A scenic spot ticket management system and method based on big data analysis

Abstract

The present invention discloses a scenic spot ticket management system and method based on big data analysis, which relates to the technical field of ticket management. The present invention analyzes all scenic spot route maps in the scenic spot, obtains block scenic spots by overlapping and extracting marked routes, sets tickets for each block scenic spot, and formulates a distributed ticketing strategy in the scenic spot; determines the peak period and off-peak period of the scenic spot by analyzing sales; calculates the normal number of people interval of each scenic spot in the scenic spot based on the number of visitors in history; calculates the circulation threshold value when tourists are not congested by using the circulation time and distance between different block scenic spots; judges whether there is a congestion risk in the block scenic spot by using the normal number of people interval and the circulation threshold value and issues an early warning, and adjusts the scenic spot ticketing by using the early warning information; during the off-peak period of the scenic spot, judges the number of visitors to each block scenic spot in the scenic spot by using the normal business interval, and adjusts the tickets of different block scenic spots according to the number of visitors.

Description

Technical Field

[0001] The present invention relates to the technical field of ticket management, and in particular to a scenic spot ticket management system and method based on big data analysis. Background Art

[0002] Before the advent of the information age, all tickets were paper-based, making their management difficult. However, the widespread adoption of the internet has provided a foundational network environment for intelligent ticket management at scenic spots. Scenic spots can now sell tickets to visitors through official websites, online travel platforms, and other channels, allowing visitors to purchase tickets anywhere with an internet connection, breaking the constraints of time and space. Modern ticket gates incorporate a variety of advanced technologies, such as barcode recognition, QR code scanning, ID card recognition, and facial recognition. These technologies make the ticket checking process faster and more accurate, effectively preventing ticket counterfeiting and fare evasion. This has significantly eliminated the problem of ticket scalping. However, with the increased convenience of ticket purchases in the digital age, managing large numbers of visitors has become more difficult, leading to frequent crowds at scenic spots, significantly impacting both business operations and the visitor experience. Furthermore, in larger scenic spots, management personnel are unable to track and manage visitors entering, creating significant management risks. Summary of the Invention

[0003] The purpose of the present invention is to provide a scenic spot ticket management system and method based on big data analysis to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A scenic spot ticket management method based on big data analysis, the method comprising the following steps:

[0006] S100: Analyze tickets for scenic spots that require ticket purchase and have separate attractions within the scenic area, analyze all attraction route maps within the scenic area, extract overlapping marked routes to obtain block attractions, set ticket gates within each block attraction, set tickets for each block attraction, formulate different ticket sales time dimensions by analyzing the route spatial dimensions of each attraction, and formulate a distributed ticket sales strategy within the scenic area;

[0007] Furthermore, the specific steps for formulating a distributed ticketing strategy in a scenic spot are as follows:

[0008] S101, extract the scenic area tour route map in the central processor of the scenic area, mark each scenic spot in the scenic area in the tour route map according to the scenic area plan, collect the historical movement trajectory of tourists visiting each scenic spot to generate a route map for each scenic spot, and set the generated route map of all scenic spots in the scenic area as , Represents the 1st, 2nd, 3rd, ..., mth scenic spot route map generated in the scenic area, where m is a positive integer; all the generated scenic spot route maps are marked with different colors, and all the marked scenic spot route maps are overlapped and inverted to obtain the scenic spot route deduction map, and the color overlapping part and the independent part of the scenic spot route deduction map are extracted separately, and the extracted color overlapping part is set as , represents the 1st, 2nd, 3rd, ..., xth overlapping parts extracted from the scenic route deduction map, where x is a positive integer; let the extracted individual parts be , Represent the 1st, 2nd, 3rd, ..., yth individual parts extracted from the scenic route interpretation diagram, where y is a positive integer; each extracted individual part is used as each block attraction in the scenic area, and each block attraction includes the scenic area planned in the scenic area and the corresponding individual route; tickets are sold separately for each block attraction;

[0009] S102. Using each scenic spot route map before overlapping inversion, the overlapping part extracted from the scenic spot route deduction map is divided into different spatial dimensions, each spatial dimension contains a scenic spot route map; the walking speed of tourists when touring the scenic spot in the history is collected as Vb, and the total length Wz of the scenic spot route map, the length Wc of the overlapping part, and the length Wd of the individual route contained in the block scenic spot in each spatial dimension are extracted; the time spent by tourists on walking during the tour is calculated as follows: S102. Using each scenic spot route map before overlapping inversion, the overlapping part extracted from the scenic spot route deduction map is divided into different spatial dimensions, each spatial dimension contains a scenic spot route map; the walking speed of tourists when touring the scenic spot in the history is collected as Vb, and the total length Wz of the scenic spot route map, the length Wc of the overlapping part, and the length Wd of the individual route contained in the block scenic spot in each spatial dimension are extracted; the time spent by tourists on walking during the tour is calculated as follows: In the formula, F represents the calculated walking time of tourists, W represents the length of the walking route, and Vb represents the walking speed of tourists during the tour; the total length Wz of the scenic route map in each spatial dimension, the length of the overlapping part Wc, and the length Wd of the individual routes contained in the block attractions are respectively input into W in the formula, and the walking time of tourists through the total length Wz of the scenic route map in each spatial dimension, the length Wh of the overlapping part, and the length Wd of the individual routes contained in the block attractions are calculated; the difference time of tourists walking in each spatial dimension is calculated, and the formula is:

[0010] ;

[0011] In the formula, Fc represents the difference in walking time of tourists in each spatial dimension, Fz represents the walking time of tourists passing the total length of the scenic route map in each spatial dimension, Fh represents the walking time of tourists passing the overlapping length of the scenic route map in each spatial dimension, and Fd represents the walking time of tourists passing the length of the individual routes contained in the block scenic spots in each spatial dimension. After calculation, the difference time of each spatial dimension is , represents the difference time of the calculated 1st, 2nd, 3rd, ..., mth dimensions;

[0012] S103. Extract the difference time in the two spatial dimensions in sequence and calculate the difference value Fcc. When Fcc≤Fh, it is determined that there is interference between the routes of the scenic spots in the corresponding two spatial dimensions, and the tickets for the scenic spots in the corresponding two spatial dimensions are sold in different time dimensions. The two ticket selling time dimensions are greater than Fh. When Fcc>Fh, it is determined that there is no interference between the routes of the scenic spots in the corresponding two spatial dimensions, and tickets are sold normally. After performing pairwise judgments on all spatial dimensions, a distributed ticketing strategy is constructed using block-based scenic spot ticketing and time-based ticketing.

[0013] The route maps of all scenic spots in the scenic area are marked and overlapped, and the overlapping parts and non-overlapping separate parts are extracted respectively. The separate parts are used as block attractions. After the tourist area in the scenic area is divided into blocks, tickets are sold separately for each attraction, and gates are set up. Only those who purchase tickets for the corresponding attractions can enter; this enables the scenic area to locate the visiting position of each tourist in the scenic area in real time, making it convenient to manage tourists in the scenic area.

[0014] The extracted overlapping parts represent the roads that need to be passed to go to different scenic spots. When the number of tourists is too large, the number of tourists who need to pass through the overlapping parts will greatly increase, and there is a high probability of congestion. Therefore, the arrival time of tourists at different scenic spots is calculated. When the arrival time of the overlapping part is less than the time of the overlapping part when going to two scenic spots, it proves that there are tourists going to different scenic spots at the same time in the overlapping part, causing congestion. Therefore, the tickets for the scenic spots where the arrival time of tourists is less than the overlapping part are divided into different sales times, so that the time of tourists arriving at the overlapping part is staggered, and congestion is not caused. The distributed ticketing strategy constructed by using spatial block scenic spot ticketing and time dimension ticketing can accurately manage the number of tourists at each location in the scenic area and avoid congestion.

[0015] S200, collecting the sales of scenic spot tickets in different time periods in history, and determining the peak and off-peak periods of the scenic spot by analyzing the sales;

[0016] Furthermore, the specific steps to determine the peak and off-peak periods of the scenic area are as follows:

[0017] S201. Collect the sales of scenic spot tickets in different time periods in history. , Represents the sales of scenic spot tickets collected in the 1st, 2nd, 3rd, ..., jth time period, where j is a positive integer; the collected sales are plotted in chronological order, and the peaks and troughs in the graph are extracted by analyzing the graph. Let the extracted peak value be , Indicates the 1st, 2nd, 3rd, ..., pth peak value of the extracted sales curve, and the extracted trough value is , Indicates the 1st, 2nd, 3rd, ..., pth trough value in the extracted sales curve graph, where p is a positive integer;

[0018] S202, judge the extracted peak values ​​and trough values, select the peak time corresponding to the maximum peak value among the p peak values ​​as the peak period of the scenic area, and select the trough time corresponding to the minimum value among the p trough values ​​as the low-peak period of the scenic area; find the time coordinates of the two inflection points of the maximum peak value in the curve graph respectively , Indicates the time coordinate of the previous inflection point of the maximum peak, Indicates the time coordinate of the next inflection point after the maximum peak; the time coordinates of the two inflection points of the minimum trough in the curve graph are , Indicates the time coordinate of the previous inflection point of the minimum trough, Indicates the time coordinate of the next inflection point after the minimum trough; the peak period of the scenic area is finally obtained as , the low peak period is .

[0019] According to the number of tourists in the scenic area at different times in history, the peak and off-peak periods of the scenic area can be determined. The ticketing of the scenic area can then be adjusted according to different time stages to achieve more humane adjustments.

[0020] S300: Collect the number of visitors to each scenic spot in the scenic area during normal operation at different historical periods, and define normal operation as when the scenic spot is profitable and not congested; calculate the normal number of visitors to each scenic spot in the scenic area based on the historical number of visitors; collect the flow time of tourists between different scenic spots in the scenic area when there is no congestion, and calculate the flow threshold when there is no congestion using the flow time and distance between different scenic spots;

[0021] Furthermore, the specific steps for calculating the normal number of people interval and circulation threshold of each block attraction are as follows:

[0022] S301. Collect the number of visitors to each scenic spot in the scenic area during normal business hours at different periods in history. , represents the number of visitors to the 1st, 2nd, 3rd, ..., kth block attractions in the scenic area during normal operation in the collected history, where k is a positive integer. The normal number of visitors to each block attraction in different periods is used to calculate the normal number of visitors to each block attraction. The formula is:

[0023] ;

[0024] ;

[0025] In the formula, Indicates the lower limit of the normal number of people in each block of scenic spots. Indicates the upper limit of the normal number of people in each block of attractions; It represents the average number of visitors to each scenic spot in different periods. It represents the standard deviation of the number of visitors to each scenic spot in different periods; after calculation, the normal number of visitors to each scenic spot in the scenic area is , Indicates the normal number of people calculated for the 1st, 2nd, 3rd, ..., mth block attractions in the scenic area;

[0026] S302: Collect the historical data of the time it takes for tourists to travel between different scenic spots when there is no congestion in the scenic area. , It represents the circulation time of tourists between the 1st, 2nd, 3rd, ..., uth pairs of scenic spots when there is no congestion in the scenic area in the collected history, where u is a positive integer; the distance between each pair of scenic spots in each circulation time is , represents the distance between each pair of scenic spots in the collected 1st, 2nd, 3rd, ..., uth circulation time; the circulation threshold within the scenic area is calculated using the collected circulation time and relative distance, and the formula is: ; In the formula, Inv represents the calculated circulation threshold within the scenic area, Indicates the circulation speed of tourists in the scenic area, represents the average value of the calculated circulation velocity, Indicates the standard deviation of the calculated circulation velocity.

[0027] When judging whether there is congestion among tourists in a scenic area, the number of people in the scenic area cannot fully reflect the congestion situation. Even if there are a large number of tourists, congestion will not occur when the circulation speed is fast. Sometimes congestion may also occur when there are not many people but the circulation speed is slow. Therefore, the normal number of people interval and the circulation threshold are calculated for double judgment to avoid misjudgment of whether there is congestion in the scenic area and make the judgment more accurate.

[0028] S400: When purchasing tickets in the ticket purchasing channel, a tourist can arbitrarily select a block of scenic spots within the scenic area to purchase tickets for each block. After entering the scenic area using the ticket, the tourist then uses the ticket to verify and register at the gate of each block of scenic spots within the scenic area before entering. During peak hours, the central processor of the scenic area records the number of visitors in each block of scenic spots in real time and calculates the real-time circulation speed of tourists.

[0029] Furthermore, during peak hours, the central processor of the scenic area records the number of visitors in each scenic spot in real time and calculates the real-time circulation speed of tourists in the following specific steps:

[0030] S401. When purchasing tickets in the ticket purchasing channel, tourists can arbitrarily choose the block attractions in the scenic area to purchase tickets in blocks. After entering the scenic area with the tickets, they can use the tickets to check and register at the gate of each block attraction to enter. During the peak period, the central processor of the scenic area records the number of visitors in each block attraction in real time. , Indicates the number of visitors to the 1st, 2nd, 3rd, ..., mth block attractions in the scenic area recorded in real time;

[0031] S402: Analyze the real-time number of visitors in each scenic spot, calculate the difference in the number of visitors between each two scenic spots at every time t1, and calculate the real-time tourist flow rate in the scenic spot. The formula is: ;

[0032] In the formula, Vs represents the real-time circulation speed of tourists in the scenic area, Ss and represents the number of visitors in the two block attractions, L represents the distance between the two block attractions, and t1 represents the calculation time interval.

[0033] S500: Analyze the real-time number of visitors in each scenic spot using the normal number interval, analyze the real-time tourist flow rate using the flow threshold, and then determine whether there is a congestion risk in the scenic spot and issue an early warning. Use the early warning information to adjust the scenic spot ticketing;

[0034] Furthermore, the specific steps for adjusting scenic spot ticketing using early warning information are as follows:

[0035] S501, using the normal number of people interval to analyze the real-time number of visitors in each block of scenic spots, using the flow threshold to analyze the real-time flow speed of tourists, when and When the corresponding block of scenic spots is judged to have congestion risks, the scenic area center processor issues an early warning and stops the ticket sales for the corresponding block of scenic spots; when but When , it is determined that the corresponding block of attractions is full and the number of tickets sold for the corresponding block of attractions is reduced; when and When , it is determined that there is a lack of tourists for the corresponding block attractions, and the number of tickets sold for the corresponding block attractions is increased; when but When the corresponding block of attractions is judged to be normal, the ticket sales of the corresponding block of attractions will not be adjusted.

[0036] After making a double assessment of the number of visitors and the circulation speed of each scenic spot during the peak period of the scenic area, the ticket price of each scenic spot is adjusted according to different judgment results. This can avoid congestion accidents caused by excessive number of people and slow circulation speed in the scenic area, reduce the probability of safety hazards in the scenic area, and increase the comfort of tourists' visits.

[0037] S600: During the off-peak period of the scenic area, the number of visitors to each sub-area attraction in the scenic area is determined by using the normal business interval, and the ticket prices of different sub-area attractions are adjusted according to the number of visitors.

[0038] Furthermore, the specific steps for adjusting the ticket prices of different scenic spots according to the number of visitors are as follows:

[0039] S601, during the off-peak period of the scenic area, use the normal business interval to judge the number of visitors to each block of scenic spots in the scenic area, and record the number of visitors to each block of scenic spots during the off-peak period as Sd in real time. When the corresponding block of attractions is judged to be not profitable, the corresponding block of attractions is closed and ticket sales for the corresponding block of attractions are stopped; when When the corresponding block attractions are judged to be profitable, they are opened normally and the ticket prices of the corresponding attractions are increased.

[0040] After judging the number of visitors to each scenic spot during the off-peak period, the scenic spots with fewer visitors can be closed and ticket sales can be stopped. This can reduce the scenic spot's maintenance costs for the blocks and avoid losses during the off-peak period.

[0041] A scenic spot ticket management system based on big data analysis, which includes a data collection module, a distributed ticket sales module, a visitor data analysis module, a real-time visitor data calculation module and a ticket adjustment module;

[0042] The data collection module is used to collect the number, time and circulation data of tourists visiting the scenic area in history;

[0043] The distributed ticketing module is used to analyze the route map of each scenic spot in the scenic area, extract each block of scenic spots after marking overlaps, and divide them into different spatial dimensions, and calculate the time dimension of the scenic spot ticket for each spatial dimension;

[0044] The tourist data analysis module is used to calculate the normal number of visitors and the circulation threshold when visiting different scenic spots in the scenic area;

[0045] The real-time tourist data calculation module is used to calculate the real-time number of visitors and circulation speed in each block of scenic spots in the scenic area;

[0046] The ticket adjustment module is used to adjust the ticket of each sub-block attraction after judging the number of visitors and the circulation speed in each sub-block attraction in real time by using the normal number of people interval and circulation speed.

[0047] The tourist data analysis module includes a normal number interval calculation unit and a circulation threshold calculation unit;

[0048] The normal number interval calculation unit is used to analyze and calculate the number of tourists visiting each block of attractions in the scenic area in history to obtain the normal number interval of each block of attractions;

[0049] The circulation threshold calculation unit is used to calculate the circulation threshold in the scenic area by using the collected circulation time and relative distance.

[0050] The ticket adjustment module includes a peak period adjustment unit and a low peak period adjustment unit;

[0051] The peak period adjustment unit is used to calculate the number of visitors and the circulation speed of each scenic spot during the peak period, and adjust the ticket price of each block scenic spot by analyzing and judging the number of visitors and the circulation speed;

[0052] The off-peak period adjustment unit is used to calculate the number of visitors to each scenic spot during the off-peak period, analyze the number of visitors to the scenic spot, and adjust the ticket price of each block scenic spot during the off-peak period.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. The present invention marks the overlapping of the route map of all scenic spots in the scenic area, extracts the overlapping parts and the non-overlapping separate parts respectively, and uses the separate parts as block scenic spots. After the tourist area in the scenic area is divided into blocks, tickets are sold separately for each scenic spot, and gates are set up. Only those who purchase the corresponding scenic spot tickets can enter. This allows the scenic area to locate the visiting location of each tourist in the scenic area in real time, facilitates the management of tourists in the scenic area, and increases the flexibility of the scenic area's operations. Tickets for scenic spots whose arrival time is less than that of the overlapping part are sold at different times, so that the time when tourists arrive at the overlapping part is staggered, thereby preventing congestion. The use of spatial block scenic spot ticketing and time dimension ticketing to build a distributed ticketing strategy can accurately manage the number of tourists at each location in the scenic area and avoid congestion when entering.

[0055] 2. The present invention makes a dual judgment on the number of visitors and the circulation speed of each block of scenic spots during the peak period of the scenic spot, and adjusts the ticket price of each block of scenic spots according to different judgment results. This can avoid congestion accidents caused by excessive number of people and slow circulation speed in the scenic spot, reduce the probability of safety hazards in the scenic spot, and increase the comfort of tourists' visits.

[0056] 3. After judging the number of visitors to each scenic spot during the off-peak period, the present invention closes the block scenic spots with fewer visitors and stops selling tickets, which can reduce the scenic spot's maintenance costs for the block scenic spots and avoid losses in the off-peak period. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a module distribution diagram of a scenic spot ticket management system based on big data analysis in the present invention;

[0058] Figure 2 This is a schematic diagram of the steps of a scenic spot ticket management method based on big data analysis of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0060] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution.

[0061] A scenic spot ticket management method based on big data analysis, the method comprising the following steps:

[0062] S100: Analyze tickets for scenic spots that require ticket purchase and have separate attractions within the scenic area, analyze all attraction route maps within the scenic area, extract overlapping marked routes to obtain block attractions, set ticket gates within each block attraction, set tickets for each block attraction, formulate different ticket sales time dimensions by analyzing the route spatial dimensions of each attraction, and formulate a distributed ticket sales strategy within the scenic area;

[0063] The specific steps for developing a distributed ticketing strategy in a scenic spot are:

[0064] S101, extract the scenic area tour route map in the central processor of the scenic area, mark each scenic spot in the scenic area in the tour route map according to the scenic area plan, collect the historical movement trajectory of tourists visiting each scenic spot to generate a route map for each scenic spot, and set the generated route map of all scenic spots in the scenic area as , Represents the 1st, 2nd, 3rd, ..., mth scenic spot route map generated in the scenic area, where m is a positive integer; all the generated scenic spot route maps are marked with different colors, and all the marked scenic spot route maps are overlapped and inverted to obtain the scenic spot route deduction map, and the color overlapping part and the independent part of the scenic spot route deduction map are extracted separately, and the extracted color overlapping part is set as , represents the 1st, 2nd, 3rd, ..., xth overlapping parts extracted from the scenic route deduction map, where x is a positive integer; let the extracted individual parts be , Represent the 1st, 2nd, 3rd, ..., yth individual parts extracted from the scenic route interpretation diagram, where y is a positive integer; each extracted individual part is used as each block attraction in the scenic area, and each block attraction includes the scenic area planned in the scenic area and the corresponding individual route; tickets are sold separately for each block attraction;

[0065] S102. Using each scenic spot route map before overlapping inversion, the overlapping portion extracted from the scenic spot route deduction map is divided into different spatial dimensions, where each spatial dimension contains a scenic spot route map; the walking speed of tourists in the scenic spot during the historical period is collected as Vb, and the total length Wz of the scenic spot route map, the length Wc of the overlapping portion, and the length Wd of the individual routes contained in the block scenic spot in each spatial dimension are extracted; and the time spent by tourists walking during the tour is calculated using the formula: In the formula, F represents the calculated walking time of tourists, W represents the length of the walking route, and Vb represents the walking speed of tourists during the tour; the total length Wz of the scenic route map in each spatial dimension, the length of the overlapping part Wc, and the length Wd of the individual routes contained in the block attractions are respectively input into W in the formula, and the walking time of tourists through the total length Wz of the scenic route map in each spatial dimension, the length Wh of the overlapping part, and the length Wd of the individual routes contained in the block attractions are calculated; the difference time of tourists walking in each spatial dimension is calculated, and the formula is:

[0066] ;

[0067] In the formula, Fc represents the difference in walking time of tourists in each spatial dimension, Fz represents the walking time of tourists passing the total length of the scenic route map in each spatial dimension, Fh represents the walking time of tourists passing the overlapping length of the scenic route map in each spatial dimension, and Fd represents the walking time of tourists passing the length of the individual routes contained in the block scenic spots in each spatial dimension. After calculation, the difference time of each spatial dimension is , represents the difference time of the calculated 1st, 2nd, 3rd, ..., mth dimensions;

[0068] S103. Extract the difference time in the two spatial dimensions in sequence and calculate the difference value Fcc. When Fcc≤Fh, it is determined that there is interference between the routes of the scenic spots in the corresponding two spatial dimensions, and the tickets for the scenic spots in the corresponding two spatial dimensions are sold in different time dimensions. The two ticket selling time dimensions are greater than Fh. When Fcc>Fh, it is determined that there is no interference between the routes of the scenic spots in the corresponding two spatial dimensions, and tickets are sold normally. After performing pairwise judgments on all spatial dimensions, a distributed ticketing strategy is constructed using block-based scenic spot ticketing and time-based ticketing.

[0069] The route maps of all scenic spots in the scenic area are marked and overlapped, and the overlapping parts and non-overlapping separate parts are extracted respectively. The separate parts are used as block attractions. After the tourist area in the scenic area is divided into blocks, tickets are sold separately for each attraction, and gates are set up. Only those who purchase tickets for the corresponding attractions can enter; this enables the scenic area to locate the visiting position of each tourist in the scenic area in real time, making it convenient to manage tourists in the scenic area.

[0070] The extracted overlapping parts represent the roads that need to be passed to go to different scenic spots. When the number of tourists is too large, the number of tourists who need to pass through the overlapping parts will greatly increase, and there is a high probability of congestion. Therefore, the arrival time of tourists at different scenic spots is calculated. When the arrival time of the overlapping part is less than the time of the overlapping part when going to two scenic spots, it proves that there are tourists going to different scenic spots at the same time in the overlapping part, causing congestion. Therefore, the tickets for the scenic spots where the arrival time of tourists is less than the overlapping part are divided into different sales times, so that the time of tourists arriving at the overlapping part is staggered, and congestion is not caused. The distributed ticketing strategy constructed by using spatial block scenic spot ticketing and time dimension ticketing can accurately manage the number of tourists at each location in the scenic area and avoid congestion.

[0071] S200, collecting the sales of scenic spot tickets in different time periods in history, and determining the peak and off-peak periods of the scenic spot by analyzing the sales;

[0072] The specific steps to determine the peak and off-peak periods of a scenic spot are:

[0073] S201. Collect the sales of scenic spot tickets in different time periods in history. , Represents the sales of scenic spot tickets collected in the 1st, 2nd, 3rd, ..., jth time period, where j is a positive integer; the collected sales are plotted in chronological order, and the peaks and troughs in the graph are extracted by analyzing the graph. Let the extracted peak value be , Indicates the 1st, 2nd, 3rd, ..., pth peak value of the extracted sales curve, and the extracted trough value is , Indicates the 1st, 2nd, 3rd, ..., pth trough value in the extracted sales curve graph, where p is a positive integer;

[0074] S202, judge the extracted peak values ​​and trough values, select the peak time corresponding to the maximum peak value among the p peak values ​​as the peak period of the scenic area, and select the trough time corresponding to the minimum value among the p trough values ​​as the low-peak period of the scenic area; find the time coordinates of the two inflection points of the maximum peak value in the curve graph respectively , Indicates the time coordinate of the previous inflection point of the maximum peak, Indicates the time coordinate of the next inflection point after the maximum peak; the time coordinates of the two inflection points of the minimum trough in the curve graph are , Indicates the time coordinate of the previous inflection point of the minimum trough, Indicates the time coordinate of the next inflection point after the minimum trough; the peak period of the scenic area is finally obtained as , the low peak period is .

[0075] According to the number of tourists in the scenic area at different times in history, the peak and off-peak periods of the scenic area can be determined. The ticketing of the scenic area can then be adjusted according to different time stages to achieve more humane adjustments.

[0076] S300: Collect the number of visitors to each scenic spot in the scenic area during normal operation at different historical periods, and define normal operation as when the scenic spot is profitable and not congested; calculate the normal number of visitors to each scenic spot in the scenic area based on the historical number of visitors; collect the flow time of tourists between different scenic spots in the scenic area when there is no congestion, and calculate the flow threshold when there is no congestion using the flow time and distance between different scenic spots;

[0077] The specific steps for calculating the normal number of people and circulation threshold for each block attraction are as follows:

[0078] S301. Collect the number of visitors to each scenic spot in the scenic area during normal business hours at different periods in history. , represents the number of visitors to the 1st, 2nd, 3rd, ..., kth block attractions in the scenic area during normal operation in the collected history, where k is a positive integer. The normal number of visitors to each block attraction in different periods is used to calculate the normal number of visitors to each block attraction. The formula is:

[0079] ;

[0080] ;

[0081] in the formula, in the formula, Indicates the lower limit of the normal number of people in each block of scenic spots. Indicates the upper limit of the normal number of people in each block of attractions; It represents the average number of visitors to each scenic spot in different periods. It represents the standard deviation of the number of visitors to each scenic spot in different periods; after calculation, the normal number of visitors to each scenic spot in the scenic area is , Indicates the normal number of people calculated for the 1st, 2nd, 3rd, ..., mth block attractions in the scenic area;

[0082] S302: Collect the historical data of the time it takes for tourists to travel between different scenic spots when there is no congestion in the scenic area. , It represents the circulation time of tourists between the 1st, 2nd, 3rd, ..., uth pairs of scenic spots when there is no congestion in the scenic area in the collected history, where u is a positive integer; the distance between each pair of scenic spots in each circulation time is , represents the distance between each pair of scenic spots in the collected 1st, 2nd, 3rd, ..., uth circulation time; the circulation threshold within the scenic area is calculated using the collected circulation time and relative distance, and the formula is: ; In the formula, Inv represents the calculated circulation threshold within the scenic area, Indicates the circulation speed of tourists in the scenic area, represents the average value of the calculated circulation velocity, Indicates the standard deviation of the calculated circulation velocity.

[0083] When judging whether there is congestion among tourists in a scenic area, the number of people in the scenic area cannot fully reflect the congestion situation. Even if there are a large number of tourists, congestion will not occur when the circulation speed is fast. Sometimes congestion may also occur when there are not many people but the circulation speed is slow. Therefore, the normal number of people interval and the circulation threshold are calculated for double judgment to avoid misjudgment of whether there is congestion in the scenic area and make the judgment more accurate.

[0084] S400: When purchasing tickets in the ticket purchasing channel, a tourist can arbitrarily select a block of scenic spots within the scenic area to purchase tickets for each block. After entering the scenic area using the ticket, the tourist then uses the ticket to verify and register at the gate of each block of scenic spots within the scenic area before entering. During peak hours, the central processor of the scenic area records the number of visitors in each block of scenic spots in real time and calculates the real-time circulation speed of tourists.

[0085] During peak hours, the central processor of the scenic area records the number of visitors in each block of scenic spots in real time and calculates the real-time circulation speed of tourists. The specific steps are as follows:

[0086] S401. When purchasing tickets in the ticket purchasing channel, tourists can arbitrarily choose the block attractions in the scenic area to purchase tickets in blocks. After entering the scenic area with the tickets, they can use the tickets to check and register at the gate of each block attraction to enter. During the peak period, the central processor of the scenic area records the number of visitors in each block attraction in real time. , Indicates the number of visitors to the 1st, 2nd, 3rd, ..., mth block attractions in the scenic area recorded in real time;

[0087] S402. Analyze the real-time number of visitors in each scenic spot, calculate the difference in the number of visitors between each two scenic spots at every time t1, and calculate the real-time tourist circulation speed in the scenic area. The formula is: S402. Analyze the real-time number of visitors in each scenic spot, calculate the difference in the number of visitors between each two scenic spots at every time t1, and calculate the real-time tourist circulation speed in the scenic area. The formula is: ;

[0088] In the formula, Vs represents the real-time circulation speed of tourists in the scenic area, Ss and represents the number of visitors in the two block attractions, L represents the distance between the two block attractions, and t1 represents the calculation time interval.

[0089] S500: Analyze the real-time number of visitors in each scenic spot using the normal number interval, analyze the real-time tourist flow rate using the flow threshold, and then determine whether there is a congestion risk in the scenic spot and issue an early warning. Use the early warning information to adjust the scenic spot ticketing;

[0090] The specific steps for adjusting scenic spot ticketing using early warning information are as follows:

[0091] S501, using the normal number of people interval to analyze the real-time number of visitors in each block of scenic spots, using the flow threshold to analyze the real-time flow speed of tourists, when and When the corresponding block of scenic spots is judged to have congestion risks, the scenic area center processor issues an early warning and stops the ticket sales for the corresponding block of scenic spots; when but When , it is determined that the corresponding block of attractions is full and the number of tickets sold for the corresponding block of attractions is reduced; when and When , it is determined that there is a lack of tourists for the corresponding block attractions, and the number of tickets sold for the corresponding block attractions is increased; when but When the corresponding block of attractions is judged to be normal, the ticket sales of the corresponding block of attractions will not be adjusted.

[0092] After making a double assessment of the number of visitors and the circulation speed of each scenic spot during the peak period of the scenic area, the ticket price of each scenic spot is adjusted according to different judgment results. This can avoid congestion accidents caused by excessive number of people and slow circulation speed in the scenic area, reduce the probability of safety hazards in the scenic area, and increase the comfort of tourists' visits.

[0093] S600: During the off-peak period of the scenic area, the number of visitors to each sub-area attraction in the scenic area is determined by using the normal business interval, and the ticket prices of different sub-area attractions are adjusted according to the number of visitors.

[0094] The specific steps for adjusting the ticket prices for different attractions according to the number of visitors are as follows:

[0095] S601, during the off-peak period of the scenic area, use the normal business interval to judge the number of visitors to each block of scenic spots in the scenic area, and record the number of visitors to each block of scenic spots during the off-peak period as Sd in real time. When the corresponding block of attractions is judged to be not profitable, the corresponding block of attractions is closed and ticket sales for the corresponding block of attractions are stopped; when When the corresponding block attractions are judged to be profitable, they are opened normally and the ticket prices of the corresponding attractions are increased.

[0096] After judging the number of visitors to each scenic spot during the off-peak period, the scenic spots with fewer visitors can be closed and ticket sales can be stopped. This can reduce the scenic spot's maintenance costs for the blocks and avoid losses during the off-peak period.

[0097] A scenic spot ticket management system based on big data analysis, which includes a data collection module, a distributed ticket sales module, a visitor data analysis module, a real-time visitor data calculation module and a ticket adjustment module;

[0098] The data collection module is used to collect the number, time and circulation data of tourists visiting the scenic area in history;

[0099] The distributed ticketing module is used to analyze the route map of each scenic spot in the scenic area, extract each block of scenic spots after marking overlaps, and divide them into different spatial dimensions, and calculate the time dimension of the scenic spot ticket for each spatial dimension;

[0100] The tourist data analysis module is used to calculate the normal number of visitors and the circulation threshold when visiting different scenic spots in the scenic area;

[0101] The real-time tourist data calculation module is used to calculate the real-time number of visitors and circulation speed in each block of scenic spots in the scenic area;

[0102] The ticket adjustment module is used to adjust the ticket of each sub-block attraction after judging the number of visitors and the circulation speed in each sub-block attraction in real time by using the normal number of people interval and circulation speed.

[0103] The tourist data analysis module includes a normal number interval calculation unit and a circulation threshold calculation unit;

[0104] The normal number interval calculation unit is used to analyze and calculate the number of tourists visiting each block of attractions in the scenic area in history to obtain the normal number interval of each block of attractions;

[0105] The circulation threshold calculation unit is used to calculate the circulation threshold in the scenic area by using the collected circulation time and relative distance.

[0106] The ticket adjustment module includes a peak period adjustment unit and a low peak period adjustment unit;

[0107] The peak period adjustment unit is used to calculate the number of visitors and the circulation speed of each scenic spot during the peak period, and adjust the ticket price of each block scenic spot by analyzing and judging the number of visitors and the circulation speed;

[0108] The off-peak period adjustment unit is used to calculate the number of visitors to each scenic spot during the off-peak period, analyze the number of visitors to the scenic spot, and adjust the ticket price of each block scenic spot during the off-peak period.

[0109] Example: After analyzing the tourist area in a certain scenic spot, the block attractions are obtained as attraction 1, attraction 2 and attraction 3;

[0110] The normal number of people in each scenic spot is calculated as (100, 200), (150, 300), and (50, 80). The flow threshold of tourists in the scenic area is 10m / min.

[0111] During peak hours, the number of visitors to each scenic spot is calculated in real time to be 140, 320, and 90 respectively. The speed of tourists in the scenic area is 8 m / min. After determining that there are not enough tourists in block 1, the number of tickets sold for the corresponding block is increased. Attractions 2 and 3 are at risk of congestion, so ticket sales are stopped.

[0112] During the off-peak period, the number of visitors to each block of attractions is calculated to be 10, 30, and 50; after judgment, attractions 1 and 2 are closed, and attraction 3 is open as usual.

[0113] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A scenic spot ticket management method based on big data analysis, characterized by: The method comprises the following steps: S100: Analyze tickets for scenic spots that require ticket purchase and have separate attractions within the scenic area, analyze all attraction route maps within the scenic area, extract overlapping marked routes to obtain block attractions, set ticket gates within each block attraction, set tickets for each block attraction, formulate different ticket sales time dimensions by analyzing the route spatial dimensions of each attraction, and formulate a distributed ticket sales strategy within the scenic area; The specific steps are: S101, collecting historical movement trajectories of tourists visiting each scenic spot to generate a route map for each scenic spot, marking all generated scenic spot route maps with different colors, overlapping and inverting all marked scenic spot route maps to obtain a scenic spot route deduction map, extracting the color overlapping parts and independent parts of the scenic spot route deduction map, and using each independent part extracted as each block scenic spot in the scenic spot; S102. Using each scenic spot route map before overlapping inversion, the overlapping portion extracted from the scenic spot route deduction map is divided into different spatial dimensions, where each spatial dimension contains a scenic spot route map; the walking speed of tourists in the scenic spot during the historical period is collected as Vb, and the total length Wz of the scenic spot route map, the length Wc of the overlapping portion, and the length Wd of the individual routes contained in the block scenic spot in each spatial dimension are extracted; the time F spent by tourists walking during the tour is calculated using the formula: , where W = Wz or Wc or Wd, calculate the difference in walking time Fc of tourists in each spatial dimension, the formula is: ； In the formula, Fz, Fh, and Fd represent the walking time of tourists through the total length of the scenic spot route map, the length of the overlapping part, and the length of the individual routes contained in the block attractions in each spatial dimension respectively; the difference time in each spatial dimension is obtained through calculation; S103. Sequentially extract the time difference between the two spatial dimensions and calculate the difference Fcc. When Fcc≤Fh, sell the attraction tickets in the two spatial dimensions in different time dimensions, and the two ticketing time dimensions are greater than Fh. When Fcc>Fh, sell tickets normally. After performing pairwise judgments on all spatial dimensions, a distributed ticketing strategy is constructed using block attraction ticketing and time dimension ticketing. S200, collecting the sales of scenic spot tickets in different time periods in history, and determining the peak and off-peak periods of the scenic spot by analyzing the sales; S300: Collect the number of visitors to each scenic spot in the scenic area during normal operation at different historical periods, and define normal operation as when the scenic spot is profitable and not congested; calculate the normal number of visitors to each scenic spot in the scenic area based on the historical number of visitors; collect the flow time of tourists between different scenic spots in the scenic area when there is no congestion, and calculate the flow threshold when there is no congestion using the flow time and distance between different scenic spots; S400: When purchasing tickets in the ticket purchasing channel, a tourist can arbitrarily select a block of scenic spots within the scenic area to purchase tickets for each block. After entering the scenic area using the ticket, the tourist then uses the ticket to verify and register at the gate of each block of scenic spots within the scenic area before entering. During peak hours, the central processor of the scenic area records the number of visitors in each block of scenic spots in real time and calculates the real-time circulation speed of tourists. S500: Analyze the real-time number of visitors in each scenic spot using the normal number interval, analyze the real-time tourist flow rate using the flow threshold, and then determine whether there is a congestion risk in the scenic spot and issue an early warning. Use the early warning information to adjust the scenic spot ticketing; S600: During the off-peak period of the scenic area, the number of visitors to each sub-area attraction in the scenic area is determined by using the normal business interval, and the ticket prices of different sub-area attractions are adjusted according to the number of visitors.

2. A scenic spot ticket management method based on big data analysis according to claim 1, characterized in that: The specific steps of determining the peak period and off-peak period of the scenic spot in S200 are as follows: S201. Collect the sales of scenic spot tickets in different time periods in history. , Represents the sales of scenic spot tickets collected in the 1st, 2nd, 3rd, ..., jth time period, where j is a positive integer; the collected sales are plotted in chronological order, and the peaks and troughs in the graph are extracted by analyzing the graph. Let the extracted peak value be , Indicates the 1st, 2nd, 3rd, ..., pth peak value of the extracted sales curve, and the extracted trough value is , Indicates the 1st, 2nd, 3rd, ..., pth trough value in the extracted sales curve graph, where p is a positive integer; S202, judge the extracted peak values ​​and trough values, select the peak time corresponding to the maximum peak value among the p peak values ​​as the peak period of the scenic area, and select the trough time corresponding to the minimum value among the p trough values ​​as the low-peak period of the scenic area; find the time coordinates of the two inflection points of the maximum peak value in the curve graph respectively as , Indicates the time coordinate of the previous inflection point of the maximum peak, Indicates the time coordinate of the next inflection point after the maximum peak; the time coordinates of the two inflection points of the minimum trough in the curve graph are , Indicates the time coordinate of the previous inflection point of the minimum trough, Indicates the time coordinate of the next inflection point after the minimum trough; the peak period of the scenic area is finally obtained as , the low peak period is .

3. A scenic spot ticket management method based on big data analysis according to claim 1, characterized in that: The specific steps of calculating the normal number of people interval and circulation threshold of each block attraction in S300 are: S301. Collect the number of visitors to each scenic spot in the scenic area during normal business hours at different periods in history. , represents the number of visitors to the 1st, 2nd, 3rd, ..., kth block attractions in the scenic area during normal operation in the collected history, where k is a positive integer. The normal number of visitors to each block attraction in different periods is used to calculate the normal number of visitors to each block attraction. The formula is: ； ； In the formula, Indicates the lower limit of the normal number of people in each block of scenic spots. Indicates the upper limit of the normal number of people in each block of attractions; It represents the average number of visitors to each scenic spot in different periods. It represents the standard deviation of the number of visitors to each scenic spot in different periods; after calculation, the normal number of visitors to each scenic spot in the scenic area is , Indicates the normal number of people calculated for the 1st, 2nd, 3rd, ..., mth block attractions in the scenic area; S302: Collect the historical data of the time it takes for tourists to travel between different scenic spots when there is no congestion in the scenic area. , It represents the circulation time of tourists between the 1st, 2nd, 3rd, ..., uth pairs of scenic spots when there is no congestion in the scenic area in the collected history, where u is a positive integer; the distance between each pair of scenic spots in each circulation time is , represents the distance between each pair of scenic spots in the collected 1st, 2nd, 3rd, ..., uth circulation time; the circulation threshold within the scenic area is calculated using the collected circulation time and relative distance, and the formula is: ; In the formula, Inv represents the calculated circulation threshold within the scenic area, Indicates the circulation speed of tourists in the scenic area, represents the average value of the calculated circulation velocity, Indicates the standard deviation of the calculated circulation velocity.

4. A scenic spot ticket management method based on big data analysis according to claim 1, characterized in that: The specific steps of the scenic area central processor recording the number of visitors in each scenic spot in real time during the peak period and calculating the real-time circulation speed of tourists in S400 are as follows: S401. When purchasing tickets in the ticket purchasing channel, tourists can arbitrarily choose the block attractions in the scenic area to purchase tickets in blocks. After entering the scenic area with the tickets, they can use the tickets to check and register at the gate of each block attraction to enter. During the peak period, the central processor of the scenic area records the number of visitors in each block attraction in real time. , Indicates the number of visitors to the 1st, 2nd, 3rd, ..., mth block attractions in the scenic area recorded in real time; S402: Analyze the real-time number of visitors in each scenic spot, calculate the difference in the number of visitors between each two scenic spots at every time t1, and calculate the real-time tourist flow rate in the scenic spot. The formula is: ; In the formula, Vs represents the real-time circulation speed of tourists in the scenic area, Ss and represents the number of visitors in the two block attractions, L represents the distance between the two block attractions, and t1 represents the calculation time interval.

5. A scenic spot ticket management method based on big data analysis according to claim 4, characterized in that: The specific steps of adjusting the scenic spot ticketing using the early warning information in S500 are: S501, using the normal number of people interval to analyze the real-time number of visitors in each block of scenic spots, using the flow threshold to analyze the real-time flow speed of tourists, when and When the corresponding block of scenic spots is judged to have congestion risks, the scenic area center processor issues an early warning and stops the ticket sales for the corresponding block of scenic spots; when but When , it is determined that the corresponding block of attractions is full and the number of tickets sold for the corresponding block of attractions is reduced; when and When , it is determined that there is a lack of tourists for the corresponding block attractions, and the number of tickets sold for the corresponding block attractions is increased; when but When , the corresponding block scenic spot is judged to be normal, and the ticket sales of the corresponding block scenic spot are not adjusted. Inv represents the calculated circulation threshold within the scenic spot.

6. A scenic spot ticket management method based on big data analysis according to claim 3, characterized in that: The specific steps of adjusting the different block attraction tickets according to the number of visitors in S600 are: S601, during the off-peak period of the scenic area, use the normal business interval to judge the number of visitors to each block of scenic spots in the scenic area, and record the number of visitors to each block of scenic spots during the off-peak period as Sd in real time. When the corresponding block of attractions is judged to be not profitable, the corresponding block of attractions is closed and ticket sales for the corresponding block of attractions are stopped; when When the corresponding block attractions are judged to be profitable, they are opened normally and the ticket prices of the corresponding attractions are increased.

7. A scenic spot ticket management system based on big data analysis, the system being used to implement the scenic spot ticket management method based on big data analysis as claimed in any one of claims 1 to 6, characterized in that: The scenic spot ticket management system includes data collection module, distributed ticketing module, visitor data analysis module, real-time visitor data calculation module and ticket adjustment module; The data collection module is used to collect the number, time and circulation data of tourists visiting the scenic area in history; The distributed ticketing module is used to analyze the route map of each scenic spot in the scenic area, extract each block of scenic spots after marking overlaps, and divide them into different spatial dimensions, and calculate the time dimension of the scenic spot ticket for each spatial dimension; The tourist data analysis module is used to calculate the normal number of visitors and the circulation threshold when visiting different scenic spots in the scenic area; The real-time tourist data calculation module is used to calculate the real-time number of visitors and circulation speed in each block of scenic spots in the scenic area; The ticket adjustment module is used to adjust the ticket of each sub-block attraction after judging the number of visitors and the circulation speed in each sub-block attraction in real time by using the normal number of people interval and circulation speed.

8. The scenic spot ticket management system based on big data analysis according to claim 7, characterized in that: The tourist data analysis module includes a normal number interval calculation unit and a circulation threshold calculation unit; The normal number interval calculation unit is used to analyze and calculate the number of tourists visiting each block of attractions in the scenic area in history to obtain the normal number interval of each block of attractions; The circulation threshold calculation unit is used to calculate the circulation threshold in the scenic area by using the collected circulation time and relative distance.

9. The scenic spot ticket management system based on big data analysis according to claim 7, characterized in that: The ticket adjustment module includes a peak period adjustment unit and a low peak period adjustment unit; The peak period adjustment unit is used to calculate the number of visitors and the circulation speed of each scenic spot during the peak period, and adjust the ticket price of each block scenic spot by analyzing and judging the number of visitors and the circulation speed; The off-peak period adjustment unit is used to calculate the number of visitors to each scenic spot during the off-peak period, analyze the number of visitors to the scenic spot, and adjust the ticket price of each block scenic spot during the off-peak period.

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