An intelligent ticketing system

Abstract

The present invention discloses an intelligent ticketing system, which relates to the technical field of intelligent ticketing, and includes: a data acquisition module that obtains historical sales, weather forecast, and holiday data; a first prediction module that constructs a linear fitting function using user behavior data and historical sales data to predict the ticket sales volume of each type of ticket on weekdays and weekends in the next week; a second prediction module that predicts the ticket sales volume of the next day based on the average ticket sales volume of the previous four weeks and weather data, and considers the impact of holidays; a comprehensive prediction module that combines the actual ticket sales volume of the previous week, the first predicted and second predicted ticket sales volumes, calculates the error and determines their respective weight factors to obtain the comprehensive predicted ticket sales volume of each type of ticket on the next day; a ticket issuing channel adjustment module that dynamically adjusts the number of ticket issuing channels according to the predicted ticket sales volume and the single-channel ticket issuing threshold, and optimizes the allocation of new ticket issuing requests to prevent network congestion and improve the ticket issuing efficiency; realizing the optimization operation of intelligent ticket issuing.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent ticketing, and particularly to an intelligent ticketing system. Background Art

[0002] An intelligent ticketing system is an innovative ticketing management system that applies advanced technologies and informatization means, based on technologies such as wireless communication, data transmission, and mobile payment, to transform traditional physical forms such as cards and tickets into electronic or virtual forms;

[0003] As an important tool for modern performance and event management, the accuracy and efficiency of the intelligent ticketing system are crucial for improving the user experience, optimizing resource allocation, and increasing revenue; with the rapid development of big data and artificial intelligence technologies, the intelligent ticketing system can not only process a large amount of ticketing data, but also analyze these data to predict future ticket sales trends, thereby providing support for decision-making;

[0004] However, existing intelligent ticketing systems usually perform ticket sales volume prediction based on simple historical data analysis. This method often ignores the influence of multiple factors such as user behavior, weather changes, and holidays, resulting in a large deviation between the prediction results and the actual ticket sales situation; in addition, existing ticketing systems usually only consider adding ticket issuing processing channels when network congestion occurs, and cannot reasonably allocate ticket issuing processing channels for ticketing data according to the prediction of the next day's ticket sales volume, and thus cannot prevent the occurrence of network congestion in advance, which leads to processing delays when congestion occurs, increases the waiting time of users, and reduces the system efficiency. Summary of the Invention

[0005] (1) Technical Problems to be Solved

[0006] In view of the technical problems in the background art, the present invention proposes an intelligent ticketing system, which includes multiple modules, specifically: a data collection module acquires historical sales, weather forecast, and holiday data; a first prediction module constructs a linear fitting function using user behavior data and historical sales data to predict the ticket sales volume of each type of ticket on weekdays and weekends in the next week; a second prediction module predicts the next day's ticket sales volume based on the average ticket sales volume in the previous four weeks and weather data, and considers the influence of holidays; a comprehensive prediction module combines the actual ticket sales volume in the previous week, the first prediction and the second prediction ticket sales volume, calculates the error and determines their respective weight factors to obtain the comprehensive predicted ticket sales volume of each type of ticket for the next day; a ticket issuing channel adjustment module dynamically adjusts the number of ticket issuing channels according to the predicted ticket sales volume and the single-channel ticket issuing threshold, and optimizes the allocation of new ticket issuing requests; thereby solving the problems mentioned in the background art.

[0007] (2) Technical Solutions

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0009] An intelligent ticketing system, comprising:

[0010] A data collection module, which obtains the sales data of various types of tickets from the ticket database of the intelligent ticketing system at the end of each day, including the ticket sales record data of various types of tickets for each day in the previous four weeks that is the same as the day of the next week, and obtains the weather forecast data and holiday data of the next day;

[0011] A first prediction module, which calculates the scoring reliability index Sri of each user according to the actual ticket purchase times of each user in the intelligent ticketing system k , and combines the scoring data of each user for various types of tickets in the previous week to calculate the comprehensive score Os of various types of tickets in the previous week i , constructs a linear fitting function between the comprehensive score and the actual ticket sales volume of various types of tickets in the previous four weeks, and calculates the first predicted ticket sales volume Fts of various types of tickets in the next week i ; for weekdays and weekends in the next week, the average ratio r i of the ticket sales volume of various types of tickets on weekdays and weekends in the previous four weeks is used to calculate the first predicted ticket sales volume of various types of tickets for each weekday and each weekend in the next week respectively; for holidays in the next week, a linear fitting function between the comprehensive score and the actual ticket sales volume of various types of tickets corresponding to the holiday in the previous four years is constructed, and the first predicted ticket sales volume of the holiday is obtained according to the scoring data of various types of tickets corresponding to the holiday in the previous year;

[0012] A second prediction module, which predicts the ticket sales volume of various types of tickets for the next day according to the average ticket sales volume data of various types of tickets that is the same as the day of the next week in the previous four weeks and the average ticket sales volume data under various weather conditions, and obtains the second predicted ticket sales volume Ets of various types of tickets for the next day i , if the next day is a holiday, the average ticket sales volume of various types of tickets during the holiday in the past three years is combined with the weather forecast data of the next day to predict the ticket sales volume of various types of tickets for the next day;

[0013] A comprehensive prediction module, based on the actual ticket sales volume data Ats of various types of tickets for each day in the previous week io , and the first predicted ticket sales volume Yts io and the second predicted ticket sales volume Ets io of various types of tickets, calculates the first error Fe io and the second error Se io of the first predicted ticket sales volume and the second predicted ticket sales volume relative to the actual ticket sales volume data respectively, and according to the first error Fe io and the second error Se ioObtain the weight factors β1 and β2 of the first predicted ticket sales volume and the second predicted ticket sales volume; for the first predicted ticket sales volume Yts of each type of ticket on the next day io and the second predicted ticket sales volume Ets io Combine with the corresponding weight factors to obtain the comprehensive predicted ticket sales volume Cts of each type of ticket on the next day i ;

[0014] The ticket issuing channel adjustment module obtains the single-channel ticket issuing threshold St of each type of ticket by simulating the network status when different numbers of tickets of each type are issued on a single channel i ; According to the relationship between the comprehensive predicted ticket sales volume Cts of each type of ticket i and the single-channel ticket issuing volume threshold St i Adjust the number of ticket issuing channels for each type of ticket; when a new ticket issuing request appears in the ticket issuing waiting queue, determine whether it is necessary to adjust the number of ticket issuing channels

[0015] Specifically, at the end of each day, obtain the ticket sales volume data of each type of ticket in the previous four weeks that is the same as the day of the week of the next day from the ticket database, denoted as Pw ij , where j represents the jth week before; obtain the weather forecast data for the next day, and use We to represent the weather forecast data for the next day. When We = 1, it represents a rainy or snowy day, and when We = 2, it represents a non-rainy or snowy day

[0016] Specifically, count the actual ticket purchase times Atp of all users in the intelligent ticketing system k , where k represents the kth user, and the actual ticket purchase times represent the number of times a user purchases a ticket normally and does not refund; according to the actual ticket purchase times Atp of all users in the intelligent ticketing system k , calculate the scoring reliability index Sri of each user k , and the expression is

[0017]

[0018] where, Mi k is the membership index, and Mi k ∈{1, N}, and N > 1. When the user is a non-member user, Mi k = 1; when the user is a member user, Mi k = N; the value of Max{x} is the maximum value of x

[0019] Obtain the ticket sales data of each type of ticket in the previous week, and obtain the names of the ticket purchasing users of each type of ticket. Obtain the scoring data Sc of the users for each type of ticket in the corresponding user table according to the names of the ticket purchasing users ik , and combine it with the scoring reliability index Sri of each user kCombine to obtain the comprehensive score Os of various types of tickets in the previous week i , and the expression is:

[0020]

[0021] where m i represents the actual total number of tickets purchased for the i-th type of ticket.

[0022] Furthermore, obtain the comprehensive score data of various types of tickets in the second week before last, the third week before last, and the fourth week before last, as well as the actual ticket sales volume data of various types of tickets in the first week before last, the second week before last, and the third week before last from the weekly record table in the ticket database; based on the linear regression equation Y ij = b + a*X i(j+1) fit the comprehensive score data of various types of tickets in the second week before last, the third week before last, and the fourth week before last, as well as the actual ticket sales volume data of various types of tickets in the first week before last, the second week before last, and the third week before last, where Y ij represents the actual ticket sales volume of the i-th type of ticket in the j-th week before last, and X i(j+1) represents the comprehensive score of the i-th type of ticket in the (j + 1)-th week before last; calculate the slope a o and the intercept b0 of the fitting line by the least squares method, and obtain the fitting equation as Y ij = b0 + a0*X i(j+1) .

[0023] Furthermore, substitute the comprehensive score data Os of various types of tickets in the first week before last, that is, the previous week i into the fitting equation to obtain the first predicted ticket sales volume Fts of various types of tickets in the next week i ; respectively screen out the ticket sales volume of various types of tickets on weekdays and weekends in the first week before last, the second week before last, the third week before last, and the fourth week before last, and calculate the weekday ticket sales volume Wts ij and the weekend ticket sales volume Sts ij of various types of tickets in each week respectively. Calculate the average ratio r of the weekday and weekend ticket sales volume of various types of tickets through the formula i , where n represents the number of weeks without holidays in the first week before last, the second week before last, the third week before last, and the fourth week before last, and n ∈ {1,..., 4}; combine the first predicted ticket sales volume Fts i of various types of tickets in the next week with the average ratio r i of the weekday and weekend ticket sales volume of various types of tickets to obtain the first predicted total ticket sales volume Wts i of various types of tickets on weekdays in the next week, and the first predicted total ticket sales volume Sts i of various types of tickets on weekends; thus obtain the first predicted ticket sales volume Yts for each day in the next weeki For the working days within the next week, the first predicted ticket sales volume of each type of ticket For the weekend within the next week, the first predicted ticket sales volume of each type of ticket

[0024] For the holidays within the next week, obtain the name of the holiday and respectively obtain the ticket sales data of each type of ticket during the holiday in the first year before, the second year before, the third year before, and the fourth year before from the ticket database, and respectively obtain the names of the ticket-purchasing users of each type of ticket in each year. According to the names of the ticket-purchasing users, obtain the rating data of the users for each type of ticket in the corresponding user table. Calculate the comprehensive ratings of each type of ticket during the holiday in the first year before, the second year before, the third year before, and the fourth year before respectively through the comprehensive rating calculation formula; combine the comprehensive ratings of each type of ticket during the holiday in the second year before, the third year before, and the fourth year before with the actual ticket sales volume data of each type of ticket during the holiday in the first year before, the second year before, and the third year before respectively to construct a linear fitting function for the corresponding holiday. Substitute the comprehensive rating of each type of ticket during the holiday in the first year before into the linear fitting function to obtain the first predicted ticket sales volume Yts of the holiday in the next week i .

[0025] Specifically, at the end of each day, obtain the ticket sales volume data of each type of ticket for each day in the previous four weeks from the ticket database, and combine the ticket sales volume data of each type of ticket with the same day of the week in the previous four weeks to obtain the average ticket sales volume data XAts of each type of ticket for each day i(week) , where week = 1 represents Monday, and so on; obtain the ticket sales volume data under various weather conditions from the ticket database, and combine the ticket sales volume data of each type of weather to obtain the average ticket sales volume data WAts of each type of ticket under each type of weather i(we) ;

[0026] Obtain the day-of-the-week information and weather forecast data for the next day; if the next day is not a holiday, combine the average ticket sales volume data of each type of ticket for the corresponding day of the week of the next day and the average ticket sales volume data of each type of ticket for the corresponding weather to obtain the second predicted ticket sales volume Ets of each type of ticket for the next day i , and the expression is:

[0027] Ets i = α1 * XAts i(week) + α2 * WAts i(we)

[0028] where α1 and α2 are the weight coefficients of the day-of-the-week information and the weather information respectively

[0029] Further, if the next day is a holiday, obtain the name of the holiday and retrieve the ticket sales volume data of various types of tickets during the holiday in the past three years from the ticket database. Combine the three sets of ticket sales volume data of various types of tickets during the holiday to obtain the average ticket sales volume HAts of various types of tickets during the holiday, where ho represents different holidays. Combine the average ticket sales volume of various types of tickets for the holiday corresponding to the next day with the average ticket sales volume data of various types of tickets for the corresponding weather to obtain the second predicted ticket sales volume Ets of various types of tickets for the next day. The expression is: Ets = α1 * HAts + α2 * WAts. i(ho) Specifically, retrieve the actual ticket sales volume data Ats of various types of tickets for each day in the previous week from the ticket database, as well as the first predicted ticket sales volume Yts and the second predicted ticket sales volume Ets of various types of tickets for each day in the previous week, where o represents the o-th day in the previous week. Combine the actual ticket sales volume data Ats with the first predicted ticket sales volume Yts and the second predicted ticket sales volume Ets respectively to obtain the first error Fe and the second error Se of the first predicted ticket sales volume and the second predicted ticket sales volume relative to the actual ticket sales volume data. The expressions are: Fe = |Ats - Yts|, Se = |Ats - Ets|. Calculate the average of the first error Fe and the second error Se for each day in the previous week to obtain the first average error and the second average error. Combine the first average error and the second average error to calculate the weight factors β1 and β2 of the first predicted ticket sales volume and the second predicted ticket sales volume respectively. The expression is: i Ets i = α1 * HAts i(ho) + α2 * WAts i(we) .

[0030] Specifically, retrieve the actual ticket sales volume data Ats of various types of tickets for each day in the previous week from the ticket database, as well as the first predicted ticket sales volume Yts and the second predicted ticket sales volume Ets of various types of tickets for each day in the previous week, where o represents the o-th day in the previous week. Combine the actual ticket sales volume data Ats with the first predicted ticket sales volume Yts and the second predicted ticket sales volume Ets respectively to obtain the first error Fe and the second error Se of the first predicted ticket sales volume and the second predicted ticket sales volume relative to the actual ticket sales volume data. The expressions are: Fe = |Ats - Yts|, Se = |Ats - Ets|. Calculate the average of the first error Fe and the second error Se for each day in the previous week to obtain the first average error and the second average error. Combine the first average error and the second average error to calculate the weight factors β1 and β2 of the first predicted ticket sales volume and the second predicted ticket sales volume respectively. The expression is: io Specifically, retrieve the actual ticket sales volume data Ats of various types of tickets for each day in the previous week from the ticket database, as well as the first predicted ticket sales volume Yts and the second predicted ticket sales volume Ets of various types of tickets for each day in the previous week, where o represents the o-th day in the previous week. Combine the actual ticket sales volume data Ats with the first predicted ticket sales volume Yts and the second predicted ticket sales volume Ets respectively to obtain the first error Fe and the second error Se of the first predicted ticket sales volume and the second predicted ticket sales volume relative to the actual ticket sales volume data. The expressions are: Fe = |Ats - Yts|, Se = |Ats - Ets|. Calculate the average of the first error Fe and the second error Se for each day in the previous week to obtain the first average error and the second average error. Combine the first average error and the second average error to calculate the weight factors β1 and β2 of the first predicted ticket sales volume and the second predicted ticket sales volume respectively. The expression is: io and the second predicted ticket sales volume Ets io , where, o represents the o-th day in the previous week; Combine the actual ticket sales volume data Ats io with the first predicted ticket sales volume Yts io and the second predicted ticket sales volume Ets io respectively to obtain the first error Fe of the first predicted ticket sales volume and the second predicted ticket sales volume relative to the actual ticket sales volume data io and the second error Se io , the expression is Fe io = |Ats io - Yts io |, Se io = |Ats io - Ets io |; Calculate the average of the first error Fe io and the second error Se io for each day in the previous week to obtain the first average error and the second average error

[0031] Combine the first average error with the second average error to calculate the weight factors β1 and β2 of the first predicted ticket sales volume and the second predicted ticket sales volume respectively. The expression is: Combine the first predicted ticket sales volume and the second predicted ticket sales volume of various types of tickets for the next day with the corresponding weight factors to obtain the comprehensive predicted ticket sales volume Cts of various types of tickets for the next day. The expression is: Cts = β1 * Yts i , the expression is: Cts i = β1 * Yts i+β2*Ets i 。

[0032] Specifically, a simplified model of the intelligent ticketing system is built. In the simplified model, the ticket issuing requests corresponding to various types of tickets are respectively simulated to perform the ticket issuing process through a single-channel method; ticket issuing requests are sent one by one to the simplified model of the intelligent ticketing system, and the network status data of the intelligent ticketing system is monitored in real time. The real-time network status data is analyzed by the network analysis tool in the system. When the network status first shows congestion, record the total number of ticket issuing requests at this time. The total number of ticket issuing requests when the network status of each type of ticket simulation first shows congestion is used as the single-channel ticket issuing threshold St of the corresponding type of ticket i 。

[0033] Furthermore, the comprehensive predicted ticket sales volume Cts of each type of ticket i is combined with the single-channel ticket issuing volume threshold St i . If Cts i < St i , then two channels are allocated for the ticket issuing process of this type of ticket; if Cts i ≥ St i , then + 2 channels are allocated for the ticket issuing process of this type of ticket, where represents the floor operation on x;

[0034] Whenever a new ticket issuing waiting request appears in the ticket issuing waiting queue of each channel, count the total number of ticket issuing waiting requests Npr in the ticket issuing waiting queue of this channel i . If Npr i ≥ 0.9*St i , then a new channel needs to be used for the ticket issuing operation. Specifically, the ticket issuing waiting requests sorted as even numbers in the current channel's ticket issuing waiting queue are taken out and placed in the ticket issuing waiting queue of a new channel, and subsequent ticket issuing process operations are performed

[0035] (III) Beneficial Effects

[0036] The present invention provides an intelligent ticketing system, which has the following beneficial effects:

[0037] 1. The data acquisition module automatically extracts the sales data of various types of tickets from the ticket database at the end of each day, and combines with weather forecast and holiday information. This module provides a solid foundation for subsequent prediction and analysis;

[0038] 2. The first prediction module constructs a more refined prediction model by deeply mining user behavior data and combining the number of ticket purchases, ratings, and membership status of users. It not only considers the historical ticket purchase behavior of users but also measures the influence of user ratings through a rating reliability index, thus more accurately predicting the ticket sales volume of various types of tickets in the next week.

[0039] 3. The second prediction module provides another dimension of prediction for the ticket sales volume of various types of tickets on the next day by comprehensively considering the day-of-the-week information, weather information, and the special impact of holidays. It uses the entropy weight method to objectively allocate the weights of different influencing factors, making the prediction results more accurate and reliable.

[0040] 4. The comprehensive prediction module integrates the prediction results of the first prediction module and the second prediction module, and combines the actual ticket sales volume data of the previous week to calculate the errors of the two prediction modules and allocate weight factors accordingly. This not only improves the accuracy of the prediction but also enhances the stability of the prediction results.

[0041] 5. The ticket issuing channel adjustment module obtains the single-channel ticket issuing threshold for various types of tickets by simulating the network status when different numbers of tickets are issued through a single channel for various types of tickets. It can adjust the number of ticket issuing channels in advance according to the predicted ticket sales volume, effectively preventing network congestion caused by excessive ticket issuing requests. By real-time monitoring the network status and dynamically adjusting the ticket issuing channels, this module ensures the efficient operation of the intelligent ticketing system, improves the user experience, and reduces potential losses caused by network congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic structural diagram of the intelligent ticketing system provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Refer to Figure 1 , the present invention provides an intelligent ticketing system, including:

[0045] A data acquisition module, which obtains the sales data of various types of tickets from the ticket database of the intelligent ticketing system at the end of each day, including the ticket sales record data of various types of tickets for each day with the same day of the week as the next day in the previous four weeks, and obtains the weather forecast data and holiday data of the next day.

[0046] The ticket database is the core component of the intelligent ticketing system. It stores various information and data related to ticket sales. The intelligent ticketing system sells multiple types of tickets. The ticket database contains ticket tables for each type of ticket. Each ticket table stores daily historical sales record data for the corresponding type of ticket and data such as the weather conditions at the start of the performance corresponding to each type of ticket. After each week, the analysis data for each week is stored in the weekly record table. The analysis data includes the total ticket purchase data for each week, the comprehensive score of each type of ticket, and other data. The ticket database also contains a forecast ticket sales table for each type of ticket, which is used to store all forecast data for the next day's ticket sales.

[0047] The user database is a key component of the smart ticketing system. It stores various information and data related to user behavior. The user database includes user tables of all users who have registered accounts in the smart ticketing system. When a user registers an account in the smart ticketing system, a new user table is created in the user database. Each user table stores relevant data of each user, including whether he is a member, the ticket purchase data of each history, as well as the ticket refund record and the rating record of the corresponding performance of the ticket. Among them, using Sc i represents the user's rating of the performance corresponding to the i-th type of ticket office, and Sc i ∈{0, 1, …, 10};

[0048] The ticket database and the user database are connected through the two keywords of user name and ticket type;

[0049] At the end of each day, the ticket sales data of each type of ticket in the same week as the next day in the previous four weeks are obtained from the ticket database, recorded as Pw ij , where j represents the jth week before. Specifically, if tomorrow is Tuesday, the ticket sales data for each Tuesday in the previous four weeks is obtained from the ticket database to obtain the weather forecast data for the next day. We is used to represent the weather forecast data for the next day. When We=1, it represents a rainy or snowy day. When We=2, it represents a non-rainy or snowy day. The weather affects people's travel willingness, the operating conditions of transportation vehicles, safety factors and other aspects. The forecast weather specifically represents the forecast weather conditions at the start of the performance corresponding to each type of ticket.

[0050] The data collection module automatically extracts sales data of various types of tickets from the ticket database at the end of each day, and combines it with weather forecasts and holiday information. This module provides a solid foundation for subsequent predictions and analysis.

[0051] The first prediction module calculates each user's score reliability index Sri according to the actual number of times each user purchases tickets in the intelligent ticketing system. kand calculate the comprehensive score \(O_s\) of each type of ticket in the previous week by combining the rating data of each type of ticket given by users in the previous week i Construct a linear fitting function between the comprehensive score and the actual ticket sales volume of each type of ticket in the previous four weeks, and calculate the first predicted ticket sales volume \(F_{ts}\) of each type of ticket in the next week i For weekdays and weekends in the next week, calculate the average ratio \(r\) of the ticket sales volume of each type of ticket on weekdays and weekends in the previous four weeks i Calculate the first predicted ticket sales volume of each type of ticket for each weekday and each weekend in the next week respectively; for holidays in the next week, construct a linear fitting function between the comprehensive score and the actual ticket sales volume of each type of ticket for the corresponding holidays in the previous four years, and obtain the first predicted ticket sales volume of the holiday according to the rating data of the corresponding holiday in the previous year

[0052] The following steps are carried out in the first prediction module

[0053] S1. Count the actual ticket purchase times \(A_{tp}\) of all users in the intelligent ticketing system k where \(k\) represents the \(k\)th user, and the actual ticket purchase times represent the number of times a user purchases a ticket normally and does not refund; according to the actual ticket purchase times \(A_{tp}\) of all users in the intelligent ticketing system k calculate the rating reliability index \(S_{ri}\) of each user k The expression is

[0054]

[0055] where \(M_i\) k is the membership index, and \(M_i\) k \(\in\{1, N\}\), and \(N\gt1\). When the user is a non-member user, \(M_i\) k \( = 1\); when the user is a member user, \(M_i\) k \( = N\), and the value of \(N\) is set by the system administrator. The ratings of member users have a higher influence than those of non-member users; the value of \(Max\{x\}\) is the maximum value of \(x\)

[0056] S2. Obtain the ticket sales data of each type of ticket in the previous week, and obtain the names of the ticket-purchasing users of each type of ticket. Obtain the rating data \(S_c\) of users for each type of ticket according to the names of the ticket-purchasing users in the corresponding user table ik and combine it with the rating reliability index \(S_{ri}\) of each user k to obtain the comprehensive score \(O_s\) of each type of ticket in the previous week i The expression is

[0057]

[0058] where \(m\) iRepresents the actual total number of tickets purchased for the i-th type of ticket; combines the scoring data of each type of ticket by users with the rating reliability index of each user to more accurately predict the ticket sales volume for the next week from the perspective of users' psychology;

[0059] S3. Obtain the comprehensive scoring data of each type of ticket for the second week before, the third week before, and the fourth week before from the weekly record table in the ticket database, as well as the actual ticket sales volume data of each type of ticket for the first week before, the second week before, and the third week before; the comprehensive scoring data of each type of ticket for the second week before is used to predict the ticket sales volume of each type of ticket for the first week before, the comprehensive scoring data of each type of ticket for the third week before is used to predict the ticket sales volume of each type of ticket for the second week before, and the comprehensive scoring data of each type of ticket for the fourth week before is used to predict the ticket sales volume of each type of ticket for the third week before;

[0060] Based on the linear regression equation Y ij = b + a*X i(j+1) Perform fitting on the comprehensive scoring data of each type of ticket for the second week before, the third week before, and the fourth week before, and the actual ticket sales volume data of each type of ticket for the first week before, the second week before, and the third week before, where Y ij represents the actual ticket sales volume of the i-th type of ticket for the j-th week before, and X i(j+1) represents the comprehensive score of the i-th type of ticket for the (j + 1)-th week before; calculate the slope a0 and intercept b0 of the fitting line through the least squares method to obtain the fitting equation Y ij = b0 + a0*X i(j+1) ;

[0061] S4. Substitute the comprehensive scoring data Os i of each type of ticket for the first week before, that is, the previous week, into the fitting equation to obtain the first predicted ticket sales volume Fts i of each type of ticket for the next week; screen out the ticket sales volume of each type of ticket on weekdays (Monday to Friday) and weekends (Saturday and Sunday) in the first week before, the second week before, the third week before, and the fourth week before, and calculate the weekday ticket sales volume Wts ij and weekend ticket sales volume Sts ij of each type of ticket for each week respectively, and calculate the average ratio r of the weekday and weekend ticket sales volume of each type of ticket through the formula i , where n represents the number of weeks without holidays (including Chinese legal holidays and Western legal holidays) in the first week before, the second week before, the third week before, and the fourth week before, and n ∈ {1,..., 4}; compare the first predicted ticket sales volume Fts i of each type of ticket for the next week with the average ratio r iCombine to obtain the first predicted total ticket sales volume Wts of each type of ticket on weekdays in the next week i and the first predicted total ticket sales volume Sts of each type of ticket on weekends i ; further obtain the first predicted ticket sales volume Yts for each day in the next week i For weekdays within the next week, the first predicted ticket sales volume of each type of ticket For weekends within the next week, the first predicted ticket sales volume of each type of ticket The weekdays and weekends do not include holidays;

[0062] For holidays within the next week, obtain the name of the holiday and respectively obtain the ticket sales data of each type of ticket during the holiday in the first year before, the second year before, the third year before, and the fourth year before from the ticket database, and respectively obtain the names of the ticket-purchasing users of each type of ticket in each year. Obtain the scoring data of users for each type of ticket in the corresponding user table according to the names of the ticket-purchasing users, and calculate the comprehensive scores of each type of ticket during the holiday in the first year before, the second year before, the third year before, and the fourth year before respectively through the comprehensive scoring calculation formula in S2; Combine the comprehensive scores of each type of ticket during the holiday in the second year before, the third year before, and the fourth year before with the actual ticket sales volume data of each type of ticket during the holiday in the first year before, the second year before, and the third year before respectively to construct a linear fitting function for the corresponding holiday, and substitute the comprehensive scores of each type of ticket during the holiday in the first year before into the linear fitting function to obtain the first predicted ticket sales volume Yts of the holiday in the next week i .

[0063] The first prediction module constructs a more refined prediction model by deeply mining user behavior data and combining the user's ticket-purchasing times, ratings, and membership status; not only considers the user's historical ticket-purchasing behavior, but also measures the influence of the user's rating through the rating reliability index, so as to more accurately predict the ticket sales volume of each type of ticket in the next week.

[0064] The second prediction module predicts the ticket sales volume of each type of ticket for the next day based on the average ticket sales volume data of each type of ticket with the same day of the week as the next day in the previous four weeks and the average ticket sales volume data under various weather conditions, and obtains the second predicted ticket sales volume Ets of each type of ticket for the next day i If the next day is a holiday, combine the average ticket sales volume of each type of ticket during the holiday in the past three years with the weather forecast data for the next day to predict the ticket sales volume of each type of ticket for the next day;

[0065] The following steps are carried out in the second prediction module:

[0066] T1. At the end of each day, obtain the ticket sales volume data of each type of ticket for each day in the previous four weeks from the ticket database, and combine the ticket sales volume data of each type of ticket with the same day of the week within the previous four weeks to obtain the average ticket sales volume data XAts of each type of ticket for each day. i(week) , where week = 1 represents Monday, and so on; for example: obtain the ticket sales volume data of each type of ticket on Wednesday within the first week, the second week, the third week, and the fourth week respectively, and combine the ticket sales volume data of each type of ticket on all Wednesdays within the previous four weeks to obtain the average ticket sales volume data WAts3 on Wednesday; obtain the ticket sales volume data under various weather conditions from the ticket database, and combine the ticket sales volume data of each type of weather to obtain the average ticket sales volume data WAts of each type of ticket under each type of weather. i(we) These data need to be updated at the end of each day.

[0067] T2. At the end of each day, obtain the day-of-the-week information of the next day, that is, determine which day of the week the next day is, and obtain the weather forecast data of the next day; determine whether the next day is a holiday. If the next day is not a holiday, combine the average ticket sales volume data of each type of ticket corresponding to the day of the week of the next day and the average ticket sales volume data of each type of ticket corresponding to the weather to obtain the second predicted ticket sales volume Ets of each type of ticket for the next day. i The expression is:

[0068] Ets i = α1 * XAts i(week) + α2 * WAts i(we)

[0069] Among them, α1 and α2 are the weight coefficients of the day-of-the-week information and the weather information respectively, and the entropy weight method is used to calculate the weight coefficients of the day-of-the-week information and the weather information; the entropy weight method is an objective weighting method based on information theory. It determines the weight by calculating the order degree (i.e., the dispersion degree) of the information contained in each index. In multi-attribute decision-making problems, especially when multiple indicators (such as the day-of-the-week information and the weather information in the present invention) need to be considered comprehensively, the entropy weight method can provide a relatively objective weight distribution.

[0070] T3. If the next day is a holiday, obtain the name of the holiday and obtain the ticket sales volume data of each type of ticket within the holiday in the past three years from the ticket database. Combine the three ticket sales volume data of each type of ticket within the holiday obtained to obtain the average ticket sales volume HAts of each type of ticket within the holiday. i(ho) , where ho represents different holidays and is represented by different numbers; combine the average ticket sales volume of each type of ticket corresponding to the holiday of the next day and the average ticket sales volume data of each type of ticket corresponding to the weather to obtain the second predicted ticket sales volume Ets of each type of ticket for the next day. i The expression is: Etsi = α1 * HAts i(ho) + α2 * WAts i(we) .

[0071] The second prediction module provides another dimension of prediction for the ticket sales volume of each type of ticket for the next day by comprehensively considering the week information, weather information, and the special impact of holidays; the entropy weight method is used to objectively allocate the weights of different influencing factors, making the prediction results more accurate and reliable.

[0072] The comprehensive prediction module, based on the actual ticket sales volume data Ats of each type of ticket per day within the last week io , and the first predicted ticket sales volume Yts of each type of ticket io and the second predicted ticket sales volume Ets io , respectively calculate the first error Fe of the first predicted ticket sales volume and the second predicted ticket sales volume relative to the actual ticket sales volume data io and the second error Se io , according to the first error Fe io and the second error Se io obtain the weight factors β1 and β2 of the first predicted ticket sales volume and the second predicted ticket sales volume; combine the first predicted ticket sales volume Yts io and the second predicted ticket sales volume Ets io of each type of ticket for the next day with the corresponding weight factors to obtain the comprehensive predicted ticket sales volume Cts i ;

[0073] The following steps are carried out in the comprehensive prediction module:

[0074] P1. Obtain the actual ticket sales volume data Ats of each type of ticket per day within the last week from the ticket database io , and the first predicted ticket sales volume Yts of each type of ticket per day within the last week io and the second predicted ticket sales volume Ets io , where o represents the oth day within the last week; combine the actual ticket sales volume data Ats io of each type of ticket per day within the last week with the corresponding first predicted ticket sales volume Yts io and the second predicted ticket sales volume Ets io to obtain the first error Fe io and the second error Se io of the first predicted ticket sales volume and the second predicted ticket sales volume relative to the actual ticket sales volume data, and the expressions are: Fe io = |Ats io - Yts io |, Se io = |Ats io - Ets io|; Calculate the average of the first error Fe for each day in the previous week io and the second error Se io to obtain the first average error and the second average error

[0075] P2. Combine the first average error with the second average error to calculate the weight factors β1 and β2 for the first predicted ticket sales volume and the second predicted ticket sales volume respectively. The expressions are as follows:

[0076]

[0077] P3. Combine the first predicted ticket sales volume and the second predicted ticket sales volume of each type of ticket for the next day with the corresponding weight factors to obtain the comprehensive predicted ticket sales volume Cts of each type of ticket for the next day i , and the expression is: Cts i = β1 * Yts i + β2 * Ets i .

[0078] The comprehensive prediction module integrates the prediction results of the first prediction module and the second prediction module, combines with the actual ticket sales volume data of the previous week, calculates the errors of the two prediction modules, and assigns weight factors accordingly; this not only improves the accuracy of the prediction but also enhances the stability of the prediction results.

[0079] The ticket issuing channel adjustment module obtains the single-channel ticket issuing threshold St for each type of ticket by simulating the network status when different numbers of tickets are issued for each type of ticket through a single channel i ; Adjust the number of ticket issuing channels for each type of ticket according to the relationship between the comprehensive predicted ticket sales volume Cts i of each type of ticket and the single-channel ticket issuing volume threshold St i ; When a new ticket issuing request appears in the ticket issuing waiting queue, determine whether it is necessary to adjust the number of ticket issuing channels;

[0080] Build a simplified model of an intelligent ticketing system, which includes the processing logic of tickets and the network interaction interface and analysis function; in the simplified model of the intelligent ticketing system, simulate the operation of issuing tickets for the corresponding ticket issuing requests of each type of ticket through a single channel, with one ticket corresponding to one ticket issuing request; send ticket issuing requests to the simplified model of the intelligent ticketing system one by one, and monitor the network status data of the intelligent ticketing system in real time. Analyze the real-time network status data through the network analysis tool in the system. When the network status first shows congestion, record the total number of ticket issuing requests at this time, and use the total number of ticket issuing requests when the network status of each type of ticket simulated first shows congestion as the single-channel ticket issuing threshold St of the corresponding type of ticketi ;

[0081] Combine the comprehensive predicted ticket sales volume Cts of each type of ticket i with the single-channel ticket issuance volume threshold St i The combined comprehensive predicted ticket sales volume is Cts i That is, it means that the total number of predicted ticket issuance requests is Cts i , if Cts i < St i , it means that according to the total number of predicted ticket issuance requests for this type of ticket, it can be processed for regular ticket issuance through a single channel. Allocate two channels for ticket issuance for this type of ticket, where one channel is used to handle special situations, such as when the actual ticket sales volume suddenly exceeds the single-channel ticket issuance volume threshold; if Cts i ≥ St i , it means that according to the total number of predicted ticket issuance requests for this type of ticket, it cannot be processed for ticket issuance through a single channel, and multiple channels need to be set for regular ticket issuance. Allocate channels for ticket issuance for this type of ticket, where represents the floor operation on x; Adjusting the number of ticket issuance channels for each type of ticket in advance according to the comprehensive predicted ticket sales volume can prevent network congestion caused by excessive ticket issuance requests in the future, and reduce the time consumed for reallocation when congestion occurs;

[0082] Whenever a new ticket issuance waiting request appears in the ticket issuance waiting queue of each channel, count the total number of ticket issuance waiting requests Npr in the ticket issuance waiting queue of this channel i , if Npr i ≥ 0.9*St i , it means that the total number of ticket issuance waiting requests in the ticket issuance waiting queue of this channel is too large, and a new channel needs to be used for ticket issuance operations. Specifically, take out the ticket issuance waiting requests with even numbers in the ticket issuance waiting queue of the current channel and put them into the ticket issuance waiting queue of a new channel, and perform subsequent ticket issuance processing operations.

[0083] The ticket issuance channel adjustment module obtains the single-channel ticket issuance threshold of each type of ticket by simulating the network status when different numbers of tickets are issued through a single channel for each type of ticket; It can adjust the number of ticket issuance channels in advance according to the predicted ticket sales volume, effectively preventing network congestion caused by excessive ticket issuance requests; By monitoring the network status in real time and dynamically adjusting the ticket issuance channels, this module ensures the efficient operation of the intelligent ticket system, improves the user experience, and reduces potential losses caused by network congestion.

[0084] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer storage medium or transmitted through a computer storage medium.

[0085] The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0086] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An intelligent ticketing system, characterized in that: include: The data collection module obtains the sales data of various types of tickets from the ticket database of the intelligent ticketing system at the end of each day, including the sales record data of various types of tickets on the same day of the next week in the previous four weeks, and obtains the weather forecast data and holiday data for the next day; The first prediction module calculates each user's score reliability index Sri according to the actual number of times each user purchases tickets in the intelligent ticketing system. k , and combined with the rating data of users on various types of tickets in the previous week, calculate the comprehensive rating Os of various types of tickets in the previous week i , construct a linear fitting function of the comprehensive score of each type of ticket and the actual ticket sales in the first four weeks, and calculate the first predicted ticket sales Fts of each type of ticket in the next week i For the working days and weekends of the next week, the average ratio of ticket sales of various types of ticket working days to weekends in the previous four weeks is used. i Calculate the first predicted ticket sales volume for each type of ticket for each working day and each weekend of the next week; for the holidays of the next week, construct a linear fitting function between the comprehensive scores of each type of ticket and the actual ticket sales volume for the corresponding holidays in the previous four years, and obtain the first predicted ticket sales volume for the holiday based on the comprehensive ticket score data for the corresponding holiday in the previous year; The second prediction module predicts the ticket sales of each type of ticket on the next day based on the average ticket sales data of each type of ticket in the previous four weeks and the average ticket sales data under various weather conditions, and obtains the second predicted ticket sales Fts of each type of ticket on the next day. i If the next day is a holiday, the average ticket sales of each type of ticket during the holiday in the past three years is combined with the weather forecast data of the next day to predict the ticket sales of each type of ticket on the next day; Comprehensive forecasting module, based on the actual ticket sales data of various types of tickets per day in the previous week Ats io , and the first predicted ticket sales volume Yts for each type of ticket io and the second predicted ticket sales Ets io , respectively calculate the first error Fe of the first predicted ticket sales volume and the second predicted ticket sales volume relative to the actual ticket sales volume data io and the second error Se io , according to the first error Fe io and the second error Se io Get the weight factors β1 and β2 of the first predicted ticket sales and the second predicted ticket sales; convert the first predicted ticket sales Yts of each type of ticket for the next day into io and the second predicted ticket sales Ets io Combined with the corresponding weight factor, the comprehensive forecast ticket sales volume Cts of each type of ticket for the next day is obtained i ; The ticket channel adjustment module simulates the network status of each type of ticket when different numbers of tickets are issued in a single channel, and obtains the single channel ticket threshold St for each type of ticket. i ; Based on the comprehensive forecast of ticket sales volume Cts for each type of ticket i The single-channel ticket volume threshold St i The relationship between the number of ticketing channels for each type of ticket is adjusted; when a new ticketing request appears in the ticketing waiting queue, it is determined whether the number of ticketing channels needs to be adjusted.

2. The intelligent ticketing system according to claim 1, characterized in that: At the end of each day, the ticket sales data of each type of ticket in the same week as the next day in the previous four weeks are obtained from the ticket database, recorded as Pw ij , where j represents the previous jth week; obtain the weather forecast data for the next day, and use We to represent the weather forecast data for the next day. When We=1, it means it is a rainy or snowy day, and when We=2, it means it is a non-rainy or snowy day.

3. An intelligent ticketing system as claimed in claim 2, characterized in that: Count the actual number of ticket purchases by all users in the smart ticketing system k , where k represents the kth user, and the actual number of ticket purchases represents the number of times a user purchases tickets normally and does not refund them; according to the actual number of ticket purchases Atp by all users in the smart ticketing system k , calculate the rating reliability index Sri for each user k , the expression is: Among them, Mi k is the member index, and Mi k ∈{1,N}, and N>1, when the user is a non-member user, Mi k =1; when the user is a member user, Mi k =N; the value of Max{x} is the maximum value of x; Get the ticket sales data of various types of tickets in the previous week, and get the names of the users who purchased the tickets of various types of tickets. According to the names of the users who purchased the tickets, get the user's rating data for each type of ticket in the corresponding user table. ik and each user's rating reliability index Sri k Combined to get the comprehensive score Os of various types of tickets in the previous week i , the expression is: Among them, m i Represents the total number of tickets actually purchased for the i-th type of ticket.

4. An intelligent ticketing system as claimed in claim 3, characterized in that: Obtain the comprehensive score data of each type of ticket in the second week, the third week and the fourth week from the weekly record table in the ticket database, as well as the actual ticket sales data of each type of ticket in the first week, the second week and the third week; based on the linear regression equation Y ij =b+a*X i(j+1) The comprehensive score data of each type of ticket in the second week, the third week, and the fourth week, as well as the actual ticket sales data of each type of ticket in the first week, the second week, and the third week are fitted, where Y ij represents the actual sales volume of the ith type of ticket in the previous j week, X i(j+1) represents the comprehensive score of the i-th type of ticket in the previous j+1 week; the slope a0 and intercept b0 of the fitting line are calculated by the least squares method, and the fitting equation is Y ij =b0+a0*X i(j+1) .

5. An intelligent ticketing system as claimed in claim 4, characterized in that: The comprehensive score data of various types of tickets in the previous week, that is, the previous week, Os i Substituting into the fitting equation, we can get the first predicted ticket sales volume Fts for each type of ticket in the next week. i ; Filter out the ticket sales of each type of ticket on weekdays and weekends in the first week, the second week, the third week, and the fourth week, and calculate the ticket sales of each type of ticket on weekdays Wts in each week ij And the weekend ticket sales volume Sts ij , through the formula Calculate the average ratio of ticket sales on weekdays to weekends for each type of ticketing i , where n represents the number of weeks without holidays in the first week, the second week, the third week, and the fourth week, and n∈{1,…,4}; the first predicted ticket sales volume Fts of each type of ticket in the next week i The average ratio of ticket sales on weekdays to weekends for all types of ticket sales i Combined, we get the first predicted total ticket sales volume Wts for each type of ticket on the next weekday i , and the first forecast total ticket sales volume of various types of tickets on weekends Sts i ; Then get the first predicted ticket sales volume Yts for each day of the next week i , the first predicted ticket sales volume for each type of ticket for the next weekday For the weekends in the next week, the first forecast ticket sales of each type of ticket For holidays within the next week, obtain the name of the holiday and obtain the ticket sales data of each type of ticket within the previous year, the previous year, the previous year, the previous year, and the previous year from the ticket database, and obtain the names of the users who purchased each type of ticket in each year, obtain the user's rating data for each type of ticket in the corresponding user table according to the name of the ticket purchasing user, and calculate the comprehensive ratings of each type of ticket within the previous year, the previous year, the previous year, and the previous year through the comprehensive rating calculation formula; combine the comprehensive ratings of each type of ticket within the previous year, the previous year, and the previous year with the actual ticket sales data of each type of ticket within the previous year, the previous year, and the previous year, respectively, to construct a linear fitting function for the corresponding holiday, and bring the comprehensive ratings of each type of ticket within the previous year into the linear fitting function to obtain the first predicted ticket sales volume Yts for the holiday in the next week. i .

6. An intelligent ticketing system as claimed in claim 5, characterized in that: At the end of each day, the ticket sales data of each type of ticket for the previous four weeks are obtained from the ticket database, and the ticket sales data of each type of ticket for the same week in the previous four weeks are combined to obtain the average ticket sales data of each type of ticket for each day XAts i(week) , where week=1 means Monday, and so on; Get the ticket sales data for each type of weather from the ticket database, combine the ticket sales data for each type of weather, and get the average ticket sales data for each type of ticket under each type of weather. i(we) ; Get the next day's week information and weather forecast data; If the next day is not a holiday, the second predicted ticket sales volume Ets for each type of ticket on the next day is obtained by combining the average ticket sales volume data of each type of ticket on the corresponding week of the next day and the average ticket sales volume data of each type of ticket on the corresponding weather. i , the expression is: You are i =α1*XAts i(week) +α2*WAts i(we) Among them, α1 and α2 are the weight coefficients of week information and weather information respectively.

7. An intelligent ticketing system as claimed in claim 6, characterized in that: If the next day is a holiday, obtain the name of the holiday and obtain the ticket sales data of each type of ticket on the holiday in the past three years from the ticket database. Combine the three obtained ticket sales data of each type of ticket on the holiday to obtain the average ticket sales of each type of ticket on the holiday. i(ho) , where ho represents different holidays; the average ticket sales of each type of ticket on the next day corresponding to the holiday is combined with the average ticket sales of each type of ticket on the corresponding weather to obtain the second predicted ticket sales of each type of ticket on the next day Ets i , the expression is: Ets i =α1*HAts i(ho) +α2*WAts i(we) .

8. An intelligent ticketing system as claimed in claim 7, characterized in that: Get the actual ticket sales data of each type of ticket every day in the previous week from the ticket database Ats io , and the first predicted ticket sales volume Yts for each type of ticket every day in the previous week io and the second predicted ticket sales Ets io , where o represents the oth day in the previous week; the actual ticket sales data Ats io and the first predicted ticket sales volume Yts io and the second predicted ticket sales Ets io After combining, we get the first error Fe between the first predicted ticket sales volume and the second predicted ticket sales volume relative to the actual ticket sales volume data. io and the second error Se io , the expression is Fe io =|Ats io -Yts io |, Se io =|Ats io -Ets io |; The first daily error Fe in the previous week io and the second error Se io Take the average and get the first average error and the second mean error The first average error The second mean error Combined, the weight factors β1 and β2 of the first predicted ticket sales volume and the second predicted ticket sales volume are calculated respectively, and the expressions are: Combine the first predicted ticket sales volume and the second predicted ticket sales volume of each type of ticket for the next day with the corresponding weight factor to obtain the comprehensive predicted ticket sales volume Cts of each type of ticket for the next day i , the expression is: Cts i =β1*Yts i +β2*Ets i .

9. An intelligent ticketing system as claimed in claim 8, characterized in that: A simplified model of the intelligent ticketing system is built. In the simplified model, the ticketing requests corresponding to each type of ticketing are simulated to process the ticketing through a single channel. Ticketing requests are sent to the simplified model of the intelligent ticketing system one by one, and the network status data of the intelligent ticketing system is monitored in real time. The real-time network status data is analyzed through the network analysis tool in the system. When the network status is first displayed as congested, the total number of ticketing requests at this time is recorded. The total number of ticketing requests obtained by simulating each type of ticketing when the network status is first displayed as congested is used as the single-channel ticketing threshold St for the corresponding type of ticketing. i .

10. An intelligent ticketing system as claimed in claim 9, characterized in that: The comprehensive forecast ticket sales volume Cts of each type of ticket i The single-channel ticket volume threshold St i Combined, if Cts i <St i , two channels are allocated for this type of ticket to process the ticket; if Cts i ≥St i , then set it as the ticket allocation of this type There are three channels for ticket issuance: Indicates that x is rounded down; Whenever a new ticket waiting request appears in the ticket waiting queue of each channel, the total number of ticket waiting requests Npr in the ticket waiting queue of the channel is counted. i , if Npr i ≥0.9*St i , you need to use a new channel for ticketing operations. Specifically, take out the ticketing waiting requests sorted as even numbers in the ticketing waiting queue of the current channel, put them into the ticketing waiting queue of a new channel, and perform subsequent ticketing processing operations.

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