Scenic spot ticket business management system and method based on big data analysis

Abstract

The invention discloses a scenic spot ticket business management system and method based on big data analysis, and relates to the technical field of ticket business management.The scenic spot ticket business management method comprises the steps that all scenic spot route maps in a scenic spot are analyzed, segmented scenic spots are extracted by marking overlapped routes, a ticket is set for each segmented scenic spot, and a distributed ticket selling strategy is formulated in the scenic spot; the peak period and the low peak period of the scenic area are determined by analyzing the sales; calculating a normal people number interval of each scenic spot in the scenic area according to the number of visitors in the history; calculating a circulation threshold value when the tourists are not congested by using the circulation time and distance between the different block scenic spots; whether congestion risks exist in the partitioned scenic spots or not is judged according to the normal people number interval and the circulation threshold value, early warning is carried out, and scenic spot ticket business is adjusted according to early warning information; and in the low peak period of the scenic area, judging the number of visitors of each block scenic spot in the scenic area by using the normal operating interval, and adjusting different block scenic spot tickets 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 kinds of tickets were paper versions, so it was difficult to manage paper tickets, and there were problems such as difficulty in buying tickets, scalpers reselling tickets, and difficulty in managing tourists. With the popularization of the Internet, a basic network environment has been provided for the intelligent management of scenic spot ticketing. Scenic spots can sell tickets to tourists through official websites, online travel platforms and other channels. Tourists can buy tickets anywhere there is a network, breaking the limitations of time and space. Modern ticket gates are equipped with a variety of advanced technologies, such as barcode recognition, QR code scanning, ID card recognition, and face recognition. The application of these technologies makes the ticket checking process faster and more accurate, and can effectively prevent fake tickets and ticket evasion. The problem of "scalpers reselling tickets" has been greatly eliminated, but after the informatization, with the convenience of people buying tickets, it is difficult to manage a large number of tourists. Crowded people often appear in scenic spots, which greatly affects the normal business of scenic spots and tourists' tour experience; and when the scenic spot is large, scenic spot managers are not easy to track and manage tourists entering the scenic spot, which poses a great management risk. 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: A scenic spot ticket management method based on big data analysis, the method comprising the following steps: S100, analyzing the tickets for scenic spots that need to be purchased and have separate attractions in the scenic spot, analyzing the route maps of all attractions in the scenic spot, extracting the overlapping marked routes to obtain block attractions, setting ticket gates in each block attraction, setting tickets for each block attraction, formulating different ticket sales time dimensions by analyzing the route space dimension of each attraction, and formulating a distributed ticket sales strategy in the scenic spot; Furthermore, the specific steps for formulating a distributed ticketing strategy in a scenic spot are: S101, extracting the scenic spot tour route map in the central processor of the scenic spot, marking each scenic spot in the scenic spot in the tour route map according to the scenic spot planning, collecting the action trajectory of tourists visiting each scenic spot in history to generate a route map for each scenic spot, and assuming that the generated route map of all scenic spots in the scenic spot is , 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 interpretation map, and the color overlapping part and the separate part of the scenic spot route interpretation 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 interpretation map, where x is a positive integer; let the extracted single part be , Indicates the 1st, 2nd, 3rd, ..., yth individual parts extracted from the scenic route interpretation map, 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 spots planned in the scenic area and the corresponding individual routes; tickets are sold separately for each block attraction; 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 in the scenic spot during the tour in 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 walking during the tour is calculated, and the formula is: , where 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 visit; 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 route contained in the block scenic spot 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 route contained in the block scenic spot is calculated; the difference time of tourists walking in each spatial dimension is calculated, and the formula is: ; In the formula, Fc represents the difference time of tourists walking in each spatial dimension, Fz represents the walking time of tourists passing the total length of the scenic spot route map in each spatial dimension, Fh represents the walking time of tourists passing the overlapping length of the scenic spot 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; S103, extract the difference time in the two spatial dimensions in turn to calculate the difference Fcc. When Fcc≤Fh, it is determined that there is interference in the scenic spot routes in the corresponding two spatial dimensions, and the scenic spot tickets 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 in the scenic spot routes in the corresponding two spatial dimensions, and tickets are sold normally; after performing pairwise judgments on all spatial dimensions, a distributed ticket selling strategy is constructed by using block scenic spot ticket selling and time dimension ticket selling.

[0005] 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 scenic spots. After the tourist areas in the scenic area are divided into blocks, tickets are sold separately for each scenic spot, and gates are set up. Only those who buy tickets for the corresponding attractions can enter; the scenic area can locate the visiting position of each tourist in the scenic area in real time, which is convenient for the management of tourists in the scenic area.

[0006] 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 great probability of congestion. Therefore, the time for tourists to arrive at the overlapping parts when going to different scenic spots is calculated. When the time to arrive at the overlapping part when going to two scenic spots is less than the time of the overlapping part, it proves that there will be tourists going to different scenic spots at the same time in the overlapping part, causing congestion. Therefore, the tickets for scenic spots whose arrival time is less than the overlapping part are divided into different sales times, so that the time for tourists to arrive at the overlapping part is staggered, and congestion cannot be caused. Using space-blocked scenic spot ticketing and time-dimensioned ticketing to build a distributed ticketing strategy can accurately manage the number of tourists at each location in the scenic area to avoid congestion. S200, collecting the sales of scenic spot tickets in different time periods in history, and determining the peak period and off-peak period of the scenic spot by analyzing the sales; Furthermore, the specific steps to determine the peak and off-peak periods of the scenic area are: 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 , represents 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, judging the extracted peak values ​​and trough values, selecting the peak time corresponding to the maximum peak value among the p peak values ​​as the peak period of the scenic spot, and selecting the trough time corresponding to the minimum value among the p trough values ​​as the low peak period of the scenic spot; finding 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 of 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 of the minimum trough; the peak period of the scenic spot is finally obtained as , the low peak period is .

[0007] 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 a more humane adjustment.

[0008] S300, collecting the number of visitors to each block scenic spot in the scenic area during normal operation at different periods in history, and taking the block scenic spot as operating normally when there is profit and no congestion; calculating the normal number interval of each scenic spot in the scenic area according to the number of visitors in history; collecting the flow time of tourists between different block scenic spots when no congestion occurs in the scenic area in history, and calculating the flow threshold of tourists when no congestion occurs by using the flow time and distance between different block scenic spots; Furthermore, the specific steps for calculating the normal number of people interval and circulation threshold of each block attraction are as follows: 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 interval of each block attraction is calculated using the number of visitors to each block attraction collected in different periods, and 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 scenic spots; 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 in the calculated 1st, 2nd, 3rd, ..., mth block attractions in the scenic area; S302: Collect the circulation time of tourists between different scenic spots in the history 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 in the collected history when there is no congestion in the scenic spot, 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 block attractions in the collected 1st, 2nd, 3rd, ..., uth circulation time; the circulation threshold in 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 in the scenic area, Indicates the circulation speed of tourists in the scenic area. represents the average value of the calculated circulation velocity, Represents the standard deviation of the calculated circulation velocity.

[0009] 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 occur even when there are not many people but the circulation speed is slow. Therefore, the normal number 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.

[0010] S400, when purchasing tickets in the ticket purchasing channel, tourists arbitrarily select block attractions in the scenic area to purchase tickets in blocks, enter the scenic area with the tickets, and then enter each block attraction in the scenic area after verification and registration at the gate of the block attraction; during the peak period, the central processor of the scenic area records the number of visitors in each block attraction in real time and calculates the real-time circulation speed of tourists; Furthermore, during the peak period, the central processor of the scenic spot records the number of visitors in each block of scenic spots in real time and calculates the specific steps of the real-time circulation speed of tourists as follows: S401. When purchasing tickets in the ticket purchasing channel, tourists arbitrarily select block attractions in the scenic area to purchase tickets in blocks. After entering the scenic area with the ticket, when entering each block attraction in the scenic area, the ticket is used to verify and register at the gate of the 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, analyzing the real-time number of visitors in each sub-block scenic spot, calculating the difference in the number of visitors between every two sub-block scenic spots at every time t1, and calculating the real-time tourist flow speed 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.

[0011] S500, using the normal number interval to analyze the real-time number of visitors in each sub-block scenic spot, using the flow threshold to analyze the real-time flow speed of tourists, then determining whether there is a congestion risk in the sub-block scenic spot and issuing an early warning, and using the early warning information to adjust the scenic spot ticketing; Furthermore, the specific steps for adjusting scenic spot ticketing using early warning information are as follows: 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 scenic spot is judged to have a congestion risk, the scenic spot central processor issues an early warning and stops ticket sales for the corresponding block scenic spot; but When , it is determined that the corresponding sub-block scenic spot is full, and the number of tickets sold for the corresponding sub-block scenic spot is reduced; when and When , it is determined that the corresponding sub-block scenic spot lacks tourists, and the number of tickets sold for the corresponding sub-block scenic spot 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.

[0012] After making a double judgment on the number of visitors and the circulation speed of each block of scenic spots during the peak period of the scenic spot, the ticket price of each block of scenic spots is adjusted according to different judgment results. This can avoid congestion accidents caused by too many 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.

[0013] S600: During the off-peak period of the scenic spot, the number of visitors to each sub-block scenic spot in the scenic spot is determined by using the normal business interval, and the ticket prices of different sub-block scenic spots are adjusted according to the number of visitors.

[0014] Furthermore, the specific steps for adjusting the ticket prices of different scenic spots according to the number of visitors are as follows: S601, during the off-peak period of the scenic spot, the number of visitors to each sub-block scenic spot in the scenic spot is judged by using the normal business interval, and the number of visitors to each sub-block scenic spot during the off-peak period is recorded in real time as Sd. When the corresponding block attractions are judged to be not profitable, the corresponding block attractions are closed and ticket sales for the corresponding block 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.

[0015] 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 to reduce the scenic spot's maintenance costs for the block attractions and avoid losses during the off-peak period. A scenic spot ticket management system based on big data analysis, the scenic spot ticket management system includes a data collection module, a distributed ticketing module, a tourist data analysis module, a real-time tourist data calculation module and a ticket adjustment module; The data collection module is used to collect the number, time and circulation data of tourists visiting the scenic spot in history; The distributed ticketing module is used to analyze the route map of each scenic spot in the scenic area, extract each block scenic spot after marking overlap; and distinguish it into different spatial dimensions, and calculate the time dimension of the scenic spot ticket in 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 visitors interval and circulation speed.

[0016] 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 attraction in the scenic area in history to obtain the normal number interval of each block attraction; 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.

[0017] 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 sub-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.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 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 the block scenic spots. After the tourist area in the scenic area is divided into blocks, each scenic spot is sold separately, and gates are set. Only by purchasing the corresponding scenic spot ticket can you enter; the scenic area can locate the tourist location of each tourist in the scenic area in real time, which is convenient for the management of tourists in the scenic area and increases the flexibility of the scenic area's business. The tickets for the scenic spots whose arrival time is less than the overlapping part are divided into different sales times, so that the time when tourists arrive at the overlapping part is staggered, and congestion cannot be caused; the distributed ticketing strategy constructed by using space-blocked scenic spot ticketing and time-dimensioned ticketing can accurately manage the number of tourists at each location in the scenic area to avoid congestion when entering.

[0019] 2. The present invention makes a double judgment on the number of visitors and the circulation speed of each sub-block scenic spot during the peak period of the scenic spot, and adjusts the ticket price of each sub-block scenic spot according to different judgment results, which 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.

[0020] 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 cost for the block scenic spots and avoid losses in the scenic spot during the off-peak period. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a module distribution diagram of a scenic spot ticket management system based on big data analysis in the present invention; Figure 2 The present invention is a schematic diagram of the steps of a scenic spot ticket management method based on big data analysis. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0023] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution. A scenic spot ticket management method based on big data analysis, the method comprising the following steps: S100, analyzing the tickets for scenic spots that need to be purchased and have separate attractions in the scenic spot, analyzing the route maps of all attractions in the scenic spot, extracting the overlapping marked routes to obtain block attractions, setting ticket gates in each block attraction, setting tickets for each block attraction, formulating different ticket sales time dimensions by analyzing the route space dimension of each attraction, and formulating a distributed ticket sales strategy in the scenic spot; The specific steps for formulating a distributed ticketing strategy in a scenic spot are: S101, extracting the scenic spot tour route map in the central processor of the scenic spot, marking each scenic spot in the scenic spot in the tour route map according to the scenic spot planning, collecting the action trajectory of tourists visiting each scenic spot in history to generate a route map for each scenic spot, and assuming that the generated route map of all scenic spots in the scenic spot is , 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 interpretation map, and the color overlapping part and the separate part of the scenic spot route interpretation 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 interpretation map, where x is a positive integer; let the extracted single part be , Indicates the 1st, 2nd, 3rd, ..., yth individual parts extracted from the scenic route interpretation map, 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 spots planned in the scenic area and the corresponding individual routes; tickets are sold separately for each block attraction; 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 in the scenic spot during the tour in 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 walking during the tour is calculated, and the formula is: , where 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 visit; 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 route contained in the block scenic spot 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 route contained in the block scenic spot is calculated; the difference time of tourists walking in each spatial dimension is calculated, and the formula is: ; In the formula, Fc represents the difference time of tourists walking in each spatial dimension, Fz represents the walking time of tourists passing the total length of the scenic spot route map in each spatial dimension, Fh represents the walking time of tourists passing the overlapping length of the scenic spot 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; S103, extract the difference time in the two spatial dimensions in turn to calculate the difference Fcc. When Fcc≤Fh, it is determined that there is interference in the scenic spot routes in the corresponding two spatial dimensions, and the scenic spot tickets 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 in the scenic spot routes in the corresponding two spatial dimensions, and tickets are sold normally; after performing pairwise judgments on all spatial dimensions, a distributed ticket selling strategy is constructed by using block scenic spot ticket selling and time dimension ticket selling.

[0024] 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 scenic spots. After the tourist areas in the scenic area are divided into blocks, tickets are sold separately for each scenic spot, and gates are set up. Only those who buy tickets for the corresponding attractions can enter; the scenic area can locate the visiting position of each tourist in the scenic area in real time, which is convenient for the management of tourists in the scenic area.

[0025] 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 great probability of congestion. Therefore, the time for tourists to arrive at the overlapping parts when going to different scenic spots is calculated. When the time to arrive at the overlapping part when going to two scenic spots is less than the time of the overlapping part, it proves that there will be tourists going to different scenic spots at the same time in the overlapping part, causing congestion. Therefore, the tickets for scenic spots whose arrival time is less than the overlapping part are divided into different sales times, so that the time for tourists to arrive at the overlapping part is staggered, and congestion cannot be caused. Using space-blocked scenic spot ticketing and time-dimensioned ticketing to build a distributed ticketing strategy can accurately manage the number of tourists at each location in the scenic area to avoid congestion. S200, collecting the sales of scenic spot tickets in different time periods in history, and determining the peak period and off-peak period of the scenic spot by analyzing the sales; The specific steps to determine the peak and off-peak periods of the scenic area are: 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 , represents 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, judging the extracted peak values ​​and trough values, selecting the peak time corresponding to the maximum peak value among the p peak values ​​as the peak period of the scenic spot, and selecting the trough time corresponding to the minimum value among the p trough values ​​as the low peak period of the scenic spot; finding 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 of 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 of the minimum trough; the peak period of the scenic spot is finally obtained as , the low peak period is .

[0026] 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 a more humane adjustment.

[0027] S300, collecting the number of visitors to each block scenic spot in the scenic area during normal operation at different periods in history, and taking the block scenic spot as operating normally when there is profit and no congestion; calculating the normal number interval of each scenic spot in the scenic area according to the number of visitors in history; collecting the flow time of tourists between different block scenic spots when no congestion occurs in the scenic area in history, and calculating the flow threshold of tourists when no congestion occurs by using the flow time and distance between different block scenic spots; The specific steps for calculating the normal number of people and circulation threshold of each block attraction are as follows: 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 interval of each block attraction is calculated using the number of visitors to each block attraction collected in different periods, and 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 scenic spots; 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 in the calculated 1st, 2nd, 3rd, ..., mth block attractions in the scenic area; S302: Collect the circulation time of tourists between different scenic spots in the history 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 in the collected history when there is no congestion in the scenic spot, 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 block attractions in the collected 1st, 2nd, 3rd, ..., uth circulation time; the circulation threshold in 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 in the scenic area, Indicates the circulation speed of tourists in the scenic area. represents the average value of the calculated circulation velocity, Represents the standard deviation of the calculated circulation velocity.

[0028] 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 occur even when there are not many people but the circulation speed is slow. Therefore, the normal number 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.

[0029] S400, when purchasing tickets in the ticket purchasing channel, tourists arbitrarily select block attractions in the scenic area to purchase tickets in blocks, enter the scenic area with the tickets, and then enter each block attraction in the scenic area after verification and registration at the gate of the block attraction; during the peak period, the central processor of the scenic area records the number of visitors in each block attraction in real time and calculates the real-time circulation speed of tourists; During the peak period, the central processor of the scenic spot 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: S401. When purchasing tickets in the ticket purchasing channel, tourists arbitrarily select block attractions in the scenic area to purchase tickets in blocks. After entering the scenic area with the ticket, when entering each block attraction in the scenic area, the ticket is used to verify and register at the gate of the 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, analyzing the real-time number of visitors in each sub-block scenic spot, calculating the difference in the number of visitors between every two sub-block scenic spots at every time t1, and calculating the real-time tourist flow speed 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.

[0030] S500, using the normal number interval to analyze the real-time number of visitors in each sub-block scenic spot, using the flow threshold to analyze the real-time flow speed of tourists, then determining whether there is a congestion risk in the sub-block scenic spot and issuing an early warning, and using the early warning information to adjust the scenic spot ticketing; The specific steps to adjust scenic spot ticketing using early warning information are as follows: 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 scenic spot is judged to have a congestion risk, the scenic spot central processor issues an early warning and stops ticket sales for the corresponding block scenic spot; but When , it is determined that the corresponding sub-block scenic spot is full, and the number of tickets sold for the corresponding sub-block scenic spot is reduced; when and When , it is determined that the corresponding sub-block scenic spot lacks tourists, and the number of tickets sold for the corresponding sub-block scenic spot 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.

[0031] After making a double judgment on the number of visitors and the circulation speed of each block of scenic spots during the peak period of the scenic spot, the ticket price of each block of scenic spots is adjusted according to different judgment results. This can avoid congestion accidents caused by too many 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.

[0032] S600: During the off-peak period of the scenic spot, the number of visitors to each sub-block scenic spot in the scenic spot is determined by using the normal business interval, and the ticket prices of different sub-block scenic spots are adjusted according to the number of visitors.

[0033] The specific steps to adjust the ticket prices for different attractions according to the number of visitors are as follows: S601, during the off-peak period of the scenic spot, the number of visitors to each sub-block scenic spot in the scenic spot is judged by using the normal business interval, and the number of visitors to each sub-block scenic spot during the off-peak period is recorded in real time as Sd. When the corresponding block attractions are judged to be not profitable, the corresponding block attractions are closed and ticket sales for the corresponding block 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.

[0034] 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 to reduce the scenic spot's maintenance costs for the block attractions and avoid losses during the off-peak period. A scenic spot ticket management system based on big data analysis, the scenic spot ticket management system includes a data collection module, a distributed ticketing module, a tourist data analysis module, a real-time tourist data calculation module and a ticket adjustment module; The data collection module is used to collect the number, time and circulation data of tourists visiting the scenic spot in history; The distributed ticketing module is used to analyze the route map of each scenic spot in the scenic area, extract each block scenic spot after marking overlap; and distinguish it into different spatial dimensions, and calculate the time dimension of the scenic spot ticket in 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 visitors interval and circulation speed.

[0035] 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 attraction in the scenic area in history to obtain the normal number interval of each block attraction; 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.

[0036] 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 sub-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.

[0037] Embodiment: After analyzing the tourist area in a certain scenic spot, the block attractions are obtained as attraction 1, attraction 2 and attraction 3; 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. During the peak period, the number of visitors to each scenic spot is calculated in real time to be 140, 320, and 90; the circulation speed of tourists in the scenic area is 8m / min; after judging that there is a lack of tourists in block attraction 1, the number of tickets sold for the corresponding block attraction is increased; there is a risk of congestion in attractions 2 and 3, so ticket sales are stopped; During the off-peak period, the number of visitors to each scenic spot is calculated to be 10, 30, and 50. After judgment, scenic spots 1 and 2 are closed, and scenic spot 3 is open as usual.

[0038] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered 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, analyzing the tickets for scenic spots that need to be purchased and have separate attractions in the scenic spot, analyzing the route maps of all attractions in the scenic spot, extracting the overlapping marked routes to obtain block attractions, setting ticket gates in each block attraction, setting tickets for each block attraction, formulating different ticket sales time dimensions by analyzing the route space dimension of each attraction, and formulating a distributed ticket sales strategy in the scenic spot; S200, collecting the sales of scenic spot tickets in different time periods in history, and determining the peak period and off-peak period of the scenic spot by analyzing the sales; S300, collecting the number of visitors to each block scenic spot in the scenic area during normal operation at different periods in history, and taking the block scenic spot as operating normally when there is profit and no congestion; calculating the normal number interval of each scenic spot in the scenic area according to the number of visitors in history; collecting the flow time of tourists between different block scenic spots when no congestion occurs in the scenic area in history, and calculating the flow threshold of tourists when no congestion occurs by using the flow time and distance between different block scenic spots; S400, when purchasing tickets in the ticket purchasing channel, tourists arbitrarily select block attractions in the scenic area to purchase tickets in blocks, enter the scenic area with the tickets, and then enter each block attraction in the scenic area after verification and registration at the gate of the block attraction; during the peak period, the central processor of the scenic area records the number of visitors in each block attraction in real time and calculates the real-time circulation speed of tourists; S500, using the normal number interval to analyze the real-time number of visitors in each sub-block scenic spot, using the flow threshold to analyze the real-time flow speed of tourists, then determining whether there is a congestion risk in the sub-block scenic spot and issuing an early warning, and using the early warning information to adjust the scenic spot ticketing; S600: During the off-peak period of the scenic spot, the number of visitors to each sub-block scenic spot in the scenic spot is determined by using the normal business interval, and the ticket prices of different sub-block scenic spots 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 formulating a distributed ticketing strategy in the scenic spot in S100 are: S101, extracting the scenic spot tour route map in the central processor of the scenic spot, marking each scenic spot in the scenic spot in the tour route map according to the scenic spot planning, collecting the action trajectory of tourists visiting each scenic spot in history to generate a route map for each scenic spot, and assuming that the generated route map of all scenic spots in the scenic spot is , 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 interpretation map, and the color overlapping part and the separate part of the scenic spot route interpretation 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 interpretation map, where x is a positive integer; let the extracted single part be , represents the 1st, 2nd, 3rd, ..., yth individual parts extracted from the scenic route interpretation map, 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 spots planned in the scenic area and the corresponding individual routes; Sell ​​tickets separately for each block of attractions; 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 in the scenic spot during the tour in 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 walking during the tour is calculated, and the formula is: , where 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 visit; 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 route contained in the block scenic spot 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 route contained in the block scenic spot is calculated; the difference time of tourists walking in each spatial dimension is calculated, and the formula is: ； In the formula, Fc represents the difference time of tourists walking in each spatial dimension, Fz represents the walking time of tourists passing the total length of the scenic spot route map in each spatial dimension, Fh represents the walking time of tourists passing the overlapping length of the scenic spot 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; S103, extract the difference time in the two spatial dimensions in turn to calculate the difference Fcc. When Fcc≤Fh, it is determined that there is interference in the scenic spot routes in the corresponding two spatial dimensions, and the scenic spot tickets 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 in the scenic spot routes in the corresponding two spatial dimensions, and tickets are sold normally; after performing pairwise judgments on all spatial dimensions, a distributed ticket selling strategy is constructed by using block scenic spot ticket selling and time dimension ticket selling.

3. 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: 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 , represents 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, judging the extracted peak values ​​and trough values, selecting the peak time corresponding to the maximum peak value among the p peak values ​​as the peak period of the scenic spot, and selecting the trough time corresponding to the minimum value among the p trough values ​​as the low peak period of the scenic spot; finding 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 of 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 of the minimum trough; the peak period of the scenic spot is finally obtained as , the low peak period is .

4. 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 sub-block scenic spot 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 interval of each block attraction is calculated using the number of visitors to each block attraction collected in different periods, and 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 scenic spots; 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 in the calculated 1st, 2nd, 3rd, ..., mth block attractions in the scenic area; S302: Collect the circulation time of tourists between different scenic spots in the history 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 in the collected history when there is no congestion in the scenic spot, 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 block attractions in the collected 1st, 2nd, 3rd, ..., uth circulation time; the circulation threshold in 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 in the scenic area, Indicates the circulation speed of tourists in the scenic area. represents the average value of the calculated circulation velocity, Represents the standard deviation of the calculated circulation velocity.

5. 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 block 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 arbitrarily select block attractions in the scenic area to purchase tickets in blocks. After entering the scenic area with the ticket, when entering each block attraction in the scenic area, the ticket is used to verify and register at the gate of the 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, analyzing the real-time number of visitors in each sub-block scenic spot, calculating the difference in the number of visitors between every two sub-block scenic spots at every time t1, and calculating the real-time tourist circulation speed 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.

6. A scenic spot ticket management method based on big data analysis according to claim 5, characterized in that: The specific steps of adjusting the scenic spot ticketing by 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 scenic spot is judged to have a congestion risk, the scenic spot central processor issues an early warning and stops ticket sales for the corresponding block scenic spot; but When , it is determined that the corresponding sub-block scenic spot is full, and the number of tickets sold for the corresponding sub-block scenic spot is reduced; when and When , it is determined that the corresponding sub-block scenic spot lacks tourists, and the number of tickets sold for the corresponding sub-block scenic spot 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.

7. A scenic spot ticket management method based on big data analysis according to claim 4, characterized in that: The specific steps of adjusting the tickets of different scenic spots in different blocks according to the number of visitors in S600 are: S601, during the off-peak period of the scenic spot, the number of visitors to each sub-block scenic spot in the scenic spot is judged by using the normal business interval, and the number of visitors to each sub-block scenic spot during the off-peak period is recorded in real time as Sd. When the corresponding block attractions are judged to be not profitable, the corresponding block attractions are closed and ticket sales for the corresponding block 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.

8. A scenic spot ticket management system based on big data analysis, characterized by: The scenic spot ticket management system includes data collection module, distributed ticketing module, tourist data analysis module, real-time tourist 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 spot in history; The distributed ticketing module is used to analyze the route map of each scenic spot in the scenic area, extract each block scenic spot after marking overlap; and distinguish it into different spatial dimensions, and calculate the time dimension of the scenic spot ticket in 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 the 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 visitors interval and circulation speed.

9. A scenic spot ticket management system based on big data analysis according to claim 8, 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 attraction in the scenic area in history to obtain the normal number interval of each block attraction; 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.

10. A scenic spot ticket management system based on big data analysis according to claim 8, 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 sub-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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