and the congestion index of the scenic spot, predicting the next pedestrian flow according to a deep learning mode, and synchronizing the next pedestrian flow to the software/application of the portable terminal of the user;
and 5, step 5: finally, a reference scheme of a higher-quality tour route is provided for the user and corresponding data support is provided for the scenic spot through the real-time scenic spot pedestrian volume, road conditions, the congestion condition of the scenic spot when the user arrives, the ticket prices of the scenic spots, the distance time between the scenic spots, the evaluation of the scenic spot and the like;
the predictions about the scenic zone congestion index at step 4 and the traffic of people for a period of time in the future include the following:
scenic spot congestion index: firstly, acquiring scene maximum bearing capacity Data Max1 disclosed on the internet or provided by a scene as a primary judgment standard, then acquiring real-time scene people flow Data in step 1, acquiring an average value of people flow every 1 hour according to the business hours of the scene, recording the average value into a database, and finally obtaining a people flow Data set Data1 of each time period of a scene working day, a people flow Data set Data2 of each time period of a weekend and a people flow Data3 of each time period of a holiday in a big Data mode; according to the comparison between the peak value Max2 in the Data1, Data2 and Data3 and the maximum bearing capacity Max1, the maximum value Max of Max2 and Max1 is used as the actual bearable people number in the scenic spot, and the scenic spot congestion index is the actual scenic spot people flow/Max; meanwhile, the peak value Max3 of each Data1, Data2 and Data3 can also be used as the processing condition of the maximum number of people in the scenic spots for three different conditions of working days, weekends and holidays, and the input resources and manpower for the processing condition can also be used as Data related to the experience of tourists, and the actual processing capacity index of the scenic spots under the current condition is the actual scenic spot people flow/Max 3;
and (3) forecasting the scenic spot pedestrian volume in a future period of time: acquiring congestion data RData and current weather data WData of roads near a scenic spot in each period when the roads enter the scenic spot through a public interface provided by a Baidu map or a Gade map, recording the congestion data RData and the current weather data WData into a database, and acquiring people number change, namely people number Increase Increate, of the scenic spot under the data; we expect that the goal is-to predict how many people will enter the scenic spot in the next period of time, we need to introduce a machine learning algorithm model to achieve this goal; in machine learning, the task of predicting continuous variables (e.g., height, weight, number of entries per hour, etc.) is called "regression"; the method adopts a linear regression method, and predicts the number of people entering the scenic region per hour by using confirmed variables (continuous variables: normalization and feature scaling are required to be considered for the continuous variables; the continuous variables in the project comprise (1) congestion data RData (2) time periods, scenic region business time periods and n hours before starting the garden (n is determined by scenic region hot degree) (3) weather data WData); establishing a model in a machine learning mode, and predicting the data of the scenic spot pedestrian volume in a short period of time in the future by inputting the current weather and time through the model;
through the content, the function of acquiring the pedestrian volume of the scenic spot in real time is achieved, the scenic spot congestion index is obtained, the visualization function similar to the traffic software road congestion condition can be provided for tourists, and the tourists can look up the pedestrian volume of the scenic spot when arriving according to the time of the tourists through the function of predicting the pedestrian volume of the scenic spot in a future period of time, so that the tourists can select which scenic spot to go preferentially.
Further, examples are as follows:
the method comprises the steps of taking a certain scenic spot as a test point, obtaining maximum load capacity data of the scenic spot, simultaneously obtaining scenic spot pedestrian volume data of a period of time (including working days, weekends and holidays), traffic jam data of roads nearby the scenic spot and weather data, storing the data into a database, building a model through big data and a machine learning mode, obtaining corresponding indexes (a scenic spot jam index and a scenic spot actual processing capacity index under the current condition), and finally displaying the indexes on a user terminal such as a webpage/App to provide convenient and fast travel application such as map software for a user.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (1)
1. A scheme for acquiring scenic spot pedestrian volume in real time is characterized by comprising the following steps:
step 1: the position location technology is realized by the data of an operator, such as comparing different signal strengths of PING communication between a mobile phone and a plurality of base stations, so that the real-time pedestrian volume of a designated scenic spot is obtained;
step 2: acquiring and storing real-time congestion conditions of roads near a scenic spot through an API (application program interface) provided by a Baidu map;
and 3, step 3: obtaining the fare of the scenic spot and the latest and later-year contemporaneous articles or comments by obtaining the data of each large platform and each social platform such as microblog and small red book about the scenic spot;
and 4, step 4: integrating the data of the 3 steps into a database for certain processing to obtain the pedestrian flow and the congestion index of the scenic spot, predicting the next pedestrian flow according to a deep learning mode, and synchronizing the next pedestrian flow to the software/application of the portable terminal of the user;
and 5, step 5: finally, a reference scheme of a higher-quality tour route is provided for the user and corresponding data support is provided for the scenic spot through the real-time scenic spot pedestrian volume, road conditions, the congestion condition of the scenic spot when the user arrives, the ticket prices of the scenic spots, the distance time between the scenic spots, the evaluation of the scenic spot and the like;
the predictions about the scenic zone congestion index at step 4 and the traffic of people for a period of time in the future include the following:
scenic spot congestion index: firstly, acquiring scene maximum bearing capacity Data Max1 disclosed on the internet or provided by a scene as a primary judgment standard, then acquiring real-time scene people flow Data in step 1, acquiring an average value of people flow every 1 hour according to the business hours of the scene, recording the average value into a database, and finally obtaining a people flow Data set Data1 of each time period of a scene working day, a people flow Data set Data2 of each time period of a weekend and a people flow Data3 of each time period of a holiday in a big Data mode; according to the comparison between the peak value Max2 in the Data1, Data2 and Data3 and the maximum bearing capacity Max1, the maximum value Max of Max2 and Max1 is used as the actual bearable people number in the scenic spot, and the scenic spot congestion index is the actual scenic spot people flow/Max; meanwhile, the peak value Max3 of each of the Data1, Data2 and Data3 can also be used as the processing condition of acquiring the maximum number of people in the scenic spot under three different conditions of working day, weekend and holiday, and the resource and manpower input for the processing condition can also be used as one Data related to the experience of the tourists, and the actual processing capacity index of the scenic spot under the current condition is the actual scenic spot people flow/Max 3;
and (3) forecasting the scenic spot pedestrian volume in a future period of time: acquiring congestion data RData and current weather data WData of roads near a scenic spot in each period when the roads enter the scenic spot through a public interface provided by a Baidu map or a Gade map, recording the congestion data RData and the current weather data WData into a database, and acquiring people number change, namely people number Increase Increate, of the scenic spot under the data; we expect that the goal is-to predict how many people will enter the scenic spot in the next period of time, we need to introduce a machine learning algorithm model to achieve this goal; in machine learning, the task of predicting continuous variables (e.g., height, weight, number of entries per hour, etc.) is called "regression"; the method adopts a linear regression method, and predicts the number of people entering the scenic region per hour by using confirmed variables (continuous variables: normalization and feature scaling are required to be considered for the continuous variables; the continuous variables in the project comprise (1) congestion data RData (2) time periods, scenic region business time periods and n hours before starting the garden (n is determined by scenic region hot degree) (3) weather data WData); establishing a model in a machine learning mode, and predicting the data of the scenic spot pedestrian volume in a short period of time in the future by inputting the current weather and time through the model;
through the content, the function of acquiring the scenic spot pedestrian volume in real time is achieved, the scenic spot congestion index is obtained, the visualization function similar to the traffic software road congestion condition can be provided for tourists, and the tourists can check the scenic spot pedestrian volume when arriving according to the time of the tourists through the function of predicting the scenic spot pedestrian volume in a future period, so that the scenic spot is selected to be preferred.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116029395A (en) * | 2023-03-24 | 2023-04-28 | 深圳市明源云科技有限公司 | Pedestrian flow early warning method and device for business area, electronic equipment and storage medium |
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| CN116029395A (en) * | 2023-03-24 | 2023-04-28 | 深圳市明源云科技有限公司 | Pedestrian flow early warning method and device for business area, electronic equipment and storage medium |
| CN116029395B (en) * | 2023-03-24 | 2023-08-04 | 深圳市明源云科技有限公司 | Pedestrian flow early warning method and device for business area, electronic equipment and storage medium |
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