A user coupon distribution method for a life platform

Abstract

The present application relates to the technical field of information data processing, and discloses a user coupon distribution method of a life platform, comprising: monitoring the current queue length and the entering rate change rate of the distribution request queue, calculating the flow back pressure index and generating a dynamic execution threshold value based on this; calculating the information entropy of user interaction behavior data, sending a non-preferential visual mark when in a fuzzy interval and monitoring the information entropy change rate to verify validity; calling a two-way deduction model to calculate an incremental probability parameter based on the verification result, and generating a distribution instruction when the product of the incremental probability parameter and an expected income value is greater than the dynamic execution threshold value, wherein the present application introduces flow back pressure feedforward control and a micro-probe active intention verification mechanism, solves the problems of instantaneous overload and oscillation of resource scheduling systems in high concurrency scenarios, and realizes the configuration of computing resources and system steady-state control.

Description

Technical Field

[0001] This invention relates to a method for distributing user coupons on a lifestyle platform, belonging to the field of information data processing technology. Background Technology

[0002] In the current technical architecture of internet platforms for lifestyle services, the resource distribution system is responsible for matching benefit data packages to massive user terminals in real time. Existing mainstream technologies use a conversion rate prediction-based machine learning model as the core scheduling engine. This model collects historical interaction logs and contextual features from user terminals, uses regression algorithms to calculate the probability of the current request triggering a transaction, and prioritizes allocating system resources to objects with a predicted probability higher than a specific threshold. This mechanism maintains the efficiency of basic business operations in scenarios with stable traffic and low real-time requirements. However, simply relying on the construction of basic infrastructure data pathways masks the shortcomings of the distribution control logic in terms of intelligence and refinement. For example, Chinese invention patent CN102611919A discloses a method and system for adding value to lifestyle TV coupons. It establishes a mechanism based on user registration status and explicit request-response. After a user initiates a request and confirms the download, the system directly mobilizes communication resources to send coupons in the form of SMS or MMS. Although this passive response mode completes the delivery of basic business, it is essentially based on a single-dimensional linear triggering logic and lacks proactive verification of user interaction intent. Faced with high concurrency traffic, the system cannot distinguish whether user clicks are based on genuine conversion intentions or stem from unintentional accidental touches or non-targeted browsing, resulting in a large amount of communication and computing resources being occupied by ineffective interactions.

[0003] Mobile internet traffic is evolving towards a pulsed, high-concurrency pattern. The existing single-prediction-model paradigm exposes inherent fundamental flaws. The objective function of current prediction models is set to minimize the error between predicted values ​​and actual behavior, causing the model training iteration process to converge towards high-frequency active samples. The system cannot distinguish at the data level whether the conversion behavior of the target object stems from the user's inherent high activity attributes or external incentives such as rights and resources. Overfitting to existing active states causes the distribution system to continuously migrate towards a state without external intervention. This results in redundant data processing tasks consuming limited system bandwidth and creating system-level ineffective load. To address the sample bias problem, the industry has introduced causal inference algorithms or gain models to quantify the net incremental intervention, but this is not feasible during the midday peak. In extremely high-concurrency scenarios, severe constraints on computing power and latency are encountered. Traditional causal inference logic requires the construction of counterfactual reasoning networks or large-scale online control experiments, with computational complexity far exceeding that of simple regression prediction. Edge computing nodes within the millisecond-level real-time decision window cannot support high-density floating-point operations. Forced deployment leads to a surge in system response latency and may even trigger service circuit breakers. Existing budget control mechanisms rely on monitoring the actual consumption rate of the backend and negative feedback adjustment. At the control theory level, a single feedback loop has inherent phase lag. When faced with sudden pulse traffic, there is an unavoidable time difference between the gateway receiving requests, model decision, and backend statistical consumption. This results in excessive requests being executed before the adjustment command takes effect, causing instantaneous resource overdraft or system overshoot oscillation.

[0004] Therefore, how to construct a mechanism for eliminating redundant data streams under extremely low computational latency constraints and using physical layer feedforward signals to overcome the lag in feedback control resource distribution processing has become the technical problem to be solved by this invention. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A method for distributing user coupons on a lifestyle platform, the method comprising the following steps:

[0006] Monitor the current queue length and queuing rate change rate of the distribution request queue. Calculate the flow back pressure index, which characterizes the current load pressure of the distribution control system, based on the current queue length and queuing rate change rate. Map the flow back pressure index to a positive threshold bias. Generate a dynamic execution threshold by superimposing the positive threshold bias onto the initial decision threshold generated based on the budget consumption rate.

[0007] In response to a current session request initiated by the user terminal, extract the user's interaction behavior data within the current session window and calculate the information entropy of the interaction behavior data;

[0008] When the information entropy is within the preset fuzzy range, the distribution instruction generation process is suspended and a visual mark rendering instruction without discount attributes is sent to the user terminal. The rate of change of information entropy within the preset time window after the visual mark rendering is monitored. When the rate of change indicates that the information entropy is decreasing and the decrease exceeds the validity judgment value, a validity verification pass signal is generated.

[0009] In response to the validity verification pass signal, the dual-path inference model is invoked to calculate the natural conversion probability of the user in the no-intervention state and the conditional conversion probability in the intervention state with preset rights parameters, and the difference between the conditional conversion probability and the natural conversion probability is calculated to obtain the incremental probability parameter.

[0010] When the product of the incremental probability parameter and the preset expected return value is greater than the dynamic execution threshold, a coupon distribution instruction for the user is generated and output.

[0011] Preferably, in the step of generating a dynamic execution threshold, the calculation logic of the traffic backpressure index includes: obtaining the normalized queue length and normalized enqueue acceleration of the distribution request queue in the current statistical period; performing a weighted summation of the normalized queue length and normalized enqueue acceleration to obtain the traffic backpressure index, wherein the weight coefficient of the normalized enqueue acceleration is positively correlated with the historical average response latency of the system; and using a preset nonlinear mapping function to convert the traffic backpressure index into a positive threshold bias, wherein the nonlinear mapping function is set to output an exponentially increasing bias when the traffic backpressure index exceeds the warning value, so as to improve the distribution admission standard during the request queue backlog stage.

[0012] Preferably, the step of monitoring the rate of change of information entropy includes: taking the moment when the visual marker is rendered as the starting point, continuously collecting the user's touch position coordinates and page scrolling speed on the user's terminal screen; for each sampling point, calculating the spatial distribution dispersion of the touch position coordinates and the temporal series volatility of the page scrolling speed, and defining the weighted combination value of the spatial distribution dispersion and the temporal series volatility as the instantaneous entropy value; calculating the rate of change of the instantaneous entropy value with respect to time; wherein, a negative rate of change indicates that the user's attention is focused on the visual marker, and the absolute value of the rate of change indicates the intensity of the user's response to the visual marker.

[0013] Preferably, the steps for calculating the incremental probability parameters follow the following dual-path differential logic: Interaction behavior data is input into the benchmark evaluation channel, which masks preset benefit parameters during the calculation process and outputs the natural conversion probability representing the user's inherent conversion intention; simultaneously, interaction behavior data and preset benefit parameters are input into the intervention evaluation channel, which uses the preset benefit parameters as feature input and outputs the conditional conversion probability; a subtraction operation is performed, subtracting the natural conversion probability from the conditional conversion probability to eliminate the existing probability component that the user will convert regardless of whether they receive intervention, thus obtaining the incremental probability component triggered only by the preset benefit parameters.

[0014] Preferably, in the calculation step of the flow back pressure index, the flow back pressure index is calculated using the following formula: ,in, The flow back pressure index, The current normalized queue length for distributing requests. The rate of change of the queuing rate is the normalized queuing acceleration. α and β are preset length weighting coefficients and acceleration weighting coefficients, respectively, and the value of β is greater than the value of α to enhance the feedforward response to the flow growth trend.

[0015] Preferably, the generation of the dynamic execution threshold is also controlled by a feedback adjustment mechanism of the budget consumption rate: calculate the actual budget consumption rate in the current statistical period; calculate the rate deviation value between the actual budget consumption rate and the preset benchmark consumption rate; process the rate deviation value using a proportional-integral-derivative control algorithm to generate a benchmark adjustment amount for the initial decision threshold; when the rate deviation value indicates that the consumption rate is higher than the preset benchmark consumption rate, the initial decision threshold is increased in a non-linear increment; when the rate deviation value indicates that the consumption rate is lower than the preset benchmark consumption rate, the initial decision threshold is decreased in a linear decrement.

[0016] Preferably, the visual markup rendering instruction without discount attributes includes local update parameters for the current rendering tree of the user terminal: parsing the document object model structure of the currently displayed page of the user terminal, identifying non-interactive blank areas located within the visible area; generating visual objects with high-contrast border attributes or dynamic light effect attributes as visual marks, the visual objects are not associated with business jump links or price reduction data; setting the insertion position coordinates of the visual objects to the center point of the non-interactive blank areas, so that the display of visual marks does not change the original business layout of the page.

[0017] Preferably, the method further includes a model calibration step based on random control samples: during the execution phase of generating coupon distribution instructions, according to a preset random sampling ratio, the coupon distribution instructions of some users who meet the distribution conditions are intercepted, and the corresponding user data is marked as control sample; the actual conversion behavior of the control sample in the state of not obtaining coupons is tracked and recorded as real natural conversion data; the benchmark evaluation channel in the dual-path inference model is calibrated using real natural conversion data, and the model parameters of the benchmark evaluation channel are updated by minimizing the error between the natural conversion probability and the real natural conversion data.

[0018] Preferably, the steps for calculating the information entropy of interactive behavior data include: obtaining the user's click interval time series, swipe trajectory curvature series, and back operation frequency on the current page; mapping the click interval time series to a temporal stationarity feature and mapping the swipe trajectory curvature series to a spatial deterministic feature; calculating the joint information entropy of the temporal stationarity feature and the spatial deterministic feature; and determining that the user is in a highly uncertain disordered operation state, i.e., a fuzzy interval, when the joint information entropy is greater than a preset high entropy threshold.

[0019] Preferably, the method is applied to a distributed architecture that includes edge computing nodes and a cloud core cluster: the steps of calculating information entropy and monitoring the rate of change are executed on the gateway node deployed at the network edge, using edge computing power to achieve real-time response to user interaction behavior and filtering of invalid requests; the steps of calling the dual-path inference model and generating distribution instructions are executed in the cloud core cluster, only receiving the validity verification pass signal and corresponding user characteristic data verified by the gateway node; through the gateway node's proactive intent verification and traffic backpressure control, invalid interaction traffic and overload traffic are intercepted before the cloud core cluster receives the business request.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. In the data processing unit, the natural probability deduction for the baseline state and the conditional probability deduction for the intervention state are executed in parallel. The difference between the two calculation results is used as the core basis for triggering the distribution instruction. At the logical level, the inherent conversion attributes of users and the external intervention response attributes are decoupled. Through the differential gating mechanism, the existing data streams that will undergo state transition even without external intervention are automatically identified and separated from the massive business requests. The system communication and computing resources are blocked from being allocated to nodes with low marginal benefits. The focus of resource scheduling is shifted from absolute state value prediction to state increment quantification. This ensures that limited distribution resources are only allocated to critical paths where the system state can be changed. The resource management system is optimized under budget constraints by utilizing existing hardware.

[0022] 2. The entropy change rate of real-time interactive behavior within the current session window is used instead of deep historical profile features as the core decision parameter. This transforms the causal inference task for complex user states into a linear calculation of the discreteness and volatility of real-time action sequences. Based on lightweight representation methods of instantaneous physical features, the decision-making process eliminates the dependence on high-frequency retrieval and backtracking of the full historical database, reduces the data processing load and I / O latency of edge computing nodes, and enables complex dual-path inference logic to adapt to millisecond-level time windows. This ensures that the resource distribution system maintains low-latency real-time response in high-concurrency scenarios with tens of millions of QPS, and resolves the contradiction between refined strategy models and engineering real-time requirements.

[0023] 3. By combining the physical congestion characteristics of the distribution request queue, a feedforward control path is constructed. The backlog length and queuing acceleration of the pending request queue at the gateway layer are monitored in real time. The changing trend is directly mapped to the positive bias of the decision threshold. The feedforward signal reflecting the instantaneous pressure of the network layer is superimposed on the negative feedback adjustment loop based on the resource consumption rate. This compensates for the phase lag of the single feedback control when facing sudden traffic pulses. Before the back-end feedback takes effect, the distribution admission criteria are tightened in advance using the physical congestion signal. This eliminates the oscillation and overshoot phenomenon of the control system under step interference, prevents instantaneous resource overdraft or system overload due to decision delay, and ensures the stability of the resource scheduling system under dynamic load environment. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the coupon distribution system architecture that integrates feedforward control and proactive intent verification according to the present invention.

[0025] Figure 2 This is a comparison diagram of the numerical distribution of incremental probability and transformation probability in the dual-path differential extrapolation model of this invention;

[0026] Figure 3 This is a timing interaction logic diagram of the flow backpressure feedforward mechanism and dynamic execution threshold generation in this invention. Detailed Implementation

[0027] This specific embodiment aims to provide a detailed explanation of the technical solution of a user coupon distribution method for a lifestyle platform according to the present invention, so that those skilled in the art can fully implement the present invention accordingly. However, the following description is only used to explain the technical concept and implementation details of the present invention and is not intended to limit the scope of protection of the present invention.

[0028] The present invention discloses a user coupon distribution method for a lifestyle platform, comprising a distributed resource scheduling system executed by edge gateway nodes and a cloud core computing cluster. Through parallel processing of traffic congestion control logic, proactive intent verification logic, and dual-path differential inference logic, it achieves millisecond-level allocation of marketing resources. At the top-level architecture of the system operation, the edge gateway nodes are responsible for real-time monitoring of the physical request queue status of the network layer and the interactive behavior characteristics of user terminals, undertaking the preliminary tasks of traffic shaping and invalid request filtering. The cloud core computing cluster, based on the filtered valid feature data, performs high-dimensional causal inference calculations and outputs the final distribution instructions. The data flow between the two follows a unidirectional convergence path from physical layer feature extraction to business layer value decision-making, ensuring that computing resources are allocated only to key business nodes with marginal gain potential.

[0029] To address the objective technical challenge of sudden surges in traffic during high-concurrency scenarios easily leading to resource scheduling system overload and instantaneous budget breakdown, this invention executes a feedforward control procedure based on traffic backpressure before the distribution request enters the core decision-making chain. Edge gateway nodes utilize built-in counters and timers to monitor the current backlog length of the distribution request queue in real time. and the rate of change of the inbound rate per unit time The system calculates the flow back pressure index, which characterizes the current system load pressure, based on these two physical quantities. The calculation follows linear weighted logic, and its formula is as follows: ,in, This is the normalized queue length. The normalized queuing acceleration is defined by α, a preset length weighting coefficient, and β, a preset acceleration weighting coefficient. β is set to a value greater than α to enhance the feedforward response to traffic growth trends. The system utilizes a preset nonlinear mapping function to convert the traffic backpressure exponent into a positive threshold bias. This bias is then directly added to the initial decision threshold generated by the negative feedback loop of the backend budget consumption rate. Above this, the final dynamic execution threshold is synthesized. This feedforward control mechanism utilizes the backlog signals of the request queue at the physical layer to tighten the distribution admission criteria at the business layer in advance, thereby building a defense barrier based on physical pressure before the traffic pulse reaches the core computing unit.

[0030] After addressing the system-level traffic throughput stability issue, this invention introduces a micro-probe-based proactive intent verification mechanism to address the high-entropy noise data commonly found in user interaction behavior data due to unintentional browsing or accidental operations. When a user terminal initiates a current session request, the system extracts the user's real-time interaction behavior sequence within the visible area of ​​the current page, including the time sequence of touch position coordinates (x, y) and page scrolling speed. The computing unit performs discrete and volatility analysis on the time series data to generate real-time information entropy characterizing the uncertainty of the user's current operation. When the real-time information entropy is within the preset fuzzy range At this time, the system temporarily suspends the subsequent rights calculation process and sends a visual mark rendering instruction without discount attributes to the user terminal. It then drives the terminal to generate a visual widget with high contrast attributes in the non-interactive blank area of ​​the current rendering tree. The system startup duration is [duration missing]. The monitoring time window continuously collects the instantaneous change rate of user interaction behavior entropy after the visual widget is rendered. When the rate of change indicates that the entropy value is monotonically decreasing and the absolute value of the decrease exceeds the validity threshold, When the system determines that the current user is in a state of valid hesitation that is sensitive to information, it generates a validity verification pass signal. If the entropy value remains high or does not converge significantly, the system marks the session as invalid noise and directly blocks the subsequent distribution process.

[0031] Based on the aforementioned validity verification signal, the system then invokes the dual-path inference model to execute the core incremental value quantification procedure. This addresses the fundamental deficiency of traditional single prediction models in failing to distinguish between existing and incremental transformations. The computational engine then uses real-time interactive behavior feature vectors... Simultaneously, the input is fed into two logically parallel and isolated deduction channels: the first channel is the benchmark evaluation channel, which masks any rights-related feature parameters during the calculation process and outputs the natural conversion probability that represents the user's inherent conversion intention under natural conditions. The second channel is the intervention assessment channel, which uses pre-set benefit parameters as explicit features and concatenates them into the input vector to output the probability of conditional conversion for users under specific benefit incentives. The equity parameters include face value and threshold data, and the logic unit performs differential operations. The system extracts the incremental probability component triggered solely by the equity parameter, and then compares this incremental probability parameter with a preset expected return value. Multiply by this to obtain the expected marginal revenue value, and then combine it with the aforementioned dynamic execution threshold that includes the flow back pressure bias. The system compares the marginal benefit value with the dynamic execution threshold. When the marginal benefit value is greater than the threshold, the system generates a coupon distribution instruction for the user and pushes the corresponding data packet to the user's terminal.

[0032] To ensure the accuracy and timeliness of the parameters in the aforementioned dual-path simulation model during long-term operation, the system incorporates a closed-loop calibration procedure based on random control samples. During the execution phase of generating distribution instructions, the system intercepts some distribution instructions from users who should have met the distribution conditions, according to a preset random sampling ratio γ. These instructions are used as control samples and kept in a non-interventional state. Simultaneously, the actual conversion behavior data of these samples in subsequent time windows is recorded and labeled as genuine natural conversion tags. Periodically use the real natural conversion label and the natural conversion probability output by the model. The error loss function between the two is used to backpropagate and update the model parameters of the benchmark evaluation channel, thereby eliminating the model's tendency to overfit to high-frequency active users and ensuring that the benchmark plane of the differential calculation is always anchored to the real uninterrupted state; in the calculation of the above-mentioned flow back pressure index, the normalized queue length is involved. With normalized queuing acceleration The specific determination procedure is as follows: the system presets a baseline queue length corresponding to the gateway's maximum concurrent processing capacity. and reference acceleration In each sampling period When finished, the system reads the physical queue length at the current instant. The difference between the total number of queuing requests within that period is calculated as the original acceleration. Perform normalization operation as well as If the calculated result is greater than 1, it is truncated to 1. The system uses historical average response delay as the basis for setting the weighting coefficients. The dynamic adjustment strategy, when When the preset warning line is exceeded, the value of the acceleration weighting coefficient β is automatically increased.

[0033] Regarding the aforementioned dynamic execution threshold The basic part, namely the initial decision threshold. The system generates data using a proportional-integral-derivative (PID) adjustment procedure based on the budget consumption rate, and the system accumulates data in real time for the current statistical period. The total cost of equity already distributed is calculated, and then divided by the cycle time to obtain the actual consumption rate. Calculate the actual consumption rate and the preset baseline consumption rate. Deviation between The deviation value is processed using a discrete proportional-integral-derivative algorithm, and a reference control quantity is output to adjust the threshold. When the deviation E is positive, meaning the consumption is too fast, the system uses an exponential function. The threshold is non-linearly increased, where k is the adjustment gain coefficient, to quickly curb the trend of budget overdraft. When the deviation E is negative, indicating that consumption is too slow, the system adopts a linear function. The threshold is gradually lowered, where The attenuation coefficient is used to avoid traffic surges caused by a sudden drop in the threshold; and for the generation and rendering of visual markers without discount attributes, the system follows a minimal intervention procedure that does not change the original business layout. When the terminal receives a rendering instruction, it parses the document object model structure of the currently displayed page, identifies the blank rectangular area within the visible area that does not contain clickable elements, and calculates the geometric center coordinates of the area. The system generates a border object with a preset pixel width and high-contrast color attributes, or a layer object with dynamic lighting effects attributes, sets its positioning attribute to absolute positioning, and uses... As an anchor point inserted at the top level of the rendering tree, this visual object is not associated with any jump links, price reduction logic, or business callback functions in terms of data attributes; it exists solely as a pure visual stimulus.

[0034] Example 1: In a local life service platform's midday peak flash sale scenario, the resource scheduling system faces a sudden surge in concurrent requests. Under such conditions, limited by the network latency of user terminals and the processing time of the backend transaction settlement system, the actual budget consumption rate is determined. The negative feedback regulation loop inherently suffers from phase lag. If relying solely on this single feedback mechanism, when the system detects excessive consumption rate and attempts to raise the admission threshold, the backlog of request sequences in the pipeline has already been distributed according to the old low threshold, causing the marketing budget to be instantly exhausted or the system to fall into overshoot oscillation. To address the aforementioned lag and overload issues, gateway nodes deployed at the network edge do not wait for feedback from backend business data but directly sample the physical state of the distribution request queue. When a sudden traffic surge arrives, although the budget consumption rate has not yet changed, the current backlog length of the queue... and rate of change of queuing rate The system rapidly increases, and based on the normalized values ​​of these two physical quantities, it uses the formula... Real-time calculation of flow back pressure index Because the preset acceleration weighting coefficient β is greater than the length weighting coefficient α, the system exhibits high sensitivity to the growth trend of the flow rate, and this back pressure index... It is then nonlinearly mapped to a positive threshold bias. And added to the current initial decision threshold. Above this, this process leads to the final dynamic execution threshold. Boost traffic before it hits the core business logic.

[0035] After stabilizing the traffic throughput baseline through feedforward control, the system utilizes an active stimulus-response mechanism to address the high-entropy noise problem mixed in with high-concurrency traffic, specifically regarding behavioral entropy. For users in the fuzzy region, the system did not directly execute the computationally intensive dual-path inference. Instead, it issued instructions to the user terminal to render a dynamic light effect border with no substantial benefit in the non-interactive area of ​​the screen. After the system detected the appearance of this widget... rate of change of entropy of user touch and scrolling behavior within a time window When monitoring data shows that the entropy value is monotonically decreasing and the absolute value of the decrease exceeds the validity threshold, At this point, the system determines that the user has shifted from disordered operation to focused state due to visual stimulation, confirming the effectiveness of the interaction. This mechanism transforms the passive inference of user intent into an active incentive response test, filtering out noisy traffic at the data source that is active but lacks actual conversion intention. Through the dual filtering of physical layer backpressure control and perception layer intent verification, requests with high determinism are transmitted to the cloud core cluster. The cloud computing engine calls the baseline evaluation channel and intervention evaluation channel in parallel to calculate the conversion probability of the user in a natural state. The probability of conditional transformation under equity intervention And derive the incremental probability. Due to dynamic execution threshold The pressure has already been increased due to the back pressure mechanism, at this time when With expected return The dispatch instruction will only be generated when the product of the two is greater than the dynamic execution threshold. This multi-level cascading control strategy allocates computing resources and marketing budgets to the most leveraged key nodes in the system.

[0036] Example 2: This example constructs a simulation test platform based on a microservice architecture and simulates the backend environment of a lifestyle service application with tens of millions of daily active users. The gateway node is deployed on an edge computing server, and the core computing cluster is deployed in a cloud containerized environment. The test data source uses real business logs from a large lifestyle platform during the Double Eleven promotion, which have been anonymized. These logs include user IDs, timestamps, touch coordinate sequences, page scrolling speeds, and historical conversion records. To simulate extreme conditions, a Poisson-distributed pulsed concurrent request is injected into the gateway node through a load generator, and Gaussian white noise with a signal-to-noise ratio of 15dB is superimposed on the background traffic to simulate network jitter and atypical user behavior. The core objective of this experiment is to quantitatively evaluate the improvement effect of introducing traffic backpressure feedforward control and micro-probe active verification mechanism on resource utilization, distribution accuracy, and system stability. To this end, three control samples are designed: Control group A adopts a traditional single negative feedback control strategy based on backend consumption rate and does not introduce an active verification mechanism; Control group B introduces a micro-probe active verification mechanism on the basis of control group A, but still relies solely on negative feedback control.

[0037] The experiment was divided into three phases: benchmark testing, stress testing, and overload testing. In the benchmark testing phase, the input traffic was maintained at 50% of the system's design capacity. In the stress testing phase, the traffic linearly increased to 120% of the design capacity. In the overload testing phase, a sudden surge in traffic (300% of the design capacity) was injected instantaneously to simulate a flash sale scenario. During each phase, the system recorded key performance indicators in real time, including the variance of budget consumption rate fluctuations, invalid request interception rate, and average response latency of core business interfaces. Regarding the key parameter settings in the calculation of the traffic backpressure index, this experiment followed the following decision logic: the values ​​of the length weighting coefficient α and the acceleration weighting coefficient β depend on the system's trade-off between steady-state error and response speed. In this experiment, considering the extremely low tolerance of flash sale scenarios to instantaneous overload, we set β to 0.7 and α to 0.3 to enhance the system's sensitivity to traffic change rates, while also setting the validity threshold. The setting is based on statistical analysis of historical noise data, and the value is taken as 1.5 times the standard deviation of the mean entropy change rate.

[0038] The test results show that during the overload test phase, the budget consumption curve of control group A exhibited violent oscillations, and budget overrun occurred within 500ms after the arrival of the traffic pulse; although control group B showed some improvement in blocking invalid requests, it failed to effectively smooth out the fluctuations in budget consumption; while the sample group of this invention maintained a stable budget consumption rate when facing the same pulse traffic, without any overrun, and the system response delay was lower than that of the control group. Specific data are shown in Table 1.

[0039] Table 1: Comparison of Key Performance Indicators of Different Test Groups During the Overload Phase

[0040]

[0041] Referring to Table 1, the sample group of this invention outperforms the control group in terms of budget consumption fluctuation variance, indicating that the flow backpressure feedforward mechanism effectively eliminates the phase lag of the control system and achieves smooth control of budget consumption. Simultaneously, the increase in invalid request interception rate and the reduction in average response latency verify the synergistic effect of the micro-probe active verification mechanism in purifying flow and reducing system load. Further analysis data shows that when the flow backpressure index... When the threshold value exceeds 0.8, the dynamic execution threshold of the sample group of this invention is... It increased exponentially, successfully blocking over 90% of low-value requests.

[0042] Example 3: This example combines Figures 1 to 3 This describes a user coupon distribution method for a lifestyle platform, such as... Figure 1 As shown, the process begins with the user terminal initiating a current session request. The system then processes two types of input signals in parallel: In the left-hand traffic backpressure feedforward control logic branch, the system monitors the status of the distribution request queue in real time, calculates the traffic backpressure index, and generates a dynamic execution threshold based on the budget feedback benchmark to achieve dynamic adjustment of the access standard. In the right-hand interactive behavior data processing branch, the system collects touch coordinates and page scrolling speed to construct a real-time behavior sequence. It also identifies fuzzy intervals through the information entropy calculation and status determination module. When the user is in a fuzzy interval, the micro-probe active intent verification mechanism is triggered to send a no-discount visual mark to the terminal and monitor the entropy change rate. Only after the verification is passed is a valid signal generated. The two signals converge into a dual-path differential inference model. This model calculates the natural conversion probability and conditional conversion probability in parallel to decouple the incremental probability parameter. This parameter is compared with the dynamic execution threshold in the incremental value quantification and decision module. When the distribution conditions are met, a coupon distribution instruction is output. In addition, a model calibration feedback loop based on random control samples is built at the bottom of the process. The actual natural conversion behavior is tracked by sampling control group data, and the benchmark channel parameters are updated periodically to eliminate model bias.

[0043] like Figure 2As shown in the figure, the horizontal axis represents discrete user samples from user A to user H, and the vertical axis represents probability values, ranging from 0 to 0.45. The figure contains three curves: the dashed line represents the natural conversion probability of a user in a no-intervention state, the solid line represents the conditional conversion probability of a user in a privileged state, and the dotted line represents the incremental probability parameter obtained by the difference between the two. It can be observed that there are individual differences and fluctuations in the natural conversion probability and conditional conversion probability of different users. For example, user D's conditional conversion probability reaches a peak of 0.45, while user C is at a trough. However, the difference between the two, i.e., the incremental probability parameter, shows high stability across all samples, consistently remaining within the range of 0.13 to 0.15. This distribution characteristic intuitively reveals the core logic of the model: by using differential calculation to remove the inherent activity differences of users, i.e., the fluctuations in the natural conversion probability, the pure incremental value brought about by privileged intervention can be extracted. Figure 3 As shown, the top defines five core interactive objects: a distribution request queue, an edge gateway node, a backpressure calculation module, a PID controller, and a threshold generator. The process begins with the edge gateway node transmitting the current queue length and the number of enqueued requests (i.e., the original queue data) to the backpressure calculation module. The backpressure calculation module then performs normalization operations to obtain the normalized queue length and normalized enqueuing acceleration, and outputs the traffic backpressure index to the threshold generator after weighted summation. Simultaneously, the PID controller obtains the actual budgeted consumption rate and calculates its deviation from the baseline consumption rate, generating an initial decision threshold which is then fed into the threshold generator. The threshold generator uses a nonlinear mapping function to convert the traffic backpressure index into a positive threshold bias, which is then superimposed on the initial decision threshold to finally synthesize a dynamic execution threshold and fed back to the edge gateway node. This timing logic ensures that the feedforward control signal can tighten the distribution admission criteria at the physical access layer before the traffic peak reaches the core business layer.

[0044] Example 4: This example describes the natural transformation probability in the dual-path differential derivation model. The calibration and update mechanisms will be supplemented and deepened in a targeted manner to address the black box problem of benchmark drift that may occur due to the accumulation of sample bias during long-term model operation, in order to ensure... The system can consistently and accurately reflect users' true conversion intentions in a non-interventional state during each round of distribution decision-making. Initially, the distribution process is not performed directly on all users. Instead, a Bernoulli trial sequence is generated based on a pre-set random seed. For each user request that enters the decision-making process, the system assigns it to the control group with probability γ according to this sequence. For users in the control group, regardless of their subsequent dual-path inference results, the system blocks any rights intervention, i.e., does not issue coupons to them, and fully records their attribution window period. The actual transformation behavior within.

[0045] Based on the collected control group samples, the system periodically triggers a model calibration task, which extracts the feature vectors of control group users at the time of request from historical logs. And the final conversion tag The value is 1 for conversion and 0 for no conversion. The system calls the current benchmark evaluation channel model and inputs... And calculate the prediction probability Based on this, a loss function is constructed with the objective of minimizing prediction bias. This loss function not only includes the conventional cross-entropy loss term, but also introduces a bias constraint term to penalize systematic overestimation or underestimation. The specific form of the loss function is as follows: Where N is the number of samples in the control group. Let i be the true conversion label of the i-th sample. Let λ be the natural conversion probability predicted by the model for the i-th sample, and λ be the weighting coefficient of the bias constraint term. This loss function is minimized using the stochastic gradient descent algorithm. The system updates the model parameters of the benchmark evaluation channel through backpropagation. To avoid excessive perturbation to the model caused by data fluctuations within a single time slice, the parameter update process employs an exponential moving average (EMA) strategy, i.e., the model parameters after the new round of calibration... Instead of directly replacing the old parameters, it follows the formula. Smooth fusion is performed, where μ is the momentum factor, and its value is set between 0.9 and 0.99.

[0046] Example 5: Regarding the natural conversion probability This embodiment further explains the specific engineering implementation process for solving the model cold start problem, including offline calibration and data filling procedures, to ensure... The computing module has a reliable initial parameter benchmark during the initial deployment of the system or major version iteration. This invention constructs a standardized offline calibration and data backfilling procedure, extracts full user behavior data from the past M periods based on historical business logs, and constructs a multi-dimensional feature vector. Actual conversion tags For each user record in the static sample database, the system uses a clustering algorithm to divide it into K baseline user groups with common behavioral characteristics. For each group, the system calculates its average natural conversion rate within a no-intervention time window and uses this conversion rate as the initial baseline probability for that group. The system sets these initial baseline probabilities and their corresponding group feature center vectors. An association mapping is performed to generate a static baseline probability lookup table. During the model cold start phase, when the real-time inference module receives the feature vector of a new user... At that time, the system calculates the feature centers of each group. The Euclidean distance is used to identify the nearest neighbor group, and the corresponding group is retrieved directly from the lookup table. This mechanism ensures that the model can still output a statistically based baseline prediction value even in the absence of sufficient real-time feedback data, serving as the user's initial natural conversion probability prediction.

[0047] In response to potential performance differences under different network environments or hardware configurations, this invention establishes a pre-deployment calibration procedure. Before the system is officially connected to production traffic, operations and maintenance personnel must perform a series of standardized stress tests in the target deployment environment. By simulating the generation of synthetic traffic requests conforming to a Poisson distribution, the concurrency rate is increased until the preset design capacity limit is reached. During the testing process, the system monitors and records key performance indicators in real time, including but not limited to average response latency. CPU utilization and memory usage Based on test data, the system uses a linear regression model to fit the functional relationship between various performance indicators and concurrency rate, and automatically adjusts the normalized baseline parameters in the flow backpressure control algorithm. and ,when This is set as the queue length when the system's average response latency reaches the Service Level Agreement (SLA) threshold. It is then set as the rate of change in the queuing rate when the CPU utilization reaches the safety threshold.

[0048] Example 6: This example supplements the explanation of a standardized parameter calibration and system debugging procedure for the flow backpressure feedforward control logic and dynamic execution threshold adjustment algorithm. This addresses potential system response delays or over-control issues caused by improper initial parameter settings in different business scenarios and physical deployment environments. Before the system goes live, a normalized baseline parameter calibration process is executed. This requires using stress testing tools in an isolated, pre-production environment to simulate and generate synthetic flow requests conforming to a Poisson distribution. The test starts from zero load and proceeds in fixed steps. Gradually increase the concurrent request rate until the system reaches its physical crash point. During this process, collect and record the request queue length L and average response latency in real time. The corresponding relationship data, when the average response delay is detected When the queue length exceeds the preset service level agreement limit for the first time, the system automatically locks the current queue length and defines it as the normalized baseline queue length. Similarly, the system monitors CPU utilization. The curve of the rate of change of the queuing rate A, when When the preset safety threshold is reached, the rate of change of the queuing rate at this point is locked and defined as the normalized reference acceleration. .

[0049] The system performs an adaptive tuning process for PID control parameters to determine the optimal gain coefficient in the dynamic threshold adjustment algorithm. It operates under constant high load pressure, with an artificial 20% step flow disturbance injected. Under this condition, the system utilizes the Ziegler-Nichols rule to adjust the proportional gain k and integral time. With differential time The tuning objective is to perform iterative optimization to optimize the budget consumption rate. With the shortest recovery time after encountering disturbances and an overshoot of no more than 5%, after multiple rounds of iterative testing, the system finally locked in a set of PID parameter combinations that comprehensively optimizes control performance indicators, and solidified it as the default operating configuration in this deployment environment.

[0050] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0051] Finally, 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.

Claims

1. A user coupon distribution method of a life platform, characterized by, The method includes the following steps: Monitor the current queue length and queuing rate change rate of the distribution request queue. Calculate the flow back pressure index, which characterizes the current load pressure of the distribution control system, based on the current queue length and queuing rate change rate. Map the flow back pressure index to a positive threshold bias. Generate a dynamic execution threshold by superimposing the positive threshold bias onto the initial decision threshold generated based on the budget consumption rate. In response to a current session request initiated by the user terminal, extract the user's interaction behavior data within the current session window and calculate the information entropy of the interaction behavior data; When the information entropy is within the preset fuzzy range, the distribution instruction generation process is suspended and a visual mark rendering instruction without discount attributes is sent to the user terminal. The rate of change of information entropy within the preset time window after the visual mark rendering is monitored. When the rate of change indicates that the information entropy is decreasing and the decrease exceeds the validity judgment value, a validity verification pass signal is generated. In response to the validity verification pass signal, the dual-path inference model is invoked to calculate the natural conversion probability of the user in the no-intervention state and the conditional conversion probability in the intervention state with preset rights parameters, and the difference between the conditional conversion probability and the natural conversion probability is calculated to obtain the incremental probability parameter. When the product of the incremental probability parameter and the preset expected return value is greater than the dynamic execution threshold, a coupon distribution instruction for the user is generated and output. The generation of the dynamic execution threshold is also controlled by the feedback adjustment mechanism of the budget consumption rate: calculate the actual budget consumption rate in the current statistical period; calculate the rate deviation value between the actual budget consumption rate and the preset benchmark consumption rate; use the proportional-integral-derivative control algorithm to process the rate deviation value and generate the benchmark adjustment amount of the initial decision threshold; when the rate deviation value indicates that the consumption rate is higher than the preset benchmark consumption rate, the initial decision threshold is increased in a non-linear increment; when the rate deviation value indicates that the consumption rate is lower than the preset benchmark consumption rate, the initial decision threshold is decreased in a linear decrement. The steps for calculating the information entropy of interactive behavior data include: obtaining the user's click interval time series, swipe trajectory curvature sequence, and back operation frequency on the current page; mapping the click interval time series to temporal stationarity features and the swipe trajectory curvature sequence to spatial deterministic features; calculating the joint information entropy of the temporal stationarity features and the spatial deterministic features; and determining that the user is in a highly uncertain disordered operation state, i.e., a fuzzy interval, when the joint information entropy is greater than a preset high entropy threshold.

2. The user coupon distribution method of a life platform according to claim 1, wherein, In the step of generating a dynamic execution threshold, the calculation logic of the traffic backpressure index includes: obtaining the normalized queue length and normalized enqueue acceleration of the distribution request queue in the current statistical period; performing a weighted summation of the normalized queue length and normalized enqueue acceleration to obtain the traffic backpressure index, wherein the weight coefficient of the normalized enqueue acceleration is positively correlated with the historical average response latency of the system; and using a preset nonlinear mapping function to convert the traffic backpressure index into a positive threshold bias, wherein the nonlinear mapping function is set to output an exponentially increasing bias when the traffic backpressure index exceeds the warning value, so as to improve the distribution admission standard during the request queue backlog stage.

3. The user coupon distribution method of a life platform according to claim 1, wherein, The steps for monitoring the rate of change of information entropy include: starting from the moment the visual marker is rendered, continuously collecting the user's touch position coordinates and page scrolling speed on the user's terminal screen; for each sampling point, calculating the spatial distribution dispersion of the touch position coordinates and the temporal series volatility of the page scrolling speed, and defining the weighted combination of the spatial distribution dispersion and the temporal series volatility as the instantaneous entropy value; calculating the rate of change of the instantaneous entropy value with respect to time; where a negative rate of change indicates that the user's attention is focused on the visual marker, and the absolute value of the rate of change indicates the intensity of the user's response to the visual marker.

4. The method for distributing user coupons on a lifestyle platform according to claim 1, characterized in that, The steps for calculating the incremental probability parameter follow the following dual-path differential logic: input the interaction behavior data into the benchmark evaluation channel, the benchmark evaluation channel masks the preset benefit parameters during the calculation process, and outputs the natural conversion probability that represents the user's inherent conversion intention; Simultaneously, interactive behavior data and preset rights parameters are input into the intervention evaluation channel. The intervention evaluation channel uses the preset rights parameters as feature input and outputs the conditional conversion probability. A subtraction operation is performed to subtract the natural conversion probability from the conditional conversion probability in order to eliminate the existing probability component that the user will convert regardless of whether they receive intervention, and obtain the incremental probability component that is only triggered by the preset rights parameters.

5. The method for distributing user coupons on a lifestyle platform according to claim 2, characterized in that, The flow back pressure index is calculated using the following formula: ,in, The flow back pressure index, The current normalized queue length for distributing requests. The rate of change of the queuing rate is the normalized queuing acceleration. α and β are preset length weighting coefficients and acceleration weighting coefficients, respectively, and the value of β is greater than the value of α to enhance the feedforward response to the flow growth trend.

6. The user coupon distribution method of a life platform according to claim 1, wherein, The visual markup rendering instruction without discount attributes includes local update parameters for the current rendering tree of the user terminal: parsing the document object model structure of the currently displayed page of the user terminal, identifying non-interactive blank areas located within the visible area; generating visual objects with high-contrast border attributes or dynamic light effect attributes as visual marks, and the visual objects are not associated with business jump links or price reduction data; Set the insertion position coordinates of the visual object to the center point of the non-interactive blank area so that the display of the visual mark does not change the original business layout of the page.

7. The user coupon distribution method of a life platform according to claim 1, wherein, The method also includes a model calibration step based on random control samples: during the execution phase of generating coupon distribution instructions, according to a preset random sampling ratio, the coupon distribution instructions of some users who meet the distribution conditions are intercepted, and the corresponding user data is marked as control sample. Track the actual conversion behavior of the control group sample when no coupons were obtained, and record it as real natural conversion data; use the real natural conversion data to calibrate the benchmark evaluation channel in the dual-path extrapolation model, and update the model parameters of the benchmark evaluation channel by minimizing the error between the natural conversion probability and the real natural conversion data.

8. The user coupon distribution method of a life platform according to claim 1, wherein, The method is applied to a distributed architecture that includes edge computing nodes and a cloud core cluster: the steps of calculating information entropy and monitoring the rate of change are executed on the gateway node deployed at the network edge, using edge computing power to achieve real-time response to user interaction behavior and filtering of invalid requests; the steps of calling the dual-path inference model and generating distribution instructions are executed on the cloud core cluster, which only receives the validity verification signal verified by the gateway node and the corresponding user feature data. By proactively verifying intent and controlling traffic backpressure at the gateway node, invalid interaction traffic and overload traffic are intercepted before the core cluster in the cloud receives business requests.

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