Ticketing Intelligent Management Platform and Method

Abstract

This invention provides an intelligent ticketing management platform and method, belonging to the field of ticketing management technology. Specifically, it includes: determining a verification processing strategy for popular tourist attractions during the target verification period based on the overlap between popular tourist attractions and those during the target verification period; performing verification processing on the updated ticketing system during the target verification period based on the verification processing strategy to obtain verification results; and determining an update processing method for popular tourist attractions during the target verification period based on the verification results, thereby improving the reliability of verification processing in the updated ticketing system.

Description

Technical Field

[0001] This invention belongs to the field of ticketing management technology, and in particular relates to an intelligent ticketing management platform and method. Background Technology

[0002] To achieve ticket management for tourist attractions, the invention patent application CN202010816128.0, "Ticket Management Method, Device, Readable Storage Medium, and Electronic Device," describes a server generating ticket orders based on verification results and sending these orders to the client. This system can dynamically and in real-time match the opening, closing, or planned status of ticketed items within the park under the current scenario, helping users obtain accurate and concise ticket information. However, it suffers from the following technical problems:

[0003] After the ticketing system is updated, especially for ticketing systems of popular scenic spots, if the stability of the ticketing system cannot be verified under high concurrency scenarios, the stability of the ticketing system cannot be guaranteed. Therefore, how to schedule the ticket sales periods of popular scenic spots and improve the reliability and efficiency of verification processing while minimizing the impact on ticket sales of popular scenic spots has become an urgent technical problem to be solved.

[0004] Therefore, there is an urgent need for an intelligent ticketing management platform and method. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted:

[0006] Specifically, this application provides a ticketing intelligent management and control method, which includes:

[0007] S1 uses the monitoring data of the ticketing system on the ticketing website to determine the associated scenic spots of the ticketing website. Based on the ticket sales data of the associated scenic spots, it determines the popular sales scenic spots among the associated scenic spots. Based on the overlap of the ticket sales time periods of the popular sales scenic spots, it determines that the ticketing system needs to be updated and managed, and then proceeds to the next step.

[0008] S2 uses the popular scenic spots data during the ticket sales period as a basis, and combines the overlap of the popular scenic spots during the ticket sales period with other ticket sales periods to determine the verification target period during the ticket sales period. Based on the overlap between the popular scenic spots and the popular scenic spots in the verification target period in different ticket sales periods, S2 determines the verification processing strategy for the popular scenic spots in the verification target period.

[0009] S3 performs verification processing on the updated ticketing system within the target verification period based on the verification processing strategy to obtain the verification processing result. Based on the verification processing result, the update processing method for popular sales attractions within the target verification period is determined.

[0010] The beneficial effects of this invention are as follows:

[0011] Based on the overlap between popular tourist attractions and those within the target verification period, a verification processing strategy is determined for popular tourist attractions within the target verification period. This strategy prioritizes tourist attractions with lower overlap in ticket sales periods within the target verification period, avoiding situations where there is significant overlap with the ticket sales periods of popular tourist attractions within the target verification period. Furthermore, this mitigates the technical challenges of excessive sales pressure in other ticket sales periods caused by ticket system anomalies during the target verification period, thus reducing the impact of ticket system verification on popular tourist attractions.

[0012] Based on the verification results, a method for updating popular tourist attractions during the verification target period is determined. This avoids the technical problem of some popular tourist attractions experiencing a decrease in sales popularity, making it impossible to effectively verify the ticketing system during the verification target period. By updating popular tourist attractions during the verification target period, the efficiency and reliability of the verification process are further improved.

[0013] Furthermore, the associated scenic spots of the ticketing website are those that sell tickets on the ticketing website.

[0014] Furthermore, the ticket sales data of the associated scenic spots are determined based on the sales volume of tickets for the associated scenic spots on the ticketing website.

[0015] Furthermore, the method for determining the popular tourist attractions among the associated scenic areas is as follows:

[0016] Based on the ticket sales data of the associated scenic spots, determine the sell-out duration during the ticket sales period of the associated scenic spots;

[0017] The sold-out duration is used to determine whether the associated scenic spot is a popular tourist attraction.

[0018] Furthermore, the method for determining the updating and processing method of popular sales areas in the target verification period is as follows:

[0019] Based on the verification processing results, the ratio of the number of ticket buyers at the popular ticket sales venues during the target verification period to the average number of ticket buyers at the popular ticket sales venues during different ticket sales periods in history is determined and used as the simulated matching ratio.

[0020] The ratio of the number of popular tourist attractions selling tickets during the target period of verification to the maximum number of popular tourist attractions selling tickets during different ticket sales periods is used as the simulated tourist attraction number ratio.

[0021] Based on the ratio of simulated scenic spots in the target verification period and the simulated matching values ​​of different popular scenic spots, an update processing method for popular scenic spots in the target verification period is determined.

[0022] Secondly, the present invention provides a ticketing intelligent management and control platform, employing the aforementioned ticketing intelligent management and control method, specifically including:

[0023] Target time period determination module, verification strategy determination module, and update processing module;

[0024] The target time period determination module is responsible for determining the target time period for verification within the ticket sales period.

[0025] The verification strategy determination module is responsible for determining the verification processing strategy for the popular sales areas during the target verification period;

[0026] The update processing module is responsible for determining the update processing method for popular sales areas during the target period.

[0027] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0028] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0029] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0030] Figure 1 This is a flowchart of a smart ticketing management method;

[0031] Figure 2 This is a flowchart illustrating the method for determining popular tourist attractions within a shared scenic area.

[0032] Figure 3This is a flowchart for determining the need for update and control processing of the ticketing system;

[0033] Figure 4 This is a flowchart illustrating the method for determining the target verification period during ticket sales. Detailed Implementation

[0034] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0035] Example 1

[0036] like Figure 1 As shown, this application provides a ticketing intelligent management and control method, specifically including:

[0037] S1 uses the monitoring data of the ticketing system on the ticketing website to determine the associated scenic spots of the ticketing website. Based on the ticket sales data of the associated scenic spots, it determines the popular sales scenic spots among the associated scenic spots. Based on the overlap of the ticket sales time periods of the popular sales scenic spots, it determines that the ticketing system needs to be updated and managed, and then proceeds to the next step.

[0038] Furthermore, the associated scenic spots of the ticketing website are those that sell tickets on the ticketing website.

[0039] Furthermore, the ticket sales data of the associated scenic spots are determined based on the sales volume of tickets for the associated scenic spots on the ticketing website.

[0040] Ticket sales period: refers to the fixed time windows that the ticketing system opens each day for selling tickets for future dates. For example, the system sets time slots such as 8:00-9:00 AM and 12:00-1:00 PM each day specifically for selling admission tickets for the next day.

[0041] Sold-out duration: For each ticket window on a given day, the length of time from when the window opens for ticket sales (e.g., 8:00:00) until all tickets for future dates are sold out.

[0042] The core logic is that in this scenario, the sell-out time is extremely short, typically within seconds or minutes. This duration directly reflects the scarcity and popularity of the attraction's tickets. The shorter the sell-out time, the higher the demand.

[0043] Judgment logic: If a scenic spot sells out its tickets for future dates in a very short time during multiple ticket sales periods (such as 8 am and 12 pm) every day, then the scenic spot is a "popular scenic spot".

[0044] Specifically, such as Figure 2 As shown, the method for determining the popular tourist attractions among the associated scenic areas is as follows:

[0045] S11 uses the ticket sales data of the associated scenic spot to determine the sell-out duration during the ticket sales period of the associated scenic spot;

[0046] In the above steps, data preparation is carried out—the sell-out time in seconds for multiple time periods is calculated. The system iterates through the sales data of a scenic spot for each ticket sales time period (8 am session, 12 pm session) in the past period (such as the past 7 days) and calculates the sell-out time for each session.

[0047] It is necessary to evaluate the performance of the scenic area across all available sales opportunities. A truly popular scenic area should demonstrate strong sales momentum across all time windows.

[0048] Sold-out time: For a specific daily sales window, the total time elapsed from its opening until the ticket inventory for the future date it is responsible for selling is depleted. For example, if the 8:00 AM session starts at 8:00:00 and tickets sell out at 8:00:25, the sold-out time is 25 seconds.

[0049] S12 determines whether the associated scenic spot is a popular scenic spot based on the sold-out duration.

[0050] It is understandable that determining whether the associated scenic spot is a popular tourist attraction based on the sold-out duration specifically includes:

[0051] If the number of times the ticket sell-out duration is less than a preset duration threshold in different ticket sales periods is greater than a preset number threshold, and the sales volume in the ticket sales periods where the sell-out duration is less than the preset duration threshold is greater than a preset sales volume threshold, then the associated scenic spot is determined to be a popular sales scenic spot.

[0052] Comprehensive Judgment – ​​Based on stability and speed across multiple time periods, the system checks whether the scenic area's historical performance during all ticket sales periods (8:00 AM and 12:00 PM) meets the condition of "selling out in seconds".

[0053] To identify top-tier tourist attractions that generate a buying frenzy regardless of when tickets are released, it's essential that the attractions possess strong appeal throughout the day, rather than being popular only at a specific time.

[0054] Speed ​​and scale conditions (rapid sell-out): In the past N days (e.g., 7 days), the number of times the ticket sales period of this scenic spot sold out was less than the preset time threshold (e.g., 60 seconds) was more than 3 times, and the number of tickets sold was greater than the preset quantity threshold. At this time, it can be identified as a popular scenic spot.

[0055] Preset duration threshold: An extremely short time standard used to determine whether tickets sell out in seconds. For example, 60 seconds. Preset quantity threshold: A standard for determining whether the sales volume is large enough in terms of the number of tickets sold. This threshold should be set relatively high to exclude niche attractions. For example, for a large theme park, this threshold could be set to 5000 tickets.

[0056] Specifically, such as Figure 3 As shown, it has been determined that an update and control process for the ticketing system is required, specifically including:

[0057] S21 determines the number of popular tourist attractions in different ticket sales periods based on the overlap of ticket sales periods for the popular tourist attractions.

[0058] In the above steps, the concurrent pressure sources are identified—the number of popular scenic spots in each time period is counted. The system traverses each ticket sales period and counts the number of all popular scenic spots where tickets are sold during that period.

[0059] This forms the data foundation for risk assessment. It requires quantifying the potential concurrent pressure for each sales window. This pressure is not evenly distributed but concentrated in certain specific time periods.

[0060] Example: Assume the system has 3 sales periods: Period A (08:00-09:00): 15 popular scenic spots are available for sale; Period B (12:00-13:00): 5 popular scenic spots are available for sale; Period C (18:00-19:00): 2 popular scenic spots are available for sale.

[0061] S22 determines the periods of high risk of lag during the ticket sales period based on the number of popular tourist attractions.

[0062] In the above steps, the risk level is assessed—the period of potential traffic disruption is determined by comparing the number of popular scenic spots in each period with a preset threshold for the number of popular scenic spots.

[0063] A clear quantitative standard is needed to define "high-risk" periods. This threshold is set based on the system's current capacity, historical failure data, and other factors.

[0064] Definition: Preset Threshold for Popular Tourist Attractions: The upper limit on the number of popular tourist attractions the system can stably handle within a single sales period. Exceeding this limit significantly increases the risk of system lag. Let's assume this threshold is set to 3.

[0065] Example and Judgment: Time Period A: 15 popular scenic spots > 3 (threshold) → belongs to the period of risk of lag; Time Period B: 5 popular scenic spots > 3 (threshold) → belongs; Time Period C: 2 popular scenic spots < 3 (threshold) → does not belong;

[0066] Based on the composition data of the time period of the lag risk, S23 determines whether the ticketing system needs to be updated and managed.

[0067] It is understood that the period of risk of lag is the ticket sales period when the number of popular scenic spots exceeds a preset threshold.

[0068] Specifically, based on the composition data of the aforementioned periods of lag risk, it is determined whether an update and control process for the ticketing system is required, including:

[0069] When the number of time periods with potential lag is insufficient, and there are a large number of such time periods, it is necessary to perform testing and verification of the ticketing system after the ticketing system update is completed to determine whether the ticketing system can operate stably.

[0070] In the steps outlined above, an operational strategy is developed—deciding whether to update controls. Based on the overall situation during periods of potential disruption, a final decision is made, translating the risk assessment into a concrete action plan. Different risk scenarios correspond to different operational response strategies.

[0071] When the number of high-risk periods for system glitches does not meet the requirements: "Not meeting the requirements" usually means that there are too many high-risk periods (e.g., more than one high-risk period for system glitches). This means that the system is facing frequent high-pressure challenges in its daily operation, and the decision is to update and manage the ticketing system.

[0072] The prevalence of high-risk periods indicates that the current system architecture or code may no longer meet business requirements. After a simple update, rigorous testing and verification (such as stress testing and load testing) must be performed to simulate concurrent scenarios during high-risk periods, ensuring the updated system can run stably and preventing it from crashing under high pressure immediately after deployment.

[0073] When the number of periods of system lag risk meets the requirements, the decision is: no emergency update or control measures are needed; routine system maintenance can proceed as planned.

[0074] It should be noted that the popular tourist attractions during the ticket sales period are those attractions where tickets are sold during the specified ticket sales period.

[0075] S2 uses the popular scenic spots data during the ticket sales period as a basis, and combines the overlap of the popular scenic spots during the ticket sales period with other ticket sales periods to determine the verification target period during the ticket sales period. Based on the overlap between the popular scenic spots and the popular scenic spots in the verification target period in different ticket sales periods, S2 determines the verification processing strategy for the popular scenic spots in the verification target period.

[0076] In this application, the most suitable "verification target period" for system stress testing is determined from all ticket sales periods. This period should most realistically reflect the complex situations the system may encounter under high concurrency.

[0077] A good stress test scenario not only needs high concurrency (many popular scenic spots), but also complex business scenarios (diverse user behaviors). If popular scenic spots that sell tickets in one time period also sell tickets in many other time periods, then if there are sales anomalies in the verification period, it may affect ticket sales in other time periods. The higher the degree of overlap, the greater the impact.

[0078] Definitions: Matching Scenic Spots: Popular scenic spots that sell tickets during the currently analyzed ticket sales period. Overlapping Ticket Sales Periods: For two different popular scenic spots, the periods during which they share ticket sales. For example, if scenic spot A and scenic spot B both sell tickets at "08:00" and "12:00", then their overlapping periods are these two times.

[0079] Single-sale attraction: A popular attraction that sells tickets during the currently analyzed ticket sales period, but not during any other sales periods. All its sales pressure is concentrated in this one period.

[0080] Specifically, such as Figure 4 As shown, the method for determining the verification target time period in the ticket sales period is as follows:

[0081] A ticketing system has three sales periods: T1 (08:00), T2 (12:00), and T3 (18:00). We now need to analyze whether the T1 (08:00) period is the "verification target period".

[0082] Given data: Scenic spot A: sold in T1, T2, T3; Scenic spot B: sold in T1, T2; Scenic spot C: sold in T1, T3; Scenic spot D: sold in T1, T2; Scenic spot E: sold in T1, T2, T3. Match the following scenic spots (sold in T1): A, B, C, D, E (quantity = 5).

[0083] S31 uses the popular scenic spots data during the ticket sales period as a basis to determine the popular scenic spots where tickets are sold during the ticket sales period and uses them as the matching scenic spots.

[0084] S32 determines the number of overlapping ticket sales periods between different matching scenic spots based on the overlap between popular scenic spots in other ticket sales periods during the ticket sales period.

[0085] S33 determines whether the ticket sales period is the target period for verification based on the number of ticket sales periods that overlap between the matched sales scenic spots and different matched sales scenic spots.

[0086] It is understood that, based on the number of ticket sales periods that overlap between the matched scenic spots and different matched scenic spots, determining whether the ticket sales period is the target period for verification specifically includes:

[0087] S331 obtains the number of matched sales attractions, and determines whether the number of matched sales attractions is greater than a preset sales attraction number threshold. If yes, proceed to the next step; otherwise, determine that the ticket sales period does not belong to the verification target period.

[0088] In the above steps, basic popularity screening is performed to determine if the number of matching sales attractions (5) > the preset sales attraction threshold (assumed to be 3), ensuring that there are enough popular attractions during this period to generate basic concurrent pressure. If there are too few popular attractions, they do not qualify for a typical pressure scenario, and the decision is to proceed to the next step.

[0089] S332 determines the matching sales areas that do not overlap with other matching sales areas by the number of overlapping ticket sales periods between different matching sales areas, and determines whether there are matching sales areas among the ticket sales periods that do not overlap with other matching sales areas. If not, proceed to the next step; if yes, determine that the ticket sales period does not belong to the verification target period.

[0090] In the above steps, the exclusive traffic source is identified, and it is determined whether there is a "solely sold scenic spot" (i.e., a time period that does not overlap with other scenic spots) in the current time period (T1). If there is a separately sold scenic spot, ticket sales will be carried out in the current time period. If there is a problem, it may cause the scenic spot's tickets to be unable to be sold normally. Therefore, it is determined that the ticket sales time period does not belong to the verification target time period. Decision: After inspection, scenic spots D and E are "solely sold scenic spots", and the condition is not met.

[0091] S333 determines the overlap impact factor between different ticket sales periods based on the number of overlapping ticket sales periods. Based on the overlap impact factor between different ticket sales periods, it determines whether the average value of the overlap impact factor between different ticket sales periods is greater than the preset impact factor threshold. If so, it determines that the ticket sales period does not belong to the verification target period. If not, it proceeds to the next step.

[0092] In the steps above, the degree of traffic dispersion is assessed, and the overlap impact factor is calculated. This factor is positively correlated with the number of overlapping ticket sales periods (the more overlapping periods, the larger the factor). Simply put: the larger the factor, the more similar the sales plans among the scenic spots, and the more dispersed the traffic.

[0093] Judgment: Calculate the average of the overlapping impact factors among all matching sales scenic spots, and determine whether it is greater than the preset impact factor threshold. If most popular scenic spots have overlapping sales in multiple time periods, any abnormal sales will affect the normal sales in other time periods, which may make the overall sales reliability poor. Therefore, it is necessary to consider the overlap situation.

[0094] Decision: If the average value is large (condition "yes"), it indicates a high degree of overlap, and this period does not belong to the target period for verification. If the average value is small (condition "no"), proceed to the final step S334.

[0095] S334 determines the matching coefficient of the ticket sales period based on the overlap influencing factors between different ticket sales periods and the number of matching scenic spots, and determines whether the ticket sales period is the verification target period based on the matching coefficient.

[0096] In the above steps, a comprehensive matching coefficient is evaluated and calculated. This coefficient is positively correlated with the number of matching scenic spots and negatively correlated with the overlap factor. The final decision is made if the matching coefficient exceeds the preset matching coefficient threshold. Even when there are no exclusive scenic spots and the overlap is low, if the total number of popular scenic spots during that period is very large, the overall concurrent pressure may still be high, making them worthy of being selected as verification targets.

[0097] Decision: If the coefficient exceeds the threshold, then the time period is determined as the target time period for verification.

[0098] In one possible embodiment, the overlap influence factor is calculated in the above steps.

[0099] Calculation method: Calculate the overlap degree of each pair of scenic spots = number of time periods sold together / total number of time periods; calculate the average overlap degree of each scenic spot; calculate the average overlap impact factor for the entire time period.

[0100] Specific calculations: A&B: Both selling in T1, T2 → Overlap = 2 / 3 = 0.67; D&E: Both selling in T1, T2 → Overlap = 2 / 3 = 0.67; Average overlap impact factor = (0.67+0.67+0.67+1.0+0.33+0.67+0.67+0.33+0.67+0.67) / 10 = 0.665;

[0101] Judgment: The average overlap impact factor (0.665) is not greater than the preset impact factor threshold (0.7), and a comprehensive matching coefficient evaluation is performed;

[0102] Matching coefficient = (Number of matched sales attractions / Preset threshold for the number of sales attractions) × (1 / Average overlap factor);

[0103] Specific calculations: Number of scenic spots for matching sales = 5, preset threshold for number of scenic spots for sales = 3, average overlap impact factor = 0.665, matching coefficient = (5 / 3) × (1 / 0.665) = 1.667 × 1.504 = 2.51, judgment: matching coefficient (2.51) > preset matching coefficient threshold (1), final decision: determine T1 (08:00) as the target time period for verification.

[0104] It is understood that when the matching coefficient of the ticket sales period is greater than the preset matching coefficient threshold, the ticket sales period is determined to be the target period for verification.

[0105] It should be noted that the verification processing strategy for popular sales areas during the target verification period is to exclude popular sales areas from conducting sales during the target verification period.

[0106] Business Objective: After the ticketing system update is complete, we need to conduct a high-pressure test (verification). We have already selected a target verification period (e.g., T1). Now we need to decide: Which "popular scenic spots not selling in T1" can be safely and temporarily added to T1 for testing? Adding more popular scenic spots can create greater concurrent pressure and more thoroughly test system performance.

[0107] Risk: The test may fail (e.g., due to a fatal bug), causing chaos in the ticketing data of the participating scenic spots. If this scenic spot is closely associated with many other important scenic spots during multiple real sales periods, the failure of the test may have a chain reaction, disrupting normal sales on a large scale.

[0108] Top-level logic: By analyzing the degree of correlation between a scenic spot and the core testing lineup (i.e., popular scenic spots that are already selling at Tier 1), the risk of adding it to the test can be assessed. The closer the correlation, the higher the risk.

[0109] Specifically, the method for determining the verification processing strategy for the popular sales areas during the target verification period is as follows:

[0110] Assume the target time period for verification is T1, and the core lineup for testing (must be tested in T1) consists of popular tourist attractions A and B (because they are actually selling in T1).

[0111] Areas to be evaluated (proposed to be temporarily added to T1 test): Popular areas C and D (they actually sell tickets at other times);

[0112] Global sales distribution: A: [T1, T2, T3], B: [T1, T3], C: [T2, T3] / / To be evaluated, D: [T2] / / To be evaluated, exclusive scenic area;

[0113] S41, based on the overlap between popular tourist attractions and popular tourist attractions during the verification target time period, determines the overlapping ticket sales periods between the popular tourist attractions and popular tourist attractions during the verification target time period, and uses these overlapping periods as the overlapping periods.

[0114] In the above steps, identify related channels—determine overlapping time periods;

[0115] Action: For each scenic area to be evaluated (e.g., C), identify the actual times during which it shares sales opportunities with each of the core test areas (A, B). This is the first step in risk assessment. The times during which two scenic areas share sales represent the "channels" through which risk can spread if the test fails. Identifying all these "channels" is the foundation for quantifying risk.

[0116] Overlapping periods: This refers to the combined period during which two different popular tourist attractions sell tickets simultaneously. It represents a pathway for risk transmission.

[0117] Example: For scenic spot C: the overlap time between C and A = [T2, T3] (because A and C are both sold in T2 and T3), the overlap time between C and B = [T3] (because B and C are both sold in T3);

[0118] S42 determines the number of overlapping time periods between the popular sales areas and the popular sales areas during the verification target time period based on the overlapping time periods between the popular sales areas and the popular sales areas during the verification target time period;

[0119] In the above steps, the correlation strength is quantified by calculating the number of overlapping time periods. For each correlation found in step S41, the number of time periods in its overlapping time period set is calculated.

[0120] This is risk quantification. The more time slots two scenic spots share for sales, the deeper the "risk entanglement" between them. If the test fails, the impact on one scenic spot will spread to the other through more channels, potentially disrupting more important real sales periods.

[0121] Number of overlapping time periods: This refers to the size of the set of overlapping time periods between two scenic spots. It is a quantitative indicator of the strength of risk association.

[0122] For scenic area C: the number of overlapping time periods between C and A = 2 ([T2, T3]), and the number of overlapping time periods between C and B = 1 ([T3]);

[0123] S43 uses the number of overlapping time periods to determine the verification processing strategy for the popular sales areas during the target verification period.

[0124] It is understandable that, by utilizing the number of overlapping time periods, the verification processing strategy for the popular tourist attractions during the target verification period is determined, specifically including:

[0125] S431 determines whether the popular sales area has only one ticket sales period and whether there is an overlap in ticket sales period with the popular sales area during the verification target period. If yes, it determines that the verification processing strategy for the popular sales area during the verification target period is that it can sell during the verification target period. If no, proceed to the next step.

[0126] In the above steps, a basic risk screening is conducted—to determine if the attraction is an exclusive attraction. This involves assessing whether the attraction sells tickets only during a single sales period, which is a very high-risk indicator. Exclusive attractions rely entirely on this single sales period for their business. If they are included in the test, any risk will affect popular attractions during the target sales period, thus impacting their performance during normal sales times. Example: Attraction D is an exclusive attraction (selling only in T2) and overlaps with attraction A. Attraction D is prohibited from participating in the T1 test.

[0127] S432 obtains the number of verification target time periods for the popular sales attractions, determines whether the number of verification target time periods for the popular sales attractions is greater than a preset threshold for the number of verification target time periods, and if so, determines that the verification processing strategy for the popular sales attractions in the verification target time period is that they can sell during the verification target time period, thereby enabling multiple popular sales attractions to sell simultaneously during the verification target time period, and determines whether the updated ticketing system can operate normally; if not, proceed to the next step.

[0128] Specifically, the test value assessment—whether it is a core test object—determines whether the scenic spot is a member of multiple verification target time periods. If a scenic spot is selected by multiple stress test scenarios, even if there is a risk in a certain verification target time period, the probability of anomalies occurring at the same time is not high due to the existence of multiple verification target time periods, and the impact on normal sales is not high.

[0129] Example: (Assuming that scenic spot C is only considered for inclusion in test T1 and not involved in other tests), Decision: Condition not met, proceed to the next step.

[0130] S433, based on the number of overlapping time periods between the popular sales areas and the popular sales areas in the verification target period, determines whether there are popular sales areas in the verification target period whose number of overlapping time periods exceeds a preset threshold. If so, sales are conducted in the verification target period. If there is a deviation, it may lead to increased sales pressure in the overlapping time periods. Therefore, the verification processing strategy for the popular sales areas in the verification target period is determined to be that sales cannot be conducted in the verification target period. If not, the verification processing strategy for the popular sales areas in the verification target period is determined to be that sales can be conducted in the verification target period, thereby enabling simultaneous sales by multiple popular sales areas in the verification target period and determining whether the updated ticketing system can operate normally.

[0131] In the above steps, the specific risk decision—whether the correlation strength exceeds the threshold—is determined by judging whether the number of overlapping time periods between the scenic area to be evaluated and any of the scenic areas in the core test lineup exceeds the safety threshold.

[0132] This is sophisticated risk control. Even if it's not an exclusive attraction, if it's too closely tied to a core test attraction (e.g., selling tickets together in all three time slots), a test failure could potentially paralyze multiple real sales periods for both attractions simultaneously. This kind of "systemic risk" must be avoided.

[0133] Preset threshold for the number of overlapping time periods: an upper limit set by management for the number of overlapping time periods deemed unacceptable due to risk.

[0134] Example: For scenic spot C: the number of overlapping time periods with A is (2), and the number of overlapping time periods with B is (1). Assuming the preset threshold is 2, determine: Is there a number of overlapping time periods > 2? No (the maximum value is equal to 2, which is not exceeded), decision: scenic spot C is allowed to be added to the T1 test.

[0135] S3 performs verification processing on the updated ticketing system within the target verification period based on the verification processing strategy to obtain the verification processing result. Based on the verification processing result, the update processing method for popular sales attractions within the target verification period is determined.

[0136] The system undergoes stress testing (verification), but the test itself may not be perfect. The purpose of this process is to assess whether the current test's "stress intensity" meets the standard, and if not, how to adjust the test lineup (i.e., add new popular attractions) to increase the stress and ensure the test's effectiveness.

[0137] The evaluation is conducted from two dimensions: Concurrency scale: Are there enough popular scenic spots selling tickets simultaneously during the test period? (Macro indicator) User behavior authenticity: Does the simulated ticket-buying user traffic reach the level of real historical peaks? (Micro indicator)

[0138] Specifically, the method for determining the updating and processing method of popular sales areas in the target verification period is as follows:

[0139] Based on the verification processing results, S51 determines the ratio of the number of ticket buyers at the popular sales venues during the monitoring time in the verification target period to the average number of ticket buyers at the monitoring time in different ticket sales periods in history for the popular sales venues, and uses it as the simulated matching ratio.

[0140] In the above steps, the authenticity of user behavior is assessed by calculating the simulated match ratio. At the monitoring time of the test (e.g., 08:00:00), the instantaneous number of users purchasing tickets for each popular scenic spot (A, B, C) is recorded. This value is then compared with the average number of users purchasing tickets for that scenic spot at the same time during all historical real sales periods.

[0141] The test verifies whether it successfully simulates real-world "ticket-grabbing" user behavior. If the ratio is much less than 1, it indicates that the test traffic is too low, like "using plastic bullets in a simulated shooting," and cannot verify real-world performance.

[0142] Definition: Simulated Match Ratio: The number of users purchasing tickets at the monitored time during the test / the average number of users purchasing tickets during the same historical time period. This ratio measures the realism of the test traffic.

[0143] Example results:

[0144] The simulated matching ratio for scenic area A is 0.9 (the test traffic reached 90% of the historical average).

[0145] S52 takes the ratio of the number of popular tourist attractions selling tickets during the target period of verification to the maximum number of popular tourist attractions selling tickets during different ticket sales periods, and uses it as the simulated tourist attraction number ratio.

[0146] Specifically, assess the concurrency scale—calculate the ratio of simulated scenic spots to the number of popular scenic spots in the current test lineup, and compare this ratio to the maximum number of popular scenic spots that appeared during all sales periods of the system. This verifies whether the concurrency complexity of the test has reached the limits of the system design. If the ratio is small, it indicates that the test scenario is too simple.

[0147] Definition: Simulated Scenic Spot Ratio: Number of popular scenic spots during the test period / Historical maximum number of popular scenic spots. This ratio measures the complexity of the test scenario.

[0148] Example Result: The current test lineup has 3 (A, B, C). If 5 popular scenic spots were on sale at the same time during a certain period, the ratio of the number of simulated scenic spots is 3 / 5 = 0.6.

[0149] S53 determines the update processing method for popular tourist attractions in the verification target period based on the ratio of the number of simulated scenic spots in the target period and the simulated matching value of different popular tourist attractions.

[0150] Furthermore, based on the ratio of simulated scenic spots in the target verification period and the simulated matching values ​​of different popular scenic spots, an update processing method for popular scenic spots in the target verification period is determined, specifically including:

[0151] S531 determines whether the ratio of simulated scenic spots in the target period of verification is greater than the preset ratio threshold. If yes, it determines that the update processing method for popular sales scenic spots in the target period of verification is that no update is required, and only the existing popular sales scenic spots need to be used for verification processing. If no, proceed to the next step.

[0152] In the above steps, is the concurrency scale sufficient? Judgment: The ratio of simulated scenic spots (0.6) is less than the preset ratio threshold (1.2). Decision: Proceed to the next step. The concurrency scale tested is far from reaching the system limit and needs to be strengthened.

[0153] S532 determines whether the ratio of the number of simulated scenic spots is less than a preset value (e.g., 1). If so, the method for updating the popular sales scenic spots in the verification target period is to use the first preset condition to determine the newly added popular sales scenic spots in the verification target period. If not, proceed to the next step.

[0154] In the above steps, is the concurrent scale seriously insufficient? Judgment: The ratio of simulated scenic spots (0.6) < 1? Yes, decision: adopt the first preset condition to determine the new scenic spots. Because the concurrent scale is seriously insufficient, the adjustment strategy can be relatively lenient, and priority should be given to rapidly increasing the number of concurrent scenic spots.

[0155] (Hypothetical deduction) If S532 judges "no" (i.e., ratio = 1), it means that the concurrency scale has reached its limit, but the user traffic is insufficient. Then proceed to S533 for in-depth analysis from the perspective of user behavior.

[0156] S533 uses simulated matching values ​​of different popular sales areas to determine whether there are popular sales areas with simulated matching values ​​less than a preset matching threshold. If so, proceed to the next step; otherwise, determine that the update processing method for popular sales areas in the verification target time period does not need to be updated and only the existing popular sales areas need to be used for verification processing.

[0157] In the steps described above, identifying distorted user behavior involves checking for popular tourist attractions with excessively low simulated match rates. This is done by examining each popular tourist attraction in the current test lineup to see if its simulated match ratio is below a preset match threshold. This marks the shift from macro-level concurrency assessment to micro-level user behavior assessment. Even if many tourist attractions are tested, the test is still ineffective if most fail to simulate real ticket-buying traffic. This step aims to identify these "ineffective stress sources."

[0158] Suppose there are 5 popular tourist attractions. Explanation of terms: This step is to detect the authenticity of user behavior. Example judgment: The ratio of tourist attraction B (0.45) < 0.6 (threshold), the ratio of tourist attraction C (0.40) < 0.6 (threshold), there are popular tourist attractions with simulated matching values ​​less than the preset matching threshold.

[0159] S534 identifies popular sales areas with simulated matching values ​​less than a preset matching threshold as verification deviation areas, and determines whether the proportion of the number of verification deviation areas in the popular sales areas during the verification target period is greater than a preset verification deviation area proportion threshold. If so, the update processing method for the popular sales areas during the verification target period is to use a first preset condition to determine the newly added popular sales areas during the verification target period; otherwise, a second preset condition is used to determine the newly added popular sales areas during the verification target period.

[0160] In the above steps, assess the severity of the problem—calculate the proportion of verification deviation scenic spots, mark all popular scenic spots with simulation matching ratios below the threshold as "verification deviation scenic spots", and calculate the proportion of the number of verification deviation scenic spots in the total number of tested scenic spots.

[0161] It's crucial to determine whether distorted user behavior is an isolated or widespread phenomenon. If only a few scenic spots are experiencing insufficient traffic, it might be a problem with the attraction of those spots themselves. However, if more than half of the scenic spots are experiencing insufficient traffic, it indicates a fundamental problem with the traffic generation mechanism or user simulation strategy used in the entire test, requiring more careful handling.

[0162] Definition: Validation Bias Areas: Popular tourist attractions where simulated user traffic (number of ticket buyers) is significantly lower than historical averages during stress testing. They are considered "weak links" or "ineffective stress sources" in the test.

[0163] Percentage of areas with validation deviation: Number of areas with validation deviation / Total number of areas tested. This indicator reflects the prevalence of invalid testing.

[0164] Example Calculation and Judgment: Verification deviation scenic spots: B, C (2 in total), Total number of tested scenic spots: 5 (A, B, C, D, E), Percentage of verification deviation scenic spots = 2 / 5 = 0.4, Percentage of verification deviation scenic spots (0.4) > Preset threshold (0.3), If "Yes" (problem is widespread): Then determine the update processing method as "Adopt the first preset condition". Because the problem is widespread, it indicates that the current testing pressure is far from sufficient, and it is necessary to significantly strengthen the testing lineup and add more scenic spots to try to improve the overall traffic.

[0165] If "No" (the problem is isolated): then the update processing method is determined to be "adopt the second preset condition". Because the problem is isolated, only minor adjustments to the test lineup are needed, specifically replacing or adding a small number of scenic spots.

[0166] Furthermore, the first preset condition is to directly add popular sales areas with a number of overlapping time periods greater than a preset threshold for overlapping time periods to the target verification time period, and to perform verification processing on popular sales areas with a number of overlapping time periods less than a preset value for the number of scenic spots, and perform verification processing on them. The second preset condition is that if there are scenic spots with verification deviations in the most recent unit time period, then the popular sales area with the fewest number of overlapping time periods greater than the preset threshold for overlapping time periods in the current target verification time period is added to the target verification time period, until the target condition is met.

[0167] First preset condition (when the concurrency scale is seriously insufficient), strategy: directly add popular scenic spots with a number of overlapping time periods with the target verification time period that is greater than the preset threshold, and popular scenic spots with a number of scenic spots that is less than the preset value of the number of scenic spots to the test.

[0168] This is a "wide net" strategy. When concurrency is severely insufficient, the primary task is to increase the sources of pressure. This criterion filters out scenic spots that are not highly correlated with the current test lineup (few overlapping time periods), adding them is a manageable risk and can quickly increase the scale of concurrency.

[0169] For example, in the embodiment where there are only popular scenic spots A, B, and C, the condition is: the number of scenic spots with an overlap with the existing time period > 2, and the number of scenic spots < 2 (preset value for the number of scenic spots).

[0170] The second preset condition (when user behavior is distorted) is triggered when S533 (check if there are scenic spots with insufficient traffic) and S534 (check if such scenic spots are the majority) are judged as "yes".

[0171] Strategy: Lean reinforcement. Add only one scenic spot at a time that has the weakest correlation with the current test lineup (fewest overlapping time periods) until the target conditions are met.

[0172] When user traffic is the primary concern, blindly increasing the number of scenic spots may be ineffective. It's necessary to carefully increase pressure sources and continuously monitor the effects to find the optimal combination that effectively stimulates users to rush for tickets.

[0173] Assuming that, according to the above example, the scenic spots in the target time period are A, B, C, D, and E, the target condition is: when the ratio of the instantaneous traffic to the historical peak at all monitoring times during yesterday's test period is less than a set threshold (such as 60%), it is found that the traffic of scenic spot B is insufficient. Among the substitute scenic spots, F has the fewest overlapping time periods with A, B, C, D, and E (1), so F is added to the test, and the new lineup is A, B, C, D, E, and F.

[0174] Continuously monitor whether the "target conditions" are met. If not, continue adding the next scenic area (e.g., G) with the fewest overlapping time periods until the conditions are met.

[0175] The target condition is that the ratio of the number of ticket buyers to the maximum number of ticket buyers in the historical monitoring time of the ticketing system is greater than a set threshold. The monitoring time is considered a valid verification time. If the duration of all monitoring times that are valid verification times in the target verification period is greater than the preset duration, then the verification is determined to be complete.

[0176] Example 2

[0177] Secondly, the present invention provides a ticketing intelligent management and control platform, employing the aforementioned ticketing intelligent management and control method, specifically including:

[0178] Target time period determination module, verification strategy determination module, and update processing module;

[0179] The target time period determination module is responsible for determining the target time period for verification within the ticket sales period.

[0180] The verification strategy determination module is responsible for determining the verification processing strategy for the popular sales areas during the target verification period;

[0181] The update processing module is responsible for determining the update processing method for popular sales areas during the target period.

[0182] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0183] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0184] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A ticketing intelligent management and control method, characterized in that, Specifically, it includes: Based on the monitoring data of the ticketing system on the ticketing website, the associated scenic spots of the ticketing website are identified. Based on the ticket sales data of the associated scenic spots, the popular sales scenic spots among the associated scenic spots are identified. Based on the overlap of the ticket sales time periods of the popular sales scenic spots, if it is determined that the ticketing system needs to be updated and controlled, proceed to the next step. Based on the data of popular tourist attractions during the ticket sales period, and considering the overlap of popular tourist attractions during the ticket sales period with other ticket sales periods, the verification target period during the ticket sales period is determined. Based on the overlap between popular tourist attractions and popular tourist attractions during the verification target period in different ticket sales periods, the verification processing strategy for popular tourist attractions during the verification target period is determined. Based on the verification processing strategy, the updated ticketing system is verified during the target verification period to obtain the verification processing result. Based on the verification processing result, the update processing method for popular tourist attractions during the target verification period is determined.

2. The ticketing intelligent management and control method as described in claim 1, characterized in that, The scenic spots associated with the ticketing website are those that sell tickets on the ticketing website.

3. The ticketing intelligent management and control method as described in claim 1, characterized in that, The ticket sales data for the associated scenic spots are determined based on the sales volume of tickets for the associated scenic spots on the ticketing website.

4. The ticketing intelligent management and control method as described in claim 1, characterized in that, The method for determining the popular tourist attractions among the associated scenic areas is as follows: Based on the ticket sales data of the associated scenic spots, determine the sell-out duration during the ticket sales period of the associated scenic spots; The sold-out duration is used to determine whether the associated scenic spot is a popular tourist attraction.

5. The ticketing intelligent management and control method as described in claim 4, characterized in that, Determining whether the associated scenic spot is a popular tourist attraction based on the sold-out duration specifically includes: If the number of times the ticket sell-out duration is less than a preset duration threshold in different ticket sales periods is greater than a preset number threshold, and the sales volume in the ticket sales periods where the sell-out duration is less than the preset duration threshold is greater than a preset sales volume threshold, then the associated scenic spot is determined to be a popular sales scenic spot.

6. The ticketing intelligent management and control method as described in claim 1, characterized in that, The ticketing system needs to be updated and managed, specifically including: Based on the overlap of ticket sales periods for the aforementioned popular tourist attractions, determine the number of popular tourist attractions in different ticket sales periods; Based on the number of popular tourist attractions, determine the periods of highest risk of lag during the ticket sales period; Based on the composition data of the aforementioned periods of lag risk, determine whether an update and control process for the ticketing system is required; It is understood that the period of risk of lag is the ticket sales period when the number of popular scenic spots exceeds a preset threshold.

7. The ticketing intelligent management and control method as described in claim 6, characterized in that, Based on the composition data of the aforementioned periods of lag risk, determine whether an update and control process for the ticketing system is necessary, specifically including: When the number of time periods with potential lag does not meet the requirements, the ticketing system needs to be tested and verified to determine whether the ticketing system can operate stably.

8. The ticketing intelligent management and control method as described in claim 1, characterized in that, The method for determining the updating and processing method of popular tourist attractions in the target verification period is as follows: Based on the verification processing results, the ratio of the number of ticket buyers at the popular ticket sales venues during the target verification period to the average number of ticket buyers at the popular ticket sales venues during different ticket sales periods in history is determined and used as the simulated matching ratio. The ratio of the number of popular tourist attractions selling tickets during the target period of verification to the maximum number of popular tourist attractions selling tickets during different ticket sales periods is used as the simulated tourist attraction number ratio. Based on the ratio of simulated scenic spots in the target verification period and the simulated matching values ​​of different popular scenic spots, an update processing method for popular scenic spots in the target verification period is determined.

9. The ticketing intelligent management and control method as described in claim 8, characterized in that, Based on the ratio of simulated scenic spots in the target verification period and the simulated matching values ​​of different popular scenic spots, an update processing method for popular scenic spots in the target verification period is determined, specifically including: If the ratio of simulated scenic spots in the target verification period is greater than a preset ratio threshold, then the update processing method for popular scenic spots in the target verification period is determined to be that no update is required, and only the existing popular scenic spots need to be used for verification processing.

10. A ticketing intelligent management and control platform, characterized in that, The ticketing intelligent management and control method according to any one of claims 1-9 specifically includes: Target time period determination module, verification strategy determination module, and update processing module; The target time period determination module is responsible for determining the target time period for verification within the ticket sales period. The verification strategy determination module is responsible for determining the verification processing strategy for the popular sales areas during the target verification period; The update processing module is responsible for determining the update processing method for popular sales areas during the target period.

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