A red envelope flow control method and system based on a social platform

Abstract

The application discloses a red envelope flow control method and system based on a social platform, and relates to the technical field of computers.The application dynamically divides user levels based on a social graph, so that high-value users in a star fan group can obtain priority access rights, the requests of core fans can still be stably reached in a flow peak, and the risk of loss of high-value users is reduced.Rigid concurrent control is converted into dynamic probability decision-making, the passing rate under a node overload scenario is automatically inhibited through a load-sensitive function, and global lock competition caused by atomic operation is avoided.A random decision mechanism disperses request peak pressure, maintains millisecond-level response capability, and guarantees the stability of the system under the condition of instantaneous concurrent access of ten thousand people.Meanwhile, a delay retry channel associated with a social level is enabled for limited requests, buffer opportunities are provided for core propagators, a timeout compensation strategy is combined, negative emotions of users caused by direct rejection are reduced, the integrity of a red envelope fission path is maintained, and positive circulation of social propagation is strengthened.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and system for controlling the flow of red envelopes based on social platforms. Background Technology

[0002] In scenarios involving tens of thousands of fans on social media platforms, such as when a celebrity makes a surprise appearance and distributes red envelopes, it triggers a massive surge of requests. These scenarios are characterized by: concentrated geographical distribution of users, similar device types, and highly synchronized behavior. When a celebrity distributes a red envelope, the fan group initiates a red envelope grabbing operation in a very short time, forming a traffic pulse that far exceeds that of ordinary groups. Existing systems mostly use atomic counting mechanisms in distributed caching layers to control concurrency, such as limiting the number of requests to split a single red envelope through Redis's CAS operation.

[0003] Current traffic control schemes suffer from severe lock contention during cross-node atomic operations and millions of concurrent requests, leading to a significant increase in latency in rate limiting decisions and causing some requests to fail due to timeouts. On the other hand, indiscriminate traffic limiting strategies fail to recognize differences in user attributes, with core fans and ordinary users being restricted equally, and requests from high-value users potentially being lost due to random discarding. Meanwhile, black market detection mechanisms rely on static rules, which are prone to misjudgments in highly homogeneous fan groups, further reducing the system's effective throughput.

[0004] Some existing solutions introduce real-time load monitoring and dynamically adjust the rate limiting threshold based on the CPU utilization of service nodes; other solutions adopt user segmentation strategies and divide priority channels based on historical behavior data; however, these methods still have response lag and are difficult to take effect quickly within the millisecond time window of traffic pulse formation; at the same time, user segmentation relies on offline calculation of tags and cannot respond in time to sudden changes in group behavior caused by celebrity appearances, resulting in insufficient rate limiting accuracy; therefore, there is an urgent need for a traffic control solution based on social platforms' red envelopes to solve these problems. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] This invention provides a method and system for controlling the traffic of red envelopes based on social platforms. Existing solutions face three major defects in red envelope scenarios involving tens of thousands of fans: atomic lock contention delay, indiscriminate traffic limiting that accidentally harms high-value users, and broken propagation chains. Dynamic thresholds and grouping strategies still suffer from response lag and insufficient scenario adaptability.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide a method for controlling the flow of red envelopes based on a social platform, comprising:

[0009] Step S1: Receive the user's access request for the target red envelope, and extract the user identifier and red envelope group identifier from the request;

[0010] Step S2: Based on the red envelope group identifier, query the social relationship graph to determine the user's social level;

[0011] Step S3: Calculate the dynamic pass probability based on the user's social level and generate a probability decision result;

[0012] Step S4: If the response probability decision result is "pass", a token is allocated to the concurrent processing queue; otherwise, the request is transferred to the delayed buffer channel.

[0013] As a preferred embodiment of the red envelope traffic control method based on a social platform described in this invention, step S2 includes:

[0014] Acquire data on user interaction frequency with red envelope initiators, historical dissemination contribution, and fan level.

[0015] The user's social level is calculated using a pre-set weighted model, and the level value is output to the dynamic probability calculation module.

[0016] As a preferred embodiment of the red envelope traffic control method based on a social platform described in this invention, step S3 includes:

[0017] Real-time collection of service node load metrics;

[0018] Based on user social level and node load metrics, the probability calculation function is invoked:

[0019] Probability value = Base coefficient × Level weight × (1 - Current load rate)

[0020] Generate random numbers in the interval [0,1]. If the random number is less than the probability value, output "pass".

[0021] As a preferred embodiment of the red envelope traffic control method based on a social platform described in this invention, step S3, the dynamic probability calculation step includes:

[0022] Calculate the social level mapping weights:

[0023]

[0024] Among them, w L The social hierarchy is mapped to weights, which are dimensionless. L represents the social hierarchy dimension, λ1 is the baseline weight coefficient, which is dimensionless, and λ2 is the exponential amplification coefficient, which is dimensionless. u Let e ​​be an integer identifier for the user's social level: 1 VIP, 2 Disseminator, 3 Ordinary. It is dimensionless, and e is a natural constant.

[0025] Calculate the basic pass probability:

[0026]

[0027] Where, p raw The pass probability without taking load into account is dimensionless, and raw indicates that the load is not taken into account;

[0028] Calculate the load sensitivity index:

[0029]

[0030] Where δ is the real-time load sensitivity index, which is dimensionless; δ0 is the basic sensitivity index, which is dimensionless; and δ1 is the additional sensitivity increment, which is dimensionless. This is an indicator function; it takes the value 1 if the condition is met, and 0 otherwise. It is dimensionless. ρ t The load rate of the current node is dimensionless, t represents the current time, and θ is the load rate threshold, which is also dimensionless.

[0031] Calculate the load discount function:

[0032]

[0033] in, This is the load discount value;

[0034] Calculate the final dynamic pass probability:

[0035]

[0036] Where, p t This represents the final dynamic pass probability;

[0037] Perform Monte Carlo judgment:

[0038] Randomly generate ξ ~ U(0,1), if ξ <p t If the condition is met, the request will enter the concurrent processing queue; otherwise, it will be transferred to the buffer channel. Here, ξ is a uniform random number, 0-1, dimensionless.

[0039] As a preferred embodiment of the red envelope traffic control method based on a social platform described in this invention, wherein: the delay buffer channel in step S4 executes:

[0040] Set differentiated waiting time limits based on users' social level;

[0041] Probability-based decision-making for periodic retry within a time limit;

[0042] The compensation mechanism is triggered and a failure response is returned after the timeout.

[0043] In setting differentiated waiting time limits, the mapping rules between social level and waiting time limits are defined as follows:

[0044] VIP level corresponds to the first waiting threshold;

[0045] The propagator level corresponds to the second waiting threshold;

[0046] The normal level corresponds to the third waiting threshold;

[0047] If the first waiting threshold is less than the second waiting threshold and the third waiting threshold, a token release monitoring mechanism will be initiated during the waiting period.

[0048] As a preferred embodiment of the red envelope traffic control method based on a social platform described in this invention, the preset weight model executes:

[0049] When the red envelope group is a celebrity fan group, the fan level priority calculation mode is activated;

[0050] Increase the weight of fan level to 1.5-3 times the base coefficient.

[0051] As a preferred embodiment of the red envelope traffic control method based on a social platform described in this invention, in the fan level weight enhancement mechanism of the preset weight model, when the red envelope group belongs to the fan group for the first release of a celebrity's new album, it is denoted as G = fan, where G represents the red envelope group type. When the value is fan, it represents a celebrity fan group; when the value is normal, the normal mode is adopted. The system activates the fan priority mode, and the social level weight is amplified a second time to form a differentiated traffic tilt. The process includes:

[0052] Define fan tier mapping factor:

[0053]

[0054] Where, α f The fan level mapping factor is dimensionless. f is used to distinguish the parameters of the fan priority mode from those of the normal mode. γ0 is the baseline amplification factor, γ1 is the adjustable gain factor, and F... u This serves as a tiered identifier for users within their fan base: 1. Top Supporters, 2. Core Fans, 3. Regular Fans (no dimension, F) max The maximum value for fan level is 3, which is dimensionless;

[0055] Define fan-weighted social weight:

[0056] w L,f =w L ·α f ,

[0057] Among them, w L,f The weighted social level weights are dimensionless, w L The social level mapping weights in step S3;

[0058] The probability of passing the fan-priority basic pass is:

[0059]

[0060] Where, p raw,f The base pass probability in the fan-first mode;

[0061] Combined with load discounting, we get:

[0062]

[0063] Where, p t,f The final dynamic pass probability in the fan-first mode is dimensionless. The load discount value defined in step S3;

[0064] Perform a release decision, generate a random number ξ ~ U(0,1), if ξ <p t,f If the request is denied, the request is allowed; otherwise, the request is sent to the buffer.

[0065] Where ξ is a uniform random number in the interval [0,1].

[0066] Secondly, the present invention provides a red envelope traffic control system based on a social platform, comprising:

[0067] The request receiving module is used to obtain the user identifier and the red packet group identifier from the user request;

[0068] The social graph engine, connected to the request receiving module, outputs the user's social level.

[0069] The dynamic decision-making module generates probabilistic decisions based on the social level and real-time node load.

[0070] The token distributor processes tokens through a decision-making allocation process.

[0071] The buffer controller responds to rejection decisions by implementing tiered delayed scheduling.

[0072] As a preferred embodiment of the social platform-based red envelope traffic control system described in this invention, the social graph engine includes:

[0073] The relationship analysis unit calculates the frequency of interaction between users and the sender of the red envelope;

[0074] The contribution evaluation unit tracks users' historical red envelope dissemination conversion rates.

[0075] Hierarchical identification unit, matching user role tags within the fan group.

[0076] As a preferred embodiment of the social platform-based red envelope traffic control system described in this invention, the buffer controller comprises:

[0077] Time limit configurator, set waiting thresholds based on social level;

[0078] A token listener monitors the token pool release status within a threshold.

[0079] The compensation generator generates an alternative resource package after a timeout.

[0080] The beneficial effects of this invention are as follows: Based on a dynamic social graph, this invention dynamically divides user levels, granting priority access to high-value users (such as top supporters) within a celebrity's fan base, overcoming the experience imbalance caused by traditional traffic limiting methods that fail to differentiate user value. Requests from core fans can still be reliably reached even during peak traffic periods, reducing the risk of high-value user churn. It transforms rigid concurrency control into dynamic probabilistic decision-making, automatically suppressing the pass rate under node overload scenarios through a load-sensitivity function, avoiding global lock contention caused by atomic operations. A random decision mechanism distributes peak request pressure, maintaining millisecond-level response capabilities and ensuring system stability under instantaneous concurrency of tens of thousands of users. Simultaneously, a social level-based delayed retry channel is enabled for restricted requests, providing a buffer opportunity for core disseminators. Combined with a timeout compensation strategy, it reduces negative emotions caused by direct user rejection, maintains the integrity of the red envelope distribution path, and strengthens the positive cycle of social dissemination. Attached Figure Description

[0081] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0082] Figure 1 This is a flowchart illustrating a method for controlling the flow of red envelopes based on a social platform, as shown in Example 1.

[0083] Figure 2 This is a schematic diagram of the framework of a social platform-based red envelope traffic control system in Example 1. Detailed Implementation

[0084] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0085] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0086] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0087] Example 1, referring to Figure 1 and Figure 2 This embodiment provides a method for controlling the flow of red envelopes based on a social platform, including the following steps:

[0088] Step S1: Receive the user's access request for the target red envelope, and extract the user identifier and red envelope group identifier from the request;

[0089] Step S2: Query the social relationship graph based on the red envelope group identifier to determine the user's social level;

[0090] Step S2 includes:

[0091] Acquire data on user interaction frequency with red envelope initiators, historical dissemination contribution, and fan level.

[0092] The user's social level is calculated using a pre-set weighted model, and the level value is output to the dynamic probability calculation module.

[0093] Preset weight model execution:

[0094] When the red envelope group is a celebrity fan group, the fan level priority calculation mode is activated;

[0095] Increase the weight of fan levels to 1.5-3 times the base coefficient;

[0096] In the pre-set weighted model's fan level weight enhancement mechanism, when the red envelope group belongs to a celebrity's new album release fan group, it is denoted as G=fan, where G represents the red envelope group type. A value of "fan" represents a celebrity fan group, while a value of "normal" uses the regular mode. The system then activates a fan priority mode, amplifying the social level weight a second time to create differentiated traffic allocation. The process includes:

[0097] Define fan tier mapping factor:

[0098]

[0099] Where, αf F is a dimensionless mapping factor for fan hierarchy. f is used to distinguish the parameters of the fan-priority mode from those of the normal mode. γ0 is the baseline amplification factor, set to 1.5. γ1 is an adjustable gain factor, also set to 1.5. γ0 and γ1 can be fine-tuned (1.2–1.8) using A / B testing. u This serves as a tiered identifier for users within their fan base: 1. Top Supporters, 2. Core Fans, 3. Regular Fans (no dimension, F) max The maximum value for fan level is 3, which is dimensionless;

[0100] Define fan-weighted social weight:

[0101] w L,f =w L ·α f ,

[0102] Among them, w L,f The weighted social level weights are dimensionless, w L The social level mapping weights in step S3;

[0103] The probability of passing the fan-priority basic pass is:

[0104]

[0105] Where, p raw,f The base pass probability in the fan-first mode;

[0106] Combined with load discounting, we get:

[0107]

[0108] Where, p t,f The final dynamic pass probability in the fan-first mode is dimensionless. The load discount value defined in step S3;

[0109] Perform a release decision, generate a random number ξ ~ U(0,1), if ξ <p t,f If the request is denied, the request is allowed; otherwise, the request is sent to the buffer.

[0110] Where ξ is a uniformly random number in the interval [0,1];

[0111] Specifically, this mechanism prioritizes real-time interaction for high-level fans during celebrity events; the mapping factor decreases linearly by level, with top-tier support receiving a 3x amplification and ordinary fans receiving a 1.5x amplification, preventing excessive weighting that could lead to long-term user hunger; after amplification, the data is normalized using the same logical compression function, maintaining a probabilistically calculable closed loop while simplifying subsequent load fusion; coupled with load discounting, the probability of passage can still be dynamically converged under high node load, preventing overflow caused by a surge in event traffic; the overall strategy balances the traffic peaks brought by celebrities with platform stability, improving the satisfaction of top-tier fans while also considering system throughput.

[0112] Step S3: Calculate the dynamic pass probability based on the user's social level and generate a probability decision result;

[0113] Step S3 includes:

[0114] Real-time collection of service node load metrics;

[0115] Based on user social level and node load metrics, the probability calculation function is invoked:

[0116] Probability value = Base coefficient × Level weight × (1 - Current load rate)

[0117] Generate random numbers in the interval [0,1]. If the random number is less than the probability value, output "pass".

[0118] In step S3, the dynamic probability calculation steps include:

[0119] Calculate the social level mapping weights:

[0120]

[0121] Among them, w L Let L represent the social hierarchy dimension, L be the social hierarchy dimension, L1 be the baseline weight coefficient (0.3-0.5, dimensionless), and L2 be the exponential amplification coefficient (0.6-1.0, dimensionless). u Let e ​​be an integer identifier for the user's social level: 1 VIP, 2 Disseminator, 3 Ordinary. It is dimensionless, and e is a natural constant.

[0122] Calculate the basic pass probability:

[0123]

[0124] Where, p raw The pass probability without taking load into account is dimensionless, and raw indicates that the load is not taken into account;

[0125] Calculate the load sensitivity index:

[0126]

[0127] Where δ is the real-time load sensitivity index, dimensionless; δ0 is the basic sensitivity index, 1.0-1.2, dimensionless; and δ1 is the additional sensitivity increment, 0.4-0.6, dimensionless. This is an indicator function; it takes the value 1 if the condition is met, and 0 otherwise. It is dimensionless. ρ t The current node load rate is 0-1, dimensionless; t represents the current time; θ is the load rate threshold, 0.75-0.85, dimensionless.

[0128] Calculate the load discount function:

[0129]

[0130] in, This is the load discount value;

[0131] Calculate the final dynamic pass probability:

[0132]

[0133] Where, p t This represents the final dynamic pass probability;

[0134] Perform Monte Carlo judgment:

[0135] Randomly generate ξ ~ U(0,1), if ξ <p t If the request is not processed, it will be entered into the concurrent processing queue; otherwise, it will be transferred to the buffer channel. Here, ξ is a uniformly random number, 0-1, dimensionless.

[0136] Specifically, this step uses exponential mapping to amplify differences in social levels, and then uses logical compression to suppress uncontrolled weights, keeping the base probability always controllable; the adaptive sensitivity index increases sharply when the load approaches the threshold, causing the discount function to quickly lower the pass probability to prevent node overload, and the index resets after the load falls back, and the throughput automatically recovers; random determination further disperses the time distribution of requests of the same level, reducing competition jitter; the synergy of level-driven, load-assisted, and randomization allows the platform to both favor star traffic and maintain service stability, significantly reducing peak latency and improving overall user satisfaction;

[0137] Step S4: If the response probability decision result is "pass", a token is allocated to the concurrent processing queue; otherwise, the request is transferred to the delayed buffer channel.

[0138] The delay buffer channel is executed in step S4:

[0139] Set differentiated waiting time limits based on users' social level;

[0140] Probability-based decision-making for periodic retry within a time limit;

[0141] The compensation mechanism is triggered and a failure response is returned after the timeout.

[0142] When setting differentiated waiting time limits, the mapping rules between social level and waiting time limits are defined as follows:

[0143] VIP level corresponds to the first waiting threshold;

[0144] The propagator level corresponds to the second waiting threshold;

[0145] The normal level corresponds to the third waiting threshold;

[0146] If the first waiting threshold is less than the second waiting threshold and the third waiting threshold, a token release monitoring mechanism will be initiated during the waiting period.

[0147] This embodiment also provides a red envelope traffic control system based on a social platform, including:

[0148] The request receiving module is used to obtain the user identifier and the red packet group identifier from the user request;

[0149] Social graph engine, connection request receiving module, outputs user's social level;

[0150] The dynamic decision-making module generates probabilistic decisions based on social level and real-time node load.

[0151] The token distributor processes tokens through a decision-making allocation process.

[0152] The buffer controller responds to rejection decisions by executing tiered delayed scheduling.

[0153] Social graph engines include:

[0154] The relationship analysis unit calculates the frequency of interaction between users and the sender of the red envelope;

[0155] The contribution evaluation unit tracks users' historical red envelope dissemination conversion rates.

[0156] Hierarchical identification unit, matching user role tags within the fan group;

[0157] The buffer controller includes:

[0158] Time limit configurator, set waiting thresholds based on social level;

[0159] A token listener monitors the token pool release status within a threshold.

[0160] Compensation generator, generates alternative resource packs after timeout;

[0161] In this embodiment:

[0162] The base coefficient refers to the initial release probability set by the platform, such as 0.4;

[0163] Compensation mechanisms refer to issuing coupons, queue numbers, or delayed red envelopes, etc.

[0164] Furthermore, it should be noted that the parameter ranges such as λ1 = 0.3-0.5, λ2 = 0.6-1.0, δ0 = 1.0-1.2, and δ1 = 0.4-0.6 mentioned above are merely examples. Those skilled in the art can optimize and select these parameters based on the historical traffic distribution of the target system, A / B experiments, or offline playback results, without affecting the method flow and technical effects of the present invention.

[0165] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for controlling the flow of red envelopes based on social platforms, characterized in that, Includes the following steps: Step S1: Receive the user's access request for the target red envelope, and extract the user identifier and red envelope group identifier from the request; Step S2: Based on the red envelope group identifier, query the social relationship graph to determine the user's social level; Step S3: Calculate the dynamic pass probability based on the user's social level and generate a probability decision result; Step S4: If the response probability decision result is "pass", allocate a token to the concurrent processing queue. Otherwise, the request will be transferred to a delayed buffer channel; In step S3 The steps for calculating the dynamic pass probability include: Calculate the social level mapping weights: ， in, The social hierarchy is mapped to weights, which are dimensionless. Indicates the social hierarchy dimension. The weighting benchmark coefficient is dimensionless. The exponential amplification factor is dimensionless. Integer identifiers for user social levels: 1 VIP, 2 Disseminator, 3 Regular, dimensionless. It is a natural constant; Calculate the basic pass probability: ， in, The pass probability is dimensionless and does not take load into account. This indicates that the load was not included. Calculate the load sensitivity index: ， in, The real-time load sensitivity index is dimensionless. Basic sensitivity index, dimensionless. To add a sensitive increment, dimensionless, This is an indicator function; it takes the value 1 if the condition is met, and 0 otherwise. It is dimensionless. The load factor of the current node is dimensionless, and t represents the current time. The load factor threshold is dimensionless. Calculate the load discount function: ， in, This is the load discount value; Calculate the final dynamic pass probability: ， in, This represents the final dynamic pass probability; Perform Monte Carlo judgment: Randomly generated ,like The request then enters the concurrent processing queue; otherwise, it is transferred to the buffer channel. It is a uniformly random number, ranging from 0 to 1, and dimensionless.

2. The method for controlling the flow of red envelopes based on a social platform as described in claim 1, characterized in that, Step S2 includes: Acquire data on user interaction frequency with red envelope initiators, historical dissemination contribution, and fan level. The user's social level is calculated using a pre-set weighted model, and the level value is output to the dynamic probability calculation module.

3. The method for controlling the flow of red envelopes based on a social platform as described in claim 1, characterized in that, Step S3 includes: Real-time collection of service node load metrics; Based on user social level and node load metrics, the probability calculation function is invoked: Probability value = Base coefficient × Level weight × (1 - Current load rate) Generate random numbers in the interval [0,1]. If the random number is less than the probability value, output "pass".

4. The method for controlling the flow of red envelopes based on a social platform as described in claim 1, characterized in that, The delay buffer channel described in step S4 is executed as follows: Set differentiated waiting time limits based on users' social level; Probability-based decision-making for periodic retry within a time limit; The compensation mechanism is triggered and a failure response is returned after the timeout. In setting differentiated waiting time limits, the mapping rules between social level and waiting time limits are defined as follows: VIP level corresponds to the first waiting threshold; The propagator level corresponds to the second waiting threshold; The normal level corresponds to the third waiting threshold; If the first waiting threshold is less than the second waiting threshold and the third waiting threshold, a token release monitoring mechanism will be initiated during the waiting period.

5. The method for controlling the flow of red envelopes based on a social platform as described in claim 2, characterized in that, The preset weight model is executed as follows: When the red envelope group is a celebrity fan group, the fan level priority calculation mode is activated; Increase the weight of fan level to 1.5-3 times the base coefficient.

6. The method for controlling the flow of red envelopes based on a social platform as described in claim 5, characterized in that, In the pre-set weighted model's fan level weight enhancement mechanism, when the red envelope group belongs to a star's new album release fan group, it is denoted as... , Indicates the type of red envelope group, with possible values. When the time represents a celebrity's fan base, the normal mode is used. The system then activates a fan-priority mode, further amplifying the weight of social level to create differentiated traffic allocation. The process includes: Define fan tier mapping factor: ， in, The fan level mapping factor is dimensionless. This parameter is used to differentiate the fan-priority mode from the normal mode parameters. As the reference magnification factor, The gain coefficient is adjustable. This serves as a tiered identifier for users within their fan base:

1. Top-tier supporters, 2. Core fans, 3. Regular fans. (No specific dimensions are provided.) The maximum value for fan level is 3, which is dimensionless; Define fan-weighted social weight: ， in, The weighted social-level weights are dimensionless. The social level mapping weights in step S3; The probability of passing the fan priority basic pass is: ， in, The base pass probability in the fan-first mode; Combined with load discounting, we get: ， in, The final dynamic pass probability in the fan-first mode is dimensionless. The load discount value defined in step S3; Perform release judgment and generate random numbers. ,like If the request is denied, the request is allowed; otherwise, the request is sent to the buffer. in, It is a uniform random number in the interval [0,1].

7. A social platform-based red envelope traffic control system, based on the social platform-based red envelope traffic control method according to any one of claims 1 to 6, characterized in that, include: The request receiving module is used to obtain the user identifier and the red packet group identifier from the user request; The social graph engine, connected to the request receiving module, outputs the user's social level. The dynamic decision-making module generates probabilistic decisions based on the social level and real-time node load. The token distributor processes tokens through a decision-making allocation process. The buffer controller responds to rejection decisions by implementing tiered delayed scheduling.

8. A red envelope traffic control system based on a social platform as described in claim 7, characterized in that, The social graph engine includes: The relationship analysis unit calculates the frequency of interaction between users and the sender of the red envelope; The contribution evaluation unit tracks users' historical red envelope dissemination conversion rates. Hierarchical identification unit, matching user role tags within the fan group.

9. A red envelope traffic control system based on a social platform as described in claim 7, characterized in that, The buffer controller includes: Time limit configurator, set waiting thresholds based on social level; A token listener monitors the token pool release status within a threshold. The compensation generator generates an alternative resource package after a timeout.

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