CN110544115B - Method and device for analyzing characteristics of tourists from scenic spot tourism big data - Google Patents
Method and device for analyzing characteristics of tourists from scenic spot tourism big data Download PDFInfo
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Abstract
The invention discloses a method and a device for analyzing characteristics of tourists from scenic spot tourism big data, which belong to the technical field of tourism, big data and data analysis application, solve the problem that online data can not obtain data of a whole number of users generally through ticketing data, and have the technical key points that: the online and offline resources of the scenic spot are fused, the mutual reinforcement and the complementation are realized, the more detailed and accurate characteristic analysis of the tourists can be carried out, the continuous behavior characteristics of the scenic spot of the tourists can be analyzed in real time based on the mathematical model of the moving behavior characteristics, and reliable data support is provided for the effective management of the scenic spot; the online and offline resources of the scenic spot can be fused, and the passenger flow condition of any area in the scenic spot can be analyzed in a fine-grained manner through the refined gridding calculation. The correlation fused data fuses the accuracy of the offline data in flow and the accuracy of the online data in space.
Description
Technical Field
The invention relates to the technical field of tourism, big data and data analysis application, in particular to a method and a device for analyzing characteristics of tourists from scenic spot tourism big data.
Background
In recent years, with the rapid development of the tourism industry, the number of tourists in each tourist attraction is increasing day by day, and the important significance of mastering the characteristics of the tourists such as stop points, position circulation and the like in the scenic attraction on the management and development of the scenic attraction is achieved. However, the traditional offline resources of scenic spots are mainly ticketing data, which are usually static data of fixed locations (ticket gates, etc.), and can only count the total traffic situation of the scenic spots, and the passenger flow situation of detailed areas such as the interior, roads, rest areas, etc. of the scenic spots is unknown.
As a supplement to off-line resources of a scenic spot, service media such as an App, an applet and a WiFi probe are used as on-line resources of the scenic spot, so that the position and people stream information of visitors in the scenic spot can be acquired in a finer granularity. But online data typically does not have access to the full amount of user data as opposed to ticketing data. Therefore, online and offline resources of the scenic spot are fused, and mutual reinforcement and complementation are achieved, so that more detailed and more accurate tourist characteristic analysis can be performed.
Disclosure of Invention
Aiming at the defects in the prior art, the embodiment of the invention aims to provide a method and a device for analyzing characteristics of tourists from scenic spot tourism big data. The analysis method of the invention is also suitable for staying and circulating in different scene areas in the city to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
an apparatus for analyzing characteristics of a guest from scenic spot tourist data, comprising:
the system comprises a scenic spot on-line data acquisition device, a data acquisition device and a data acquisition device, wherein the scenic spot on-line data acquisition device is an acquisition device for the real-time position of a tourist on line, and acquires the real-time position of the tourist in a scenic spot through an on-line application medium (a small tourist program, a WIFI probe and the like), so that the tourist big data of the scenic spot are continuously and accurately accumulated; the position information of tourists in the scenic spot on the line can be collected in multiple channels, and the tourism big data of the scenic spot are accumulated.
The system comprises a scenic spot data fusion computing device, a data fusion computing device and a data fusion computing device, wherein the scenic spot data fusion computing device is a fusion computing device of online data and offline data, the built-in scenic spot data fusion computing device can perform numerical fitting on the online data and the offline data of scenic spots, the passenger flow condition of any area in the scenic spots is quantified through refined gridding computation, and the accuracy of the offline data on flow and the accuracy of the online data on space are fused; the online resources and the offline resources of the scenic spot can be combined, and the passenger flow condition of any region of the scenic spot is analyzed in a fine-grained manner through the refined gridding fusion calculation, so that a data basis is provided for the tourist characteristic analysis device.
The tourist characteristic analysis device is internally provided with a set of tourist characteristic analysis method, can provide reliable data support for the effective management of scenic spots, can analyze the continuous behavior characteristics (stop positions, tour tracks, stop time in each scenic spot, passenger flow trend and the like) of the scenic spots of the tourists in real time based on a mathematical model of the movement behavior characteristics, and provides reliable data support for the effective management of the scenic spots.
A method for analyzing characteristics of tourists from scenic spot big tourist data can utilize scenic spot data to fuse fine-grained data fitted by a computing device, analyze continuous scenic spot behavior characteristics (stay positions, tour tracks, stay time in each scenic spot, passenger flow trend and the like) of the tourists in real time based on a mathematical model of mobile behavior characteristics, fuse online and offline resources of the scenic spots, perform grid numerical fitting in a fine-grained manner, and analyze the continuous scenic spot behavior characteristics of the tourists.
The method specifically comprises the following steps:
step 1, collecting position information of online tourists
The online tourist position information acquisition means that the unique identification and position information of online tourists in a scenic spot are continuously acquired at time intervals of seconds through a small tourist program and a WiFi probe, the acquired data are uploaded to a server in real time, and big tourist data are continuously accumulated;
step 2, scenic spot data fusion calculation
The scenic spot data fusion calculation refers to numerical fitting of offline ticket data and online collected data of scenic spots, and real-time passenger flow conditions of the whole scenic spot are calculated.
Due to the limitations of the small programs and the WiFi probes, the position information of the whole number of users cannot be collected, and certain deviation can be generated between the position information and the real scenic spot passenger flow. The scenic spot ticketing system comprises full ticketing and ticket checking data, but people flow conditions of any area in the scenic spot cannot be acquired. Therefore, scenic spot data fusion is amplified to a proper proportion by a numerical fitting method by using a small program and the user amount of a WiFi probe, so that the real passenger flow is approximate.
Step 3, scene space gridding
The data gridding processing is a processing method of scenic spot tourism big data, the whole scenic spot can be approximated through limited grids, and a large amount of position data acquired by a data acquisition device are reduced into each grid, so that the passenger flow condition of any area in the scenic spot is quantized.
Step 4, analysis of the place of residence
By using the scenic spot tourism big data after online and offline fusion, the area with abnormal staying amount of the tourists in the scenic spot can be quickly positioned through gridding calculation and a threshold value method, and the interest points and the congested road sections of the tourists are found.
The stay point analysis means that the area with abnormal stay number of the tourists in the scenic spot is calculated by using the fusion data. Specifically, by counting the passenger flow of all grids in the scenic spot, the grids with the flow greater than the threshold are listed as the stop points. The stopping point can be a dominant position in a scenic spot to attract visitors to stop, or a congested road section, and field guidance and problem troubleshooting are required.
Step 5, tour track analysis
Through counting the appearance sequence of different scenic spots of the tourists in the scenic spot, the touring track is analyzed, the tourist preference route is obtained, the scenic spot shunting condition is obtained, and the scenic spots with potential guide problems are found.
The tour track analysis is to count the appearance sequence of the same visitor (unique identifier) in different scenic spots in the scenic region through the small program in the day or in the last month and the position data collected by the WiFi probe, wherein the appearance sequence of each visitor represents a tour track.
Step 6, residence time analysis
And estimating the amount of visitors which can be received in each hour of the scenic spot according to the time difference of the same visitor monitored at the exit and the entrance of the scenic spot as the stay time of the scenic spot, and assisting the flow limiting decision.
Step 7, passenger flow trend analysis
And acquiring the passenger flow trend of any area in the scenic spot according to the demand time granularity by using the fusion data and the gridding processing method, and performing peak early warning.
By utilizing the fusion data and the gridding processing, the passenger flow trend of the scenic spots can be obtained, roads, rest areas, parking lots and the like in the scenic spots can be counted according to hours, and the passenger flow peak time and the passenger flow duty ratio in the morning and afternoon of any area can be accurately obtained; the fused data is counted according to the days, the passenger flow change trend of the holidays and the passenger flow change of the workdays and weekends can be obtained, and the results have important significance for scenic area management.
In summary, compared with the prior art, the embodiment of the invention has the following beneficial effects:
(1) the method and the device for analyzing the characteristics of the tourists are provided, so that the continuous behavior characteristics (stay positions, tour tracks, stay time in each scenic spot, passenger flow trend and the like) of the scenic spots of the tourists can be analyzed in real time based on a mathematical model of the moving behavior characteristics, and reliable data support is provided for effective management of the scenic spots.
(2) A method and a device for fusion analysis of scenic spot data are provided, which can fuse online and offline resources of scenic spots, and analyze the passenger flow condition of any area in the scenic spots in a fine-grained manner through refined gridding calculation. The correlation fused data fuses the accuracy of the offline data in flow and the accuracy of the online data in space.
To more clearly illustrate the structural features and effects of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Drawings
FIG. 1 is a schematic diagram of an actual method and apparatus for analyzing characteristics of tourists from scenic spot tourist data according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1, an embodiment of the present invention provides an apparatus for analyzing characteristics of tourists from scenic spot tourist big data, which is capable of fusing online and offline resources of scenic spots, performing grid numerical fitting with fine granularity, and analyzing characteristics of continuous behavior of the scenic spots of the tourists, and includes:
the system comprises a scenic spot on-line data acquisition device, a wireless fidelity (WIFI) probe and a data processing device, wherein the scenic spot on-line data acquisition device is an acquisition device for the real-time position of a tourist on line, acquires the real-time position of the tourist in a scenic spot through a small tourist program, the WIFI probe and other media, and continuously and accurately accumulates scenic spot tourist big data; the position information of tourists in the scenic spot on the line can be collected in multiple channels, and the tourism big data of the scenic spot are accumulated.
The system comprises a scenic spot data fusion computing device, a data fusion computing device and a data fusion computing device, wherein the scenic spot data fusion computing device is a fusion computing device of online data and offline data, the built-in scenic spot data fusion computing device can perform numerical fitting on the online data and the offline data of scenic spots, the passenger flow condition of any area in the scenic spots is quantified through refined gridding computation, and the accuracy of the offline data on flow and the accuracy of the online data on space are fused; the online resources and the offline resources of the scenic spot can be combined, and the passenger flow condition of any region of the scenic spot is analyzed in a fine-grained manner through the refined gridding fusion calculation, so that a data basis is provided for the tourist characteristic analysis device.
The tourist characteristic analysis device is internally provided with a set of tourist characteristic analysis method, can provide reliable data support for the effective management of scenic spots, can analyze the continuous behavior characteristics (stop positions, tour tracks, stop time in each scenic spot, passenger flow trend and the like) of the scenic spots of the tourists in real time based on a mathematical model of the movement behavior characteristics, and provides reliable data support for the effective management of the scenic spots.
A method for analyzing characteristics of tourists from scenic spot big tourist data can utilize scenic spot data to fuse fine-grained data fitted by a computing device, analyze continuous scenic spot behavior characteristics (stay positions, tour tracks, stay time in each scenic spot, passenger flow trend and the like) of the tourists in real time based on a mathematical model of mobile behavior characteristics, fuse online and offline resources of the scenic spots, perform grid numerical fitting in a fine-grained manner, and analyze the continuous scenic spot behavior characteristics of the tourists.
The method specifically comprises the following steps:
step 1, collecting position information of online tourists
The online tourist position information acquisition means that the unique identification and position information of online tourists in a scenic spot are continuously acquired at time intervals of seconds through a small tourist program and a WiFi probe, the acquired data are uploaded to a server in real time, and big tourist data are continuously accumulated;
step 2, scenic spot data fusion calculation
The scenic spot data fusion calculation refers to numerical fitting of offline ticket data and online collected data of scenic spots, and real-time passenger flow conditions of the whole scenic spot are calculated.
Due to the limitations of the small programs and the WiFi probes, the position information of the whole number of users cannot be collected, and certain deviation can be generated between the position information and the real scenic spot passenger flow. The scenic spot ticketing system comprises full ticketing and ticket checking data, but people flow conditions of any area in the scenic spot cannot be acquired. Therefore, scenic spot data fusion is amplified to a proper proportion by a numerical fitting method by using a small program and the user amount of a WiFi probe, so that the real passenger flow is approximate.
The fitting formula of the real-time passenger flow of different scenic spots in the scenic spot is as follows:
wherein the content of the first and second substances,
the ticket sales of the nearest monthly spot i,
the number of visitors monitored by a probe deployed at attraction i for the last month,
number of visitors using applets at sight i for a month at hand, MiNumber of visitors monitored by a probe arranged at the sight spot i within 3 minutes before the current time, NiThe number of guests using the applet at sight i within 3 minutes before the current time.
Step 3, scene space gridding
The data gridding processing is a processing method of scenic spot tourism big data, the whole scenic spot can be approximated through limited grids, and a large amount of position data acquired by a data acquisition device are reduced into each grid, so that the passenger flow condition of any area in the scenic spot is quantized.
Specifically, the gridding process refers to regularly dividing the entire scene space into a finite square grid of the same size. Wherein, the side length of the grid is determined according to the size of the scenic spot or the required granularity.
Through gridding processing, the real-time passenger flow of any grid A in the scenic spot can be fused and calculated, and the fitting formula is as follows:
wherein the content of the first and second substances,
the total ticket sales in the scenic spot of nearly one month,
number of visitors using applets in scenic spots for the next month, NgridThe number of guests using the applet for grid a is specified 3 minutes prior to the current time.
Step 4, analysis of the place of residence
By using the scenic spot tourism big data after online and offline fusion, the area with abnormal staying amount of the tourists in the scenic spot can be quickly positioned through gridding calculation and a threshold value method, and the interest points and the congested road sections of the tourists are found.
The stay point analysis means that the area with abnormal stay number of the tourists in the scenic spot is calculated by using the fusion data. Specifically, by counting the passenger flow of all grids in the scenic spot, the grids with the flow greater than the threshold are listed as the stop points. The stopping point can be a dominant position in a scenic spot to attract visitors to stop, or a congested road section, and field guidance and problem troubleshooting are required.
The calculation formula of the threshold is as follows:
Threshold＝Q3+1.5IQR
wherein Q is3And the third quartile of the passenger flow of all grids in the scenic spot, and the IQR is the quartile distance of the passenger flow of all grids.
Step 5, tour track analysis
Through counting the appearance sequence of different scenic spots of the tourists in the scenic spot, the touring track is analyzed, the tourist preference route is obtained, the scenic spot shunting condition is obtained, and the scenic spots with potential guide problems are found.
The tour track analysis is to count the appearance sequence of the same visitor (unique identifier) in different scenic spots in the scenic region through the small program in the day or in the last month and the position data collected by the WiFi probe, wherein the appearance sequence of each visitor represents a tour track.
Scenic spot tourism big data based on the tour track are analyzed from three different angles:
1. the different tour tracks are counted, the tour tracks preferred by most tourists can be obtained, and the tourists can be applied to business and pay attention to safety precaution and persuasion.
2. After different tourists visit the scenic spot A, the selection condition of the next target scenic spot in the track of the tourists is counted, and the shunting condition of the scenic spot A can be obtained, so that the tourists of different scenic spots can be more effectively guided or shunted according to the situation, and the passenger flow pressure during holidays is relieved.
3. Counting the occurrence frequency of each sight spot in one track, and enhancing the guidance of tourists when the sight spots repeatedly appear in a large number of tour tracks.
Step 6, residence time analysis
And estimating the amount of visitors which can be received in each hour of the scenic spot according to the time difference of the same visitor monitored at the exit and the entrance of the scenic spot as the stay time of the scenic spot, and assisting the flow limiting decision.
The analysis of the stay time of the tourists in each sight spot means to count the average time that a large number of tourists need to visit a certain sight spot. The calculation formula is as follows:
wherein the content of the first and second substances,
the time when the position information of the visitor j is collected at the exit of the scenic spot,
the time when the position information of the visitor j is collected at the entrance of the scenic spot,
the length of the guest's stay at the attraction (in minutes), and N is the number of guests whose location information is collected within the attraction.
The stay time analysis can play a planning role in the current limiting of hot scenic spots in the scenic area, and the number of tourists in the scenic spots is assumed to be
The number of guests that the attraction will receive per hour should not exceed
Step 7, passenger flow trend analysis
The passenger flow trend analysis means that the passenger flow trend of any area in the scenic spot is counted according to different time dimensions.
And acquiring the passenger flow trend of any area in the scenic spot according to the demand time granularity by using the fusion data and the gridding processing method, and performing peak early warning.
By utilizing the fusion data and the gridding processing, the passenger flow trend of the scenic spots can be obtained, roads, rest areas, parking lots and the like in the scenic spots can be counted according to hours, and the passenger flow peak time and the passenger flow duty ratio in the morning and afternoon of any area can be accurately obtained; the fused data is counted according to the days, the passenger flow change trend of the holidays and the passenger flow change of the workdays and weekends can be obtained, and the results have important significance for scenic area management.
The related method is developed and realized by Python language, and the whole processing process of the method and the device for analyzing the characteristics of the tourists from the scenic spot tourism big data is completed.
The technical principle of the present invention has been described above with reference to specific embodiments, which are merely preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. Other embodiments of the invention will occur to those skilled in the art without the exercise of inventive faculty, and such will fall within the scope of the invention.
Claims (6)
1. An apparatus for analyzing characteristics of a guest from scenic spot tourist data, comprising:
the system comprises a scenic spot on-line data acquisition device, a data acquisition device and a data acquisition device, wherein the scenic spot on-line data acquisition device is an acquisition device for the real-time position of a tourist on line, acquires the real-time position of the tourist in a scenic spot through an on-line application medium, and accumulates scenic spot tourism big data;
the scenic spot data fusion computing device is a fusion computing device of online data and offline data, performs numerical fitting on the online data and the offline data of the scenic spot, and quantifies the passenger flow condition of any area in the scenic spot through detailed gridding computation; the fitting formula of the real-time passenger flow of different scenic spots in the scenic spot is as follows:
wherein the content of the first and second substances,
the ticket sales of the nearest monthly spot i,
the number of visitors monitored by a probe deployed at attraction i for the last month,
number of visitors using applets at sight i for a month at hand, MiNumber of visitors monitored by a probe arranged at the sight spot i within 3 minutes before the current time, NiThe number of visitors using the small programs at the scenic spot i within 3 minutes before the current time;
the gridding processing means that the whole scenic spot space is regularly divided into a finite square grid with the same size;
wherein, the side length of the grid is determined according to the size of the scenic spot or the required granularity;
through gridding processing, the real-time passenger flow of any grid A in the scenic spot is calculated in a fusion mode, and the fitting formula is as follows:
wherein the content of the first and second substances,
the total ticket sales in the scenic spot of nearly one month,
number of visitors using applets in scenic spots for the next month, NgridThe number of tourists using the small programs in the grid A is specified within 3 minutes before the current moment;
and the tourist characteristic analysis device analyzes the scenic spot continuous behavior characteristics of the tourists in real time based on the mathematical model of the movement behavior characteristics.
2. The device for analyzing tourist characteristics of tourist attractions according to claim 1 wherein the online application medium in the scenic data fusion computing device comprises a tourist applet and a WIFI probe.
3. The apparatus for analyzing tourist characteristics of tourist attraction according to claim 2, wherein said tourist characteristic analyzing means is configured to analyze the characteristic of continuous behavior of tourist at attraction including location of stay, track of tourist, length of stay at each attraction, and tendency of passenger flow.
4. A method for analyzing characteristics of tourists from scenic spot tourist big data, comprising the steps of:
step 1, collecting position information of online tourists
Continuously acquiring unique identification and position information of online tourists in a scenic spot at time intervals of seconds through an online application medium, uploading the acquired data to a server in real time, and continuously accumulating big tourism data;
step 2, scenic spot data fusion calculation
Performing numerical fitting on offline ticket data and online acquired data of the scenic spot, and calculating the real-time passenger flow condition of the panoramic spot; the fitting formula of the real-time passenger flow of different scenic spots in the scenic spot is as follows:
wherein the content of the first and second substances,
the ticket sales of the nearest monthly spot i,
the number of visitors monitored by a probe deployed at attraction i for the last month,
number of visitors using applets at sight i for a month at hand, MiNumber of visitors monitored by a probe arranged at the sight spot i within 3 minutes before the current time, NiThe number of visitors using the small programs at the scenic spot i within 3 minutes before the current time;
step 3, scene space gridding
Approximating the whole scenic spot through a limited grid, reducing a large amount of position data acquired by a data acquisition device into each grid, and quantifying the passenger flow condition of any area in the scenic spot; the gridding processing means that the whole scenic spot space is regularly divided into a finite square grid with the same size;
wherein, the side length of the grid is determined according to the size of the scenic spot or the required granularity;
through gridding processing, the real-time passenger flow of any grid A in the scenic spot is calculated in a fusion mode, and the fitting formula is as follows:
wherein the content of the first and second substances,
the total ticket sales in the scenic spot of nearly one month,
number of visitors using applets in scenic spots for the next month, NgridNumber of visitors using the applet for the designated grid A within 3 minutes before the current time
Step 4, analysis of the place of residence
By utilizing the scenic spot tourism big data after online and offline fusion, the area with abnormal staying amount of the tourists in the scenic spot can be quickly positioned through gridding calculation and a threshold value method, and the interest points and the congested road sections of the tourists are found;
step 5, tour track analysis
By counting the appearance sequence of different scenic spots of tourists in the scenic area, analyzing the tour track, acquiring the preferential route of the tourists and the shunting condition of the scenic spots, and finding the scenic spots with potential guide problems;
step 6, residence time analysis
According to the time difference of the same tourist monitored at the exit and the entrance of the scenic spot as the stay time of the scenic spot, estimating the amount of tourists which can be received by the scenic spot per hour, and assisting in flow limiting decision;
step 7, passenger flow trend analysis
And counting the passenger flow trend of any area in the scenic spot according to different time dimensions, acquiring the passenger flow trend of any area in the scenic spot according to the demand time granularity by using the fusion data and the gridding processing method, and performing peak early warning.
5. The method for analyzing tourist characteristics of scenic spot according to claim 4, wherein in step 4, the stay location analysis means calculating areas with abnormal stay number of tourists in the scenic spot by using the fusion data, and listing the grids with flow rate greater than threshold as stay points by counting the passenger flow rate of all grids in the scenic spot;
the calculation formula of the threshold value is as follows:
Threshold=Q3+1.5IQR
wherein Q is3And the third quartile of the passenger flow of all grids in the scenic spot, and the IQR is the quartile distance of the passenger flow of all grids.
6. The method as claimed in claim 4, wherein the analysis of the staying time of the tourists at each attraction in step 6 is the average time spent by a large number of tourists visiting a certain attraction, and the calculation formula is:
wherein the content of the first and second substances,
the time when the position information of the visitor j is collected at the exit of the scenic spot,
the time when the position information of the visitor j is collected at the entrance of the scenic spot,
the unit of the stay time of the tourist in the scenic spot is minutes, and N is the number of the tourist acquiring the position information in the scenic spot.
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