Intelligent dynamic traffic limiting method and system for sports event tickets

Abstract

The invention discloses an intelligent dynamic traffic limiting method and system for sports event tickets. The method comprises the following steps: acquiring a ticket buying request initiated by a user for a specific site area in a target stadium; according to the user credit rating corresponding to the ticket buying request, the historical behavior score and the area weight of the specific site area, calculating a comprehensive current limiting threshold value of the user; and if the instantaneous flow is greater than the reference threshold, determining a processing mode of the ticket buying request according to the comprehensive flow limiting threshold and the reference threshold. According to the method, the dynamic threshold value is constructed through the user credit rating and the historical behavior score, so that the false interception rate of normal users can be remarkably reduced. Moreover, the historical behavior score is used as a core calculation factor, and features such as an abnormal ticket buying path of the cattle can be captured in real time, so that accurate current limiting is realized. In addition, the ticket buying requests of different seat areas are subjected to differentiation processing through the area weights, and refined flow distribution can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-concurrency system flow control, and in particular to an intelligent dynamic flow limiting method and system for sports event ticketing. Background Art

[0002] Sports event ticket sales, characterized by extremely high instantaneous traffic peaks, significant differences in user behavior, and strict resource allocation requirements, pose significant challenges to the dynamic, intelligent, and fine-grained control of traffic limiting strategies. Traditional traffic limiting technologies currently used in the industry primarily include fixed threshold traffic limiting algorithms and token bucket algorithms based on fixed parameters.

[0003] Fixed threshold throttling technology uses a preset upper limit on system processing capacity to directly block subsequent requests when the number of concurrent requests exceeds the threshold. This method is simple to implement, but its core flaw is that the threshold parameters cannot be dynamically adjusted to suit the business scenario. Especially before the start of a popular event, when user traffic is growing exponentially, static thresholds can easily lead to excessive blocking of legitimate user requests, resulting in congestion at the ticket purchasing entrance and a serious impact on the user experience.

[0004] The traditional token bucket algorithm controls traffic by generating tokens at a fixed rate and requiring requests to obtain tokens before they can pass. Its limitation is that when faced with dynamic distributed attacks launched by scalper clusters using proxy IPs, automated scripts, etc., it is unable to identify abnormal traffic patterns and adjust token generation strategies, resulting in a large number of tokens being consumed by malicious requests and real user requests being rejected.

[0005] In addition, existing technologies generally lack refined traffic control strategies tailored to user seat selection behavior, and are unable to implement differentiated flow control based on dimensions such as seat popularity and regional distribution. This often leads to resource mismatches, with popular seats experiencing system response delays due to traffic shocks, while ordinary seats experience inventory backlogs due to overly strict flow control.

[0006] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide an intelligent dynamic flow limiting method and system for sports event ticketing in response to the above-mentioned defects of the existing technology, aiming to solve the problems of normal user requests being excessively intercepted, tokens being consumed by malicious requests, and lack of regional differentiated flow limiting in the existing flow limiting technology.

[0008] The technical solutions adopted by the present invention to solve the problem are as follows:

[0009] In a first aspect, an embodiment of the present invention provides an intelligent dynamic flow limiting method for sports event ticketing, the method comprising:

[0010] Obtain a user's ticket purchase request for a specific area within a target stadium;

[0011] Obtaining the user credit rating, historical behavior score, and regional weight of the specific venue area corresponding to the ticket purchase request;

[0012] Calculating a comprehensive flow limiting threshold for the user based on the user's credit rating, the historical behavior score, and the regional weight of the specific venue area;

[0013] If the instantaneous flow rate is greater than the reference threshold, the processing method of the ticket purchase request is determined according to the comprehensive flow limiting threshold and the reference threshold.

[0014] In one embodiment, the method for obtaining the user credit rating includes:

[0015] Obtain historical ticket purchase data of the user account corresponding to the ticket purchase request;

[0016] The user credit rating is obtained based on the historical ticket purchase data.

[0017] In one embodiment, the method for obtaining the historical behavior score includes:

[0018] Obtain historical behavior data of the request interface corresponding to the ticket purchase request;

[0019] Scoring is performed based on the historical behavior data to obtain the historical behavior score.

[0020] In one embodiment, the method for obtaining the regional weight of the specific site area includes:

[0021] Obtaining the revenue contribution ratio of the specific venue area to the target sports venue;

[0022] According to the revenue contribution ratio, the regional weight of the specific venue area is obtained.

[0023] In one embodiment, calculating the user's comprehensive flow limiting threshold based on the user's credit rating, the historical behavior score, and the regional weight of the specific venue area includes:

[0024] Determining a correction factor according to the user's credit rating, the historical behavior score, and the regional weight of the specific venue area;

[0025] The comprehensive current limiting threshold of the user is obtained by multiplying the three correction factors in sequence according to the reference threshold.

[0026] In one embodiment, determining a processing method for the ticket purchase request according to the comprehensive current limiting threshold and the reference threshold includes:

[0027] If the comprehensive current limiting threshold is greater than the reference threshold, the application priority is determined according to the comprehensive current limiting threshold, and a token application is performed according to the application priority. When the token application is successful, business processing is performed according to the ticket purchase request; wherein the token generation rate is adjusted according to the remaining number of seats in the target stadium;

[0028] If the comprehensive current limiting threshold is less than or equal to the reference threshold, the token application is rejected.

[0029] In one embodiment, the method further comprises:

[0030] When the specific venue area is a VIP area, an independent token pool is used to process the ticket purchase request.

[0031] In one embodiment, the method further comprises:

[0032] If the time interval between the request time and the opening time of the ticket purchase request is less than the preset time length, and the user requests to purchase tickets for more than one venue area type, the user's ticket purchase request will be fused.

[0033] In a second aspect, an embodiment of the present invention further provides an intelligent dynamic flow limiting system for sports event ticketing, the system comprising:

[0034] A request acquisition module is used to acquire a ticket purchase request initiated by a user for a specific venue area in a target stadium;

[0035] A data acquisition module, configured to acquire a user credit rating, a historical behavior score, and a regional weight of the specific venue area corresponding to the ticket purchase request;

[0036] A data analysis module, configured to calculate a comprehensive flow limiting threshold for the user based on the user's credit rating, the historical behavior score, and the regional weight of the specific venue area;

[0037] The request processing module is used to determine the processing method of the ticket purchase request based on the comprehensive current limiting threshold and the reference threshold if the instantaneous flow is greater than the reference threshold.

[0038] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded and executed by a processor to implement any of the steps of the above-mentioned intelligent dynamic flow limiting method for sports event ticketing.

[0039] Beneficial effects of the present invention: The embodiment of the present invention obtains the ticket purchase request initiated by the user for a specific venue area in the target stadium; calculates the user's comprehensive flow limiting threshold through the user's credit level, historical behavior score and regional weight of the specific venue area corresponding to the ticket purchase request; if the instantaneous flow is greater than the reference threshold, the processing method of the ticket purchase request is determined according to the comprehensive flow limiting threshold and the reference threshold. The present invention constructs a dynamic threshold through the user's credit level and historical behavior score, which can significantly reduce the false interception rate of normal users. In addition, the present invention uses the historical behavior score as the core calculation factor, which can capture the abnormal ticket purchase path and other characteristics of the scalper in real time, thereby achieving accurate flow limiting. In addition, the present invention implements differentiated processing of ticket purchase requests in different seating areas through regional weights, which can achieve refined flow distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 This is a flow chart of an intelligent dynamic flow limiting method for sports event ticketing provided by an embodiment of the present invention.

[0042] Figure 2 This is a system flow chart of an intelligent dynamic flow limiting method for sports event ticketing provided by an embodiment of the present invention.

[0043] Figure 3 This is a schematic diagram of venue area weight distribution provided by an embodiment of the present invention.

[0044] Figure 4 This is a state transition diagram of a dynamic token pool provided by an embodiment of the present invention.

[0045] Figure 5 This is a module diagram of an intelligent dynamic current limiting system for sports event ticketing provided by an embodiment of the present invention.

[0046] Figure 6 This is a principle block diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The present invention discloses an intelligent dynamic flow limiting method and system for sports event ticketing. To make the objectives, technical solutions, and effects of the present invention more clear and explicit, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention.

[0048] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the description of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0049] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0050] To address the aforementioned shortcomings of the prior art, the present invention provides an intelligent dynamic traffic limiting method for sports event ticketing. The method obtains user-initiated ticket purchase requests for specific venue zones within a target stadium; obtains the user's credit rating, historical behavior score, and the regional weight of the specific venue zone corresponding to the ticket purchase request; calculates the user's comprehensive traffic limiting threshold based on the user's credit rating, historical behavior score, and regional weight of the specific venue zone; and, if the instantaneous traffic exceeds a baseline threshold, determines how to handle the ticket purchase request based on the comprehensive traffic limiting threshold and the baseline threshold. The present invention constructs dynamic thresholds based on the user's credit rating and historical behavior score, granting more relaxed traffic limiting thresholds to high-credit users, significantly reducing the false blocking rate for normal users. Furthermore, the present invention uses the historical behavior score as a core calculation factor, enabling real-time detection of abnormal characteristics such as scalpers' high-frequency requests and proxy IP switching. By lowering the comprehensive threshold, precise traffic limiting is achieved, effectively reducing scalpers' attack capabilities. Furthermore, the present invention uses regional weights to differentiate ticket purchase requests from different seating zones, tightening traffic limiting in popular zones to prevent instantaneous overloads and relaxing restrictions in less popular zones to promote resource flow, thus achieving refined traffic allocation.

[0051] like Figure 1 As shown, the method specifically includes the following steps:

[0052] Step S100: Obtain a ticket purchase request initiated by a user for a specific venue area in a target sports stadium.

[0053] Specifically, the target stadium in this embodiment can be any stadium that has a ticket purchasing system installed. The target stadium typically has multiple functional areas, such as a grandstand area, a VIP area, and a general area. In actual use, the system will receive one or more ticket purchase requests from a user and collect relevant information about the user's ticket purchase process for subsequent analysis to determine whether the user's ticket purchase request is normal.

[0054] Step S200: Obtain the user credit rating, historical behavior score, and area weight of the specific venue area corresponding to the ticket purchase request.

[0055] Specifically, a user credit rating is a credit assessment system built based on historical ticket purchase behavior, compliance, account qualifications, and other factors, used to quantify a user's trustworthiness. The purpose of establishing a user credit rating is that high-credit users generally represent genuine ticket purchase demand, resulting in a higher request approval rate during throttling, thereby reducing the likelihood of legitimate users being blocked. A historical behavior score is a risk score generated by analyzing a user's historical ticket purchase behavior patterns (such as request frequency and operation history). It is used to identify abnormal traffic (such as scalper automated script attacks). The historical behavior score identifies high-frequency abnormal requests, lowering their approval threshold during throttling, and precisely curbing malicious attacks. Regional weighting is a weighting factor set based on factors such as the popularity and commercial value of different seating areas within a stadium. It is used to differentiate the strictness of traffic control in different venue areas. Regional weighting implements stricter throttling in popular areas (high weighting) to prevent system overload, while less stringent throttling is applied to less popular areas (low weighting) to facilitate resource flow. In actual application scenarios, this embodiment needs to obtain the user's credit rating, historical behavior score, and regional weight of a specific venue area. These three types of data can more accurately depict the current ticket purchase scenario characteristics.

[0056] Step S300: Calculate the user's comprehensive flow limiting threshold based on the user's credit rating, the historical behavior score, and the area weight of the specific venue area.

[0057] Specifically, if Figure 2As shown, this embodiment integrates multi-dimensional features, including user credit rating, historical behavior score, and specific venue area, and determines the current user's comprehensive throttling threshold based on the fusion results. The comprehensive throttling thresholds corresponding to normal and abnormal ticket purchase requests differ significantly. Using comprehensive throttling thresholds for throttling decisions transforms traffic control into intelligent filtering based on the characteristics of the ticket purchase scenario. This not only ensures a genuine user's ticket purchase experience, but also precisely combats malicious traffic through differentiated strategies, while optimizing the efficient allocation of scarce resources.

[0058] In one implementation, the method for obtaining the user credit rating includes:

[0059] Obtain historical ticket purchase data of the user account corresponding to the ticket purchase request;

[0060] The user credit rating is obtained based on the historical ticket purchase data.

[0061] Specifically, by obtaining the historical ticket purchase data of the user account associated with the current ticket purchase request, analyzing or setting the historical ticket purchase data in a targeted manner, the user credit rating of the current user is obtained. The level of the user credit rating reflects the credibility of the user. In actual application, the basic account characteristics (such as registration duration, membership level, and degree of real-name authentication) can be extracted through the user account associated with the current ticket purchase request; and the historical ticket purchase data can be obtained through the user account, and the account purchase characteristics (such as the frequency of on-time payments, the frequency of ticket refunds, and the frequency of malicious ticket grabbing / ticket hoarding) can be extracted based on the historical ticket purchase data. By analyzing and extracting the basic account characteristics and account purchase characteristics, an appropriate user credit rating is set for the user.

[0062] In one implementation, the method for obtaining the historical behavior score includes:

[0063] Obtain historical behavior data of the request interface corresponding to the ticket purchase request;

[0064] Scoring is performed based on the historical behavior data to obtain the historical behavior score.

[0065] Specifically, system logs can collect and record details of user calls to request interfaces in real time, generating historical behavior data and forming a behavioral trajectory sequence. This behavioral trajectory sequence can be used to analyze user ticket purchasing behavior patterns and assign scores accordingly. The historical behavior score can be used to identify abnormal ticket purchasing behavior (such as automated scalper script attacks).

[0066] In one implementation, scoring the historical behavior data to obtain the historical behavior score includes:

[0067] determining a ticket purchase behavior path corresponding to the ticket purchase request according to the historical behavior data;

[0068] Obtain a standard ticket purchase behavior path, and compare the type, quantity, arrangement order, and request time difference of the request behaviors in the ticket purchase behavior path with those in the standard ticket purchase behavior path;

[0069] The historical behavior score is calculated based on the comparison result.

[0070] The historical behavior score is mainly obtained by analyzing the similarity between the current ticket purchase behavior path and the standard ticket purchase behavior path. Specifically, the historical behavior data is mainly obtained through request interface detection. The current ticket purchase path is analyzed through historical behavior data. For example, the typical ticket purchase path is to first view the venue details, then select the venue type, then select the time period, and finally complete the payment. At the same time, the historical behavior data also needs to analyze the time difference, that is, the time interval between the two steps. For example, the interval between machine attack operations is extremely short or fixed. Figure 4 As shown, the historical behavior score calculation logic is as follows: if a step is missing, such as skipping the venue details page and directly selecting the venue type, the missing step will be deducted by the preset first point value per step; if the sequence is incorrect, such as first selecting the time period and then returning to select the venue type, the preset second point value per time will be deducted; if the time difference is abnormal, such as the interval between two steps exceeds 600 seconds (such as the user leaving the page for too long), the preset third point value per time will be deducted; if the interval is extremely fast than human reaction time (such as the interval is less than 1 second, which may be a scripted automated operation), the preset fourth point value per time will be deducted. Normal operation, that is, the steps in the ticket purchase behavior path are completed and executed in sequence, and the time difference between each step is reasonable, will be scored 1 point.

[0071] In one implementation, the method for obtaining the area weight of the specific site area includes:

[0072] Obtaining the revenue contribution ratio of the specific venue area to the target sports venue;

[0073] According to the revenue contribution ratio, the regional weight of the specific venue area is obtained.

[0074] like Figure 3As shown, this embodiment will configure a regional weight for different types of venue areas based on factors such as the popularity and commercial value of different seating areas in the stadium, so as to distinguish the strictness of flow control in different venue areas, thereby realizing differentiated management of different areas. Specifically, the regional weight of each venue area is related to its own regional popularity, and regional popularity is related to its own revenue contribution. Taking a venue area as an example, the higher the proportion of revenue contribution of the venue area, the higher the regional popularity of the venue area, and the higher the regional weight assigned to the venue area. In actual application scenarios, the historical ticket sales popularity corresponding to each venue area can also be used as one of the reference factors for setting the regional weight. In addition, after the sale starts, the regional weight of the venue area can be dynamically adjusted in real time based on the remaining inventory of each venue area and the popularity of the user's real-time click feedback.

[0075] In one implementation, calculating the user's comprehensive traffic limiting threshold based on the user's credit rating, the historical behavior score, and the area weight of the specific venue area includes:

[0076] Determining a correction factor according to the user's credit rating, the historical behavior score, and the regional weight of the specific venue area;

[0077] The comprehensive current limiting threshold of the user is obtained by multiplying the three correction factors in sequence according to the reference threshold.

[0078] Specifically, the essence of the correction factor is to convert user and regional attributes into quantifiable adjustment coefficients, which are used to dynamically adjust the throttling threshold. This embodiment generates a correction factor based on the user's credit rating, historical behavior score, and the regional weight of a specific venue area. Combined with a pre-determined baseline threshold, a personalized comprehensive throttling threshold is generated for the current user.

[0079] For example, a multi-dimensional weighted traffic limiting model is pre-built to calculate the comprehensive traffic limiting threshold for each user. The calculation method of the comprehensive traffic limiting threshold T is as follows:

[0080] T=T_base×(1+W_zone)×(1-W_credit)×(1-W_behavior);

[0081] T_base is the baseline threshold in the rate limiting system. This threshold can be set based on the request frequency consistently handled by a single server during stress testing. For example, if a single server consistently handles 100 requests per second during stress testing, T_base can be set to 100, representing the token pool capacity. W_zone is the weight of the venue zone. W_credit is the weight corresponding to the user's credit rating. W_behavior is the weight corresponding to the historical behavior score.

[0082] As shown in Table 1, in different scenarios of user ticket purchase, the comprehensive current limiting threshold will change, thereby identifying users with abnormal ticket purchases.

[0083] Table 1. Typical scenario verification results

[0084]

[0085] Step S400: If the instantaneous flow rate is greater than the reference threshold, the processing method of the ticket purchase request is determined according to the comprehensive flow limiting threshold and the reference threshold.

[0086] Specifically, the baseline threshold is the system's default security traffic threshold, representing the maximum number of concurrent requests the system can stably handle when throttling is in place. When instantaneous traffic (i.e., burst traffic) exceeds the baseline threshold, it indicates the system is under pressure or may be under attack, triggering token throttling. Instantaneous traffic refers to the number of requests per second for which the calculated comprehensive throttling threshold exceeds the baseline threshold—that is, the number of legitimate user requests. For example, the default baseline threshold is 100. If the system calculates that the number of legitimate users with a comprehensive throttling threshold exceeding the baseline threshold within 1 second reaches 200, then the instantaneous traffic is 200. In short, the baseline threshold is a global, unified traffic security baseline used to determine whether to enter throttling mode. After token throttling is implemented, the relationship between each user's comprehensive throttling threshold and the baseline threshold is used to determine whether to approve their ticket purchase request. Only users whose calculated comprehensive throttling threshold exceeds the baseline threshold can apply for tokens. Users with higher comprehensive throttling threshold scores receive priority in obtaining tokens and use them for business processing.

[0087] In one implementation, determining a processing method for the ticket purchase request according to the comprehensive current limiting threshold and the reference threshold includes:

[0088] If the comprehensive current limiting threshold is greater than the reference threshold, the application priority is determined according to the comprehensive current limiting threshold, and a token application is performed according to the application priority. When the token application is successful, business processing is performed according to the ticket purchase request; wherein the token generation rate is adjusted according to the remaining number of seats in the target stadium;

[0089] If the comprehensive current limiting threshold is less than or equal to the reference threshold, the token application is rejected.

[0090] Specifically, only users whose calculated comprehensive flow-limiting threshold exceeds the baseline threshold can apply for tokens. They compete for tokens based on priority. Users with higher comprehensive flow-limiting threshold scores indicate high credibility and low risk, and therefore receive priority tokens for business processing. Traditional token buckets generate tokens at a fixed rate. This embodiment dynamically adjusts the token generation rate based on the remaining seats in the entire venue and determines the priority of token applications based on the comprehensive threshold, forming an intelligent token bucket and flexible token allocation mechanism.

[0091] In another implementation, the token generation rate can be adjusted by venue area: the token generation rate of each venue area can be determined based on the area weight and the number of remaining seats of the venue area. For example, the popularity of the venue area can be comprehensively determined based on the area weight and the number of remaining seats of the venue area. A high area weight and a small number of remaining seats indicate that the venue area is highly popular. The token generation rate can be reduced, the processing speed of ticket purchase requests can be delayed, and the seats in the popular venue area can be prevented from being snapped up instantly, leaving enough time for normal users to operate. A low area weight and a large number of remaining seats indicate that the venue area is highly popular. If the venue area is not popular, the token generation rate can be increased, the ticket sales efficiency can be accelerated, and the inventory backlog can be reduced.

[0092] In one implementation, Figure 4 As shown, the comprehensive current limit threshold meets the token application conditions, indicating that the user's token application will consume the token pool. The system replenishes the consumed tokens every second, analyzes the token consumption based on the number of tokens replenished each time, and determines the current status based on the token consumption. In normal mode, a successful token application will enter the system for business processing. In the alert mode, token applications can still be made normally, but the system administrator will receive an alert notification. In the circuit breaker mode, all token applications will fail, and no further tokens will be allowed to enter the business system. In the recovery mode, the system returns to normal.

[0093] In one implementation, the method further includes:

[0094] When the specific venue area is a VIP area, an independent token pool is used to process the ticket purchase request.

[0095] Specifically, the VIP area in this embodiment utilizes a separate token pool and manual review channel, allowing for customized traffic control strategies tailored to the scarcity and high-value nature of the VIP area. In practical applications, ticket purchase requests for the VIP area enter a separate queue, eliminating the need to compete for tokens with requests from regular areas, thus reducing waiting times.

[0096] In another implementation, for users who purchase the VIP area, if they do not have a token and the waiting time in the queue exceeds 10 seconds, their application will fail.

[0097] In one implementation, the method further includes:

[0098] If the time interval between the request time and the opening time of the ticket purchase request is less than the preset time length, and the user requests to purchase tickets for more than one venue area type, the user's ticket purchase request will be fused.

[0099] Specifically, this embodiment also implements a special policy for the rush-buy period: During the preset time period before the show begins, the rush-buy phase begins, and anti-seat reservation mode is activated, which disconnects cross-zone ticket purchase requests from the same account. For example, if the same account applies for both the grandstand area and the standard area, this indicates a cross-zone ticket purchase request, and the request will be disconnected.

[0100] In one implementation, this embodiment can also automatically expand the token pool based on the instantaneous traffic prediction of LSTM.

[0101] Specifically, the time series traffic data is learned through the LSTM model to predict the instantaneous traffic consisting of a reasonable number of user requests per second in real time. When the predicted instantaneous traffic exceeds the baseline threshold, the system triggers the automatic expansion mechanism, and simultaneously realizes server expansion and token pool expansion: on the one hand, by increasing server resources to improve the system's carrying capacity, the server can support more businesses; on the other hand, the token pool is expanded, and the baseline threshold and token pool capacity are dynamically adjusted through hot updates, so that more user requests can enter the business processing flow through the token pool. This embodiment achieves an adaptive response to instantaneous traffic peaks through the coordination of LSTM's precise prediction and dynamic expansion, thereby improving the efficiency of user request processing while ensuring system stability.

[0102] The advantages of the present invention are:

[0103] 1. Dynamic and differentiated traffic limiting strategy: By integrating user credit rating, historical behavior score, and regional weight, a personalized comprehensive traffic limiting threshold is constructed, avoiding the limitations of traditional fixed thresholds and achieving dynamic adaptation of users, behaviors, and resources.

[0104] 2. Accurate traffic identification capability: Introducing user behavior characteristics (credit rating, historical behavior) as flow control decision factors can effectively distinguish between normal users and scalpers' automated attacks, improving the accuracy of abnormal traffic filtering.

[0105] 3. Fine-grained business scenario adaptation: Based on differentiated processing of venue area weights, different flow control strategies can be implemented for popular seats and ordinary seats to optimize resource allocation efficiency.

[0106] 4. Balancing user experience and system stability: Through dynamic threshold calculation, the request approval rate of high-credit, low-risk users is prioritized during peak traffic periods. This not only reduces false interceptions for legitimate users but also curbs the impact of malicious traffic, achieving dual optimization of user experience and performance.

[0107] In summary, the present invention establishes a dynamic and intelligent flow limiting decision-making system from the two levels of user feature quantification and regional value assessment, fundamentally avoiding the one-size-fits-all limitations of traditional flow limiting technology and realizing a technical upgrade of flow control and precise scene adaptation.

[0108] Based on the above embodiments, the present invention also provides an intelligent dynamic current limiting system for sports event ticketing, such as Figure 5 As shown, the system includes:

[0109] Request acquisition module 01, used to obtain a ticket purchase request initiated by a user for a specific venue area in a target stadium;

[0110] Data acquisition module 02, used to obtain the user credit rating, historical behavior score and regional weight of the specific venue area corresponding to the ticket purchase request;

[0111] Data analysis module 03, used to calculate the user's comprehensive flow limiting threshold based on the user's credit rating, the historical behavior score and the regional weight of the specific venue area;

[0112] The request processing module 04 is used to determine the processing method of the ticket purchase request based on the comprehensive flow limiting threshold and the reference threshold if the instantaneous flow is greater than the reference threshold.

[0113] Based on the above embodiment, the present invention further provides a terminal, whose principle block diagram can be shown as follows: Figure 6 As shown. The terminal includes a processor, a memory, a network interface, and a display screen connected via a system bus. The processor of the terminal is used to provide computing and control capabilities. The memory of the terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the terminal is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an intelligent dynamic current limiting method for sports event ticketing is implemented. The display screen of the terminal can be a liquid crystal display or an electronic ink display.

[0114] Those skilled in the art will understand that Figure 6The principle block diagram shown in the figure is only a block diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the terminal to which the solution of the present invention is applied. The specific terminal may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0115] In one implementation, the terminal stores one or more programs in its memory and is configured to be executed by one or more processors. The one or more programs include instructions for performing an intelligent dynamic flow limiting method for sports event ticketing.

[0116] Those skilled in the art will appreciate that all or part of the processes in the above-described embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described embodiments. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0117] In summary, the present invention discloses an intelligent dynamic flow limiting method and system for sports event ticketing. The method includes: obtaining a ticket purchase request initiated by a user for a specific venue area in a target sports stadium; obtaining the user credit rating, historical behavior score and regional weight of the specific venue area corresponding to the ticket purchase request; calculating the user's comprehensive flow limiting threshold according to the user credit rating, the historical behavior score and the regional weight of the specific venue area; if the instantaneous flow is greater than the benchmark threshold, determining the processing method of the ticket purchase request according to the comprehensive flow limiting threshold and the benchmark threshold. The present invention constructs a dynamic threshold through the user credit rating and historical behavior score, gives a more relaxed flow limiting threshold to high-credit users, and significantly reduces the false blocking rate of normal users. In addition, the present invention uses the historical behavior score as the core calculation factor, which can capture the abnormal characteristics of scalpers such as high-frequency requests and proxy IP switching in real time, and achieves precise flow limiting by lowering its comprehensive threshold, effectively consuming the scalper's attack capabilities. In addition, the present invention implements differentiated processing of ticket purchase requests in different seating areas through regional weights, increases the strictness of flow control in popular areas to prevent instantaneous overload, relaxes restrictions in ordinary areas to promote resource flow, and realizes refined traffic distribution.

[0118] It should be understood that the application of the present invention is not limited to the above examples. For those skilled in the art, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. An intelligent dynamic flow limiting method for sports event ticketing, characterized in that: The method comprises: Obtain a user's ticket purchase request for a specific area within a target stadium; Obtaining the user credit rating, historical behavior score, and regional weight of the specific venue area corresponding to the ticket purchase request; Calculating a comprehensive flow limiting threshold for the user based on the user's credit rating, the historical behavior score, and the regional weight of the specific venue area; If the instantaneous flow rate is greater than the reference threshold, the processing method of the ticket purchase request is determined according to the comprehensive flow limiting threshold and the reference threshold.

2. The intelligent dynamic flow limiting method for sports event ticketing according to claim 1 is characterized in that: The method for obtaining the user credit rating includes: Obtain historical ticket purchase data of the user account corresponding to the ticket purchase request; The user credit rating is obtained based on the historical ticket purchase data.

3. The intelligent dynamic flow limiting method for sports event ticketing according to claim 1 is characterized in that: The method for obtaining the historical behavior score includes: Obtain historical behavior data of the request interface corresponding to the ticket purchase request; Scoring is performed based on the historical behavior data to obtain the historical behavior score.

4. The intelligent dynamic flow limiting method for sports event ticketing according to claim 1 is characterized in that: The method for obtaining the regional weight of the specific site area includes: Obtaining the revenue contribution ratio of the specific venue area to the target sports venue; According to the revenue contribution ratio, the regional weight of the specific venue area is obtained.

5. The intelligent dynamic flow limiting method for sports event ticketing according to claim 1 is characterized in that: Calculating the user's comprehensive flow limiting threshold based on the user's credit rating, the historical behavior score, and the regional weight of the specific venue area, including: Determining a correction factor according to the user's credit rating, the historical behavior score, and the regional weight of the specific venue area; The comprehensive current limiting threshold of the user is obtained by multiplying the three correction factors in sequence according to the reference threshold.

6. The intelligent dynamic flow limiting method for sports event ticketing according to claim 1 is characterized in that: Determining a processing method for the ticket purchase request according to the comprehensive current limiting threshold and the benchmark threshold includes: If the comprehensive current limiting threshold is greater than the reference threshold, the application priority is determined according to the comprehensive current limiting threshold, and a token application is performed according to the application priority. When the token application is successful, business processing is performed according to the ticket purchase request; wherein the token generation rate is adjusted according to the remaining number of seats in the target stadium; If the comprehensive current limiting threshold is less than or equal to the reference threshold, the token application is rejected.

7. The intelligent dynamic flow limiting method for sports event ticketing according to claim 6 is characterized in that: The method further comprises: When the specific venue area is a VIP area, an independent token pool is used to process the ticket purchase request.

8. The intelligent dynamic flow limiting method for sports event ticketing according to claim 1 is characterized in that: The method further comprises: If the time interval between the request time and the opening time of the ticket purchase request is less than the preset time length, and the user requests to purchase tickets for more than one venue area type, the user's ticket purchase request will be fused.

9. An intelligent dynamic flow limiting system for sports event ticketing, characterized in that: The system comprises: A request acquisition module is used to acquire a ticket purchase request initiated by a user for a specific venue area in a target stadium; A data acquisition module, configured to acquire a user credit rating, a historical behavior score, and a regional weight of the specific venue area corresponding to the ticket purchase request; A data analysis module, configured to calculate a comprehensive flow limiting threshold for the user based on the user's credit rating, the historical behavior score, and the regional weight of the specific venue area; The request processing module is used to determine the processing method of the ticket purchase request based on the comprehensive current limiting threshold and the reference threshold if the instantaneous flow is greater than the reference threshold.

10. A computer-readable storage medium having a plurality of instructions stored thereon, characterized in that: The instructions are suitable for being loaded and executed by a processor to implement the steps of the intelligent dynamic flow limiting method for sports event ticketing as described in any one of claims 1 to 8 above.

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