A delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weight

Abstract

The application discloses a putting strategy method based on multi-behavior probability modeling and value weight dynamic optimization, and relates to the technical field of putting strategy management. The putting strategy method based on multi-behavior probability modeling and value weight dynamic optimization comprises the following steps: putting storage space interference monitoring; behavior synchronization and advertisement putting data feedback monitoring; value weight eligibility determination. The application carries out putting storage space interference evaluation to determine whether to carry out click event accumulation interference quantification, carries out behavior synchronization interference quantification to determine whether to carry out behavior synchronization interference optimization after the click event accumulation interference quantification is qualified, carries out advertisement putting data feedback delay evaluation to determine whether to carry out value weight eligibility evaluation after the behavior synchronization interference quantification is qualified, and the effect of improving putting distribution timeliness is achieved, and the problem of low putting distribution timeliness of an advertisement putting background in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of campaign strategy management technology, and in particular to a campaign strategy method based on multi-behavior probability modeling and dynamic optimization of value weights. Background Technology

[0002] The existing overall advertising process can be broken down into the following stages: First, collect and integrate multi-source data reflecting the interaction between users and advertisements, such as user behavior data (exposure, clicks, add-to-cart, favorites, purchases, resource consumption, etc.) and historical conversion data (purchase conversion records, add-to-cart time, etc.). Then, use a multi-task learning model (such as MMoE) to simultaneously predict the probability of multiple behaviors such as clicks, add-to-cart, favorites, and purchases. This can accurately and comprehensively capture users' diverse interests, providing a basis for advertising and reasonable resource allocation. It supports the Bandit algorithm to dynamically optimize the value weight of each user behavior based on behavior probability, and uses the advertising performance as a feedback signal in real time to update the probability distribution of candidate weight combinations and select the optimal weight. This enables the advertising backend to accurately process data (such as number of impressions, number of clicks, click-through rate, etc.). Then, through a real-time behavior feedback loop, such as dividing the advertising period, the multi-task learning model and value weights are continuously updated.

[0003] Existing technology divides the advertising campaign cycle and allocates resources by assigning value weights. Based on the optimization goals and initial weights, it generates and executes an initial advertising campaign strategy, collects advertising performance data in real time during the initial campaign, such as resource consumption, analyzes the performance of the previous stage, and dynamically adjusts the initial weights of the optimization goals for the next stage based on the analysis results. Based on the adjusted weights of the optimization goals, it optimizes the allocation of advertising resources in the advertising campaign backend (such as reallocating resources and adjusting the advertising campaign algorithm).

[0004] For example, Chinese invention patent CN113674024B discloses a method and apparatus for pushing the advertising screen placement value, as well as an advertising screen. This includes using radar to acquire pedestrian traffic and individual information over a preset time period; assigning weights to pedestrian traffic, individual information, and the preset time period; calculating the benchmark cost for advertising screen placement during the preset time period based on pedestrian traffic, individual information, the preset time period, and the corresponding weights; acquiring information about the advertising promoter; planning a suitable advertising placement pricing scheme based on the benchmark cost and the advertising promoter information; and pushing the advertising placement pricing scheme to the corresponding advertising promoter so that the advertising promoter can make a decision.

[0005] For example, Chinese invention patent CN110210898B discloses a user behavior-based advertising push method, apparatus, and device, which includes: establishing a reference advertising set for a target user based on the target user's transaction data; determining reference advertisements to be pushed to the target user from the reference advertising set based on the target user's total asset amount; pushing the determined reference advertisements to the target user; acquiring behavioral data within a predetermined time after the determined reference advertisements are pushed to the target user; and adjusting the reference advertisements or the weights of the reference advertisements in the reference advertising set based on the behavioral data.

[0006] The above-mentioned technology has at least the following technical problems:

[0007] When pre-loading and residual ad creatives cause storage space constraints on the ad delivery platform, it may lead to increased memory usage during the next round of ad delivery, resulting in a backlog of click events. This can ultimately slow down event processing and prevent click, add-to-cart, and other behavioral data from being synchronized to the server in real time. Consequently, ad delivery data feedback may be delayed, leading to a delay in dynamic weight optimization based on feedback signals. Summary of the Invention

[0008] To address the technical problem of low timeliness in ad delivery allocation in existing technologies, this invention provides a delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights. The technical solution is as follows:

[0009] On the one hand, a delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights is provided. This includes: evaluating the storage space interference during ad delivery to obtain delivery storage results; if the delivery storage results are satisfactory, click event accumulation interference quantification (which reflects the impact of event accumulation on event processing speed) is not performed; otherwise, based on the generated click event accumulation interference quantification results, it is determined whether to perform click event accumulation interference optimization to reduce the interference of memory overflow caused by click event accumulation on ad delivery processing speed. Click event accumulation interference optimization includes thread allocation and parallel call setting; after the click event accumulation interference quantification is satisfactory, the click event... The latency of synchronizing user behavior data to the server is quantified to reflect the interference with ad delivery data feedback. Based on the obtained quantification results of behavior synchronization interference, it is determined whether to optimize behavior synchronization interference to reduce the latency of synchronizing user behavior data to the server. Behavior synchronization interference optimization includes compression level optimization and time window duration setting. After the behavior synchronization interference quantification is qualified, the latency of ad delivery data feedback is evaluated to reflect the degree of impact of ad delivery data feedback latency on real-time delivery performance. Based on the obtained ad delivery feedback latency results, it is determined whether to conduct value weight qualification evaluation to reflect the real-time performance when updating value weights based on the Bandit algorithm.

[0010] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0011] 1. By evaluating storage space interference, it determines whether click event accumulation interference quantification is needed. If not, a qualified storage prompt is sent; otherwise, the generated click event accumulation interference quantification result determines whether click event accumulation interference optimization is needed. In-depth analysis of various interference factors that may affect storage space during operation, such as interference caused by storage capacity limitations, helps reduce interference caused by click event accumulation, improving the stability and response speed of the ad delivery backend. After qualified click event accumulation interference quantification, behavioral synchronization interference quantification is performed to generate behavioral synchronization interference quantification results. Based on these results, it determines whether behavioral synchronization interference optimization is needed, which helps reduce uplink traffic and make batch processing granularity finer, thus ensuring the accuracy and timeliness of behavioral synchronization. After qualified behavioral synchronization interference quantification, ad delivery data feedback delay evaluation is performed to generate ad delivery feedback delay results. Based on these results, it determines whether value weight qualification evaluation is needed, which helps improve the timeliness and accuracy of data feedback time information collection and analysis during ad delivery, thereby improving the timeliness of ad delivery backend allocation and solving the problem of low timeliness in ad delivery backend allocation in existing technologies.

[0012] 2. By harmonic averaging the click event accumulation interference data, a quantitative result of click event accumulation interference is obtained. Compared with existing technologies that are singular and do not consider multi-dimensional correlation parameters, ignoring the complex relationships between different parameters and their comprehensive impact on the overall interference, the harmonic averaging method can fully consider the relative importance and interrelationships between various interference data, organically integrating multiple dimensions of interference factors. This helps to accurately assess the impact of event accumulation on event processing speed. Based on the quantitative result of click event accumulation interference, if the quantitative result of click event accumulation interference is greater than the preset quantitative result of click event accumulation interference, click event accumulation interference optimization is performed. This helps to show the difference in the impact of event accumulation on processing speed, thereby improving the processing efficiency of click events in the advertising backend, reducing the possibility of event accumulation, and thus reducing the degree of interference.

[0013] 3. By adjusting and optimizing the value weights when the delay value of the ad delivery data feedback is greater than the preset ad delivery data feedback value, it helps to improve the accuracy of ad delivery and the accuracy and timeliness of ad delivery feedback. Then, when the delay value of the ad delivery data feedback is not greater than the preset ad delivery data feedback value, the probability distribution of the value weight combination is updated based on the Bandit algorithm and the corresponding value weights are obtained, which helps to improve the efficiency and accuracy of value weight updates.

[0014] 4. After evaluating the interference of storage space during the ad delivery process, when the storage result exceeds the preset storage result, click event accumulation interference is quantified. This helps to specifically quantify the impact of storage space interference on event processing speed. Through the correlation analysis between the evaluation of storage space interference and the quantification of click event accumulation interference, it helps to promote the dynamic improvement of the anti-interference capability of the ad delivery process.

[0015] 5. By evaluating the delay in ad delivery feedback and adjusting the value weight when the delay value exceeds the preset value, it helps to quantify the impact of ad delivery feedback delay on the real-time performance of the Bandit algorithm when updating the value weight. By evaluating the value weight's suitability after assessing the ad delivery data feedback delay, it helps to quantify the correlation effect and achieve the optimal balance between the real-time performance of the Bandit algorithm and the real-time performance of ad delivery in delayed feedback scenarios. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0017] Figure 1 This is a flowchart of the delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weight provided in the embodiments of the present invention;

[0018] Figure 2 This is a schematic diagram illustrating the overall overview of the delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weight provided in the embodiments of the present invention.

[0019] Figure 3 This is an overview diagram of the behavior synchronization interference optimization of the delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weight provided in the embodiments of the present invention;

[0020] Figure 4 This is a schematic diagram of the structure of the multi-task learning model provided in the embodiments of this application. Detailed Implementation

[0021] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0022] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0023] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0024] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0025] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0026] This invention provides a delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights. For example... Figure 1 The flowchart shown is for a delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights. The processing flow of this method may include the following steps:

[0027] First, monitoring storage space interference during ad delivery: The system evaluates storage space interference during ad delivery to obtain delivery storage results. If the delivery storage results are satisfactory, click event backlog interference quantification (which reflects the impact of event backlog on event processing speed) is not performed. Otherwise, based on the generated click event backlog interference quantification results, it is determined whether click event backlog interference optimization should be performed to reduce the interference of memory overflow caused by click event backlog on ad delivery processing speed. Click event backlog interference optimization includes thread allocation and parallel call settings. By monitoring storage space interference, it helps to identify and avoid click event backlog and processing delays caused by cache overflow before the end-side storage pressure is triggered, thereby ensuring the stability and continuity of the ad delivery chain.

[0028] Secondly, monitoring of behavior synchronization and ad delivery data feedback: After the click event accumulation interference is quantified to be satisfactory, the latency of synchronizing user behavior data corresponding to the click event to the server is quantified to reflect the interference on ad delivery data feedback. Based on the obtained behavior synchronization interference quantification results, it is determined whether to optimize behavior synchronization interference to reduce the latency of user behavior data synchronization to the server. Behavior synchronization interference optimization includes compression level optimization and time window duration setting. By monitoring behavior synchronization and ad delivery data feedback, it is helpful to quantify the impact of latency on delivery feedback in real time in the user behavior data upload link, and ensure the timeliness and integrity of ad delivery data through compression and window optimization strategies.

[0029] Finally, the value weight qualification assessment: After the behavior synchronization interference quantification is qualified, the delay in advertising delivery data feedback is evaluated to reflect the degree to which the real-time delivery effect is affected by the delay in advertising delivery data feedback. Based on the obtained advertising delivery feedback delay results, it is determined whether to conduct a value weight qualification assessment to reflect the real-time performance when updating value weights based on the Bandit algorithm. By conducting a value weight qualification assessment, it is helpful to dynamically calibrate the value weight update rhythm of the Bandit algorithm when there is a delay in advertising delivery feedback, prevent strategy drift caused by lagging signals, and improve the robustness of real-time delivery decisions.

[0030] Before designing the delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weight provided in this application, a database is established to store various settings data. The database includes, but is not limited to, preset click event accumulation number, preset request waiting time, preset maximum synchronization delay, etc., and the various values ​​are directly set by technical personnel.

[0031] like Figure 2 The diagram shown is a general overview of the delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights provided in this application embodiment. Figure 2It can be known that the interference value of the placement storage space is obtained through interference monitoring of the placement storage space. When the monitored interference value of the placement storage space is not greater than the preset interference value of the placement storage space obtained from the database, the placement storage result is recorded as qualified; otherwise, click event accumulation interference quantization is performed to obtain the click event accumulation interference quantization result. When the monitored click event accumulation interference quantization result is greater than the preset click event accumulation interference quantization result, click event accumulation interference optimization is performed, specifically including simultaneously performing thread number allocation and parallel call number setting. On the contrary, the interference reflection value of behavior synchronization is first obtained through behavior synchronization and advertisement placement data feedback monitoring. When the monitored interference reflection value of behavior synchronization is greater than the preset interference reflection value of behavior synchronization, behavior synchronization interference optimization is performed, specifically including sequentially performing compression level optimization and time window duration setting. On the contrary, then the advertisement placement data feedback delay value is obtained. When the monitored advertisement placement data feedback delay value is greater than the preset advertisement placement data feedback value, value weight adjustment optimization is performed. On the contrary, the corresponding advertisement placement data is marked as qualified advertisement placement data, and value weight qualification evaluation is performed to obtain the value weight qualification evaluation value. When the monitored value weight qualification evaluation value is greater than the preset value weight qualification evaluation value, a placement strategy qualification prompt is sent; otherwise, a placement strategy unqualified prompt is sent.

[0032] In this embodiment, through the correlation analysis of interference monitoring of the placement storage space, behavior synchronization, advertisement placement data feedback monitoring, and value weight qualification determination, it is beneficial to achieve adaptive optimization and stability guarantee in scenarios of limited background resources, network fluctuations, and feedback delays in advertisement placement; furthermore, it helps to maximize the utilization rate of background resources in advertisement placement, ensure the real-time placement ability of the advertisement placement background, and contribute to the improvement of the timeliness of placement allocation in the advertisement placement background.

[0033] Furthermore, the storage space interference during ad delivery is evaluated to obtain the delivery storage result. The specific process is as follows: By quantifying the storage space utilization rate of the ad delivery end monitored by the server and the preset storage space utilization rate, which reflects the storage space occupation of the ad delivery end during ad delivery, a delivery storage space interference value is obtained. This value reflects the influence of the storage space occupation of the ad delivery end on the click event accumulation rate. Here, the quantification means performing a ratio calculation. The preset storage space utilization rate is represented by the average value of the storage space utilization rate of the ad delivery end over a historical period. Based on the delivery storage space interference value, a judgment is made: if the delivery storage space interference value is not greater than the preset delivery storage space interference value obtained from the database, the delivery storage result is recorded as qualified; otherwise, the delivery storage result is recorded as unqualified and click event accumulation interference is quantified. Here, the preset delivery storage space interference value is obtained from a historical period. By quantifying click event accumulation interference when the delivery storage result is unqualified, it is helpful to accurately identify the click event processing bottleneck caused by insufficient storage space, thereby achieving rapid intervention and ensuring the stability of ad delivery data.

[0034] Specifically, the process for quantifying click event backlog interference is as follows: Click event backlog interference data reflecting click event backlog is harmonic-averaged to obtain a quantified result reflecting the decrease in event processing efficiency caused by backlog, thus quantifying the impact of backlog on event processing speed. Click event backlog interference data includes request wait time interference values, click event backlog interference values, and unqualified delivery storage interference values. The request wait time interference value represents the result of weighting the comparison between the preset request wait time interference harmonic value and the preset request wait time, and then adding it to a preset request wait time constant to ensure the request wait time interference value is non-zero. The preset request wait time is represented by the average request wait time over a historical time period; comparison is performed, i.e., a ratio calculation is conducted using log analysis tools such as New... Relic monitors the total time it takes for ad requests to reach the ad delivery backend and receive a response within a specified click event backend period, which is used as the request waiting time. The click event backend interference value represents the result of weighting the comparison between the preset click event backend quantity and the preset click event backend quantity using a weighted value, and then adding it to a preset click event backend quantity constant used to ensure the interference value is non-zero. The preset click event backend quantity is represented by the average click event backend quantity over a historical time period. The total number of pending click events in the queue used to store pending events within the specified click event backend period is monitored by a counter and used as the click event backend quantity. The unqualified delivery storage interference value represents the time taken to reach the ad delivery backend after weighting the preset delivery storage value. The interference value of the delivery storage space that is greater than the preset delivery storage space interference value is weighted and superimposed with the preset delivery storage constant used to ensure that the unqualified delivery storage interference value is non-zero; the specified click event accumulation interference time period represents the preset time period corresponding to the click event accumulation interference quantification; the judgment is made based on the click event accumulation interference quantification result: if the click event accumulation interference quantification result is greater than the preset click event accumulation interference quantification result obtained from the database, click event accumulation interference optimization is performed, otherwise behavior synchronization interference quantification is performed. The preset click event accumulation interference quantification result is represented by the average value of the click event accumulation interference quantification results of historical time periods. The preset click event accumulation quantity constant, the preset request waiting time constant, and the preset delivery storage constant are all preset by preset personnel.

[0035] It should be added that, in the embodiments of this application, a set of mapping groups obtained from the database and pre-configured by preset personnel is presented, which includes multiple mapping sets. The mapping relationship defined in the mapping group is variable, which can be a one-to-one correspondence between single parameters or a many-to-one relationship of multiple parameters corresponding to one parameter. Specifically, the preset click event accumulation interference harmonic data provided in this embodiment is determined according to the proportion of the corresponding click event accumulation interference data in the overall data, including a preset request waiting time interference harmonic value, a preset click event accumulation quantity interference harmonic value, and a preset delivery storage harmonic value, which are used to reflect the degree of influence of click event accumulation interference data on the click event accumulation interference quantification result. A one-to-one or many-to-one mapping relationship can be established between the click event accumulation interference data and the preset click event accumulation interference harmonic data. By inputting the click event accumulation interference data collected in real time into the corresponding mapping group, the corresponding preset click event accumulation interference harmonic data is output according to the preset mapping relationship. The value range of the preset click event accumulation interference harmonic data is limited to the range of 0-1.

[0036] In this embodiment, the impact of click event backlog interference data on event processing speed is analyzed by quantifying the data, thereby obtaining accurate quantitative results of click event backlog interference. The various indicators in the click event backlog interference data do not exist in isolation, but rather reflect the impact of event backlog on event processing speed through complex interactions and influences. The following are the interaction and influence mechanisms between the various click event backlog interference data: Larger click event backlog interference data means larger request waiting times, a larger number of backlogged click events, and a larger storage space interference value exceeding the preset storage space interference value. These three factors are interconnected and influence each other. When the number of backlogged click events increases, ad requests may need to wait longer after reaching the ad delivery backend before being processed. Therefore, an increase in the number of backlogged click events directly leads to a longer request waiting time. A larger storage space interference value exceeding the preset storage space interference value means a greater impact of ad delivery storage space usage on the click event backlog rate. This may result in new events not being stored and processed in a timely manner, causing increased queue congestion, and consequently increasing request waiting times and the number of backlogged click events. By deeply analyzing the combined effects of click event accumulation and interference data, it is possible to accurately assess the impact of event accumulation on event processing speed.

[0037] Furthermore, the specific process for optimizing click event stacking interference is as follows: The click event stacking interference quantification result and the number of concurrent requests monitored by the network layer counter are input into a thread allocation mapping set in the database. The resulting thread allocation value is used as the thread allocation value to be allocated to the user and ad interaction data (such as click-through rate, purchase rate, etc.) corresponding to the click event stacking interference quantification result. The database contains a mapping set that reflects the mapping relationship between the click event stacking interference quantification result, the number of concurrent requests, and the corresponding thread allocation value. The specific process for setting the parallel call quantity is as follows: The click event stacking interference quantification result and the number of concurrent requests are input into a parallel call quantity mapping set in the database. The resulting parallel call quantity adjustment ratio is obtained. Within a preset parallel call quantity adjustment range, the adjustment step size is used to gradually increase the parallel call quantity. The preset parallel call quantity adjustment range is set in advance by preset personnel. The database contains a mapping set for... This is a mapping set that reflects the mapping relationship between the click event backlog interference quantification result and the number of concurrent requests, and the corresponding parallel call quantity adjustment ratio. During the click event backlog interference optimization process, when it is detected that the click event backlog interference quantification result of the next adjacent specified click event backlog interference time period is not greater than the preset click event backlog interference quantification result, the click event backlog interference optimization is stopped, and behavior synchronization interference quantification is performed. If the click event backlog interference optimization ends and the click event backlog interference quantification result of the next adjacent specified click event backlog interference time period is still greater than the preset click event backlog interference quantification result, a click event backlog interference alarm is sent. Allocating the number of threads to be allocated for user and ad interaction data according to the thread quantity allocation value helps to avoid resource waste caused by expansion. By using the magnitude corresponding to the parallel call quantity adjustment ratio as the adjustment step size, the number of parallel calls is gradually increased, which helps to gradually increase the pressure within a controllable range, both quickly alleviating click event backlog and preventing a one-time increase in traffic from causing data collapse.

[0038] In this embodiment, simultaneously acquiring the thread number allocation value and setting the parallel call quantity helps to form a complementary relationship and avoid the performance limitations of the ad delivery backend caused by single path optimization. By re-acquiring the click event accumulation interference quantification result for verification in the next adjacent specified click event accumulation interference time period, it helps to prevent the click event accumulation interference from expanding, improves the processing throughput of ad click events, and thus helps to ensure the real-time performance and accuracy of ad delivery while achieving the best balance between maximizing the utilization rate and stability of ad delivery backend resources.

[0039] Furthermore, based on the obtained quantitative results of behavioral synchronization interference, a decision is made on whether to optimize behavioral synchronization interference. The specific process is as follows: The maximum behavioral synchronization delay interference value and the qualified click event accumulation interference result are harmonicly averaged to obtain the behavioral synchronization interference response value, which reflects the latency of user behavior data synchronization to the server. This value is used to quantify the risk of synchronization failure caused by the superposition of terminal storage pressure and click event accumulation during the user behavior data upload process. Based on this value, a judgment is made: if the behavioral synchronization interference response value is greater than the preset value, behavioral synchronization interference optimization is performed; otherwise, an assessment of the advertising data feedback latency is conducted. The preset value is represented by the average of behavioral synchronization interference response values ​​over a historical time period. The maximum behavioral synchronization delay interference value represents the result of weighting the preset harmonic value by comparing the maximum behavioral synchronization delay with the preset maximum synchronization delay, and then adding the result to the preset synchronization delay constant used to ensure the maximum behavioral synchronization delay interference value is non-zero. The preset maximum synchronization delay is represented by the average of the maximum behavioral synchronization delay over a historical time period. A timer is used to monitor the synchronization of all user behavior data within a specified behavioral synchronization time period. When the longest time taken to reach the server exceeds the preset longest synchronization time, the difference between the longest time taken for user behavior data to synchronize to the server and the preset longest synchronization time is taken as the maximum behavior synchronization delay. The specified behavior synchronization time period represents the preset time period corresponding to the behavior synchronization interference quantification. The qualified click event accumulation interference result represents the sum of the preset click event accumulation interference harmonic value (weighted at values ​​not greater than the preset click event accumulation interference quantification result) and the preset click event accumulation interference constant used to ensure the qualified click event accumulation interference result is non-zero. Both the preset synchronization delay constant and the preset click event accumulation interference constant are preset by designated personnel. Harmonic averaging the maximum behavior synchronization delay interference value and the qualified click event accumulation interference result helps quantify the correlation between them, thus facilitating a comprehensive analysis of the latency of user behavior data synchronization to the server. For example, a larger qualified click event accumulation interference result means a stronger click event accumulation interference effect, which may lead to dual congestion on the client side and network buffer, resulting in a larger maximum behavior synchronization delay interference value.

[0040] It should be added that, in the embodiments of this application, a set of mapping groups obtained from the database and pre-configured by preset personnel is presented, which includes multiple mapping sets. The mapping relationship defined in the mapping group is variable, which can be a one-to-one correspondence between single parameters or a many-to-one relationship of multiple parameters corresponding to one parameter. Specifically, the preset behavior synchronization interference data provided in this embodiment is determined based on the proportion of the corresponding maximum behavior synchronization delay interference value and the qualified click event accumulation interference result in the overall data. It includes a preset synchronization delay harmonic value and a preset click event accumulation interference harmonic value, which are used to reflect the degree of influence of the maximum behavior synchronization delay interference value and the qualified click event accumulation interference result on the behavior synchronization interference reflection value. The maximum behavior synchronization delay interference value and the qualified click event accumulation interference result can establish a one-to-one or many-to-one mapping relationship with the preset behavior synchronization interference data. By inputting the real-time collected maximum behavior synchronization delay interference value and qualified click event accumulation interference result into the corresponding mapping group, the corresponding preset behavior synchronization interference data is output according to the preset mapping relationship. The value range of the preset behavior synchronization interference data is limited to the range of 0-1.

[0041] like Figure 3 The diagram shown is an overview of the behavior synchronization interference optimization of the delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights provided in this application embodiment. Figure 3 It can be seen that: when the detected behavior synchronization interference response value is not greater than the preset behavior synchronization interference response value, the advertising data feedback delay assessment is performed; otherwise, behavior synchronization interference optimization is performed. First, behavior synchronization interference optimization verification is performed. When the obtained predicted behavior probability is within the preset behavior probability range, and the behavior synchronization interference response value re-obtained in the next adjacent specified behavior synchronization time period is not greater than the preset behavior synchronization interference response value, the advertising data feedback delay assessment is performed; otherwise, the time window duration is set, that is, the time window duration is gradually reduced by using the magnitude corresponding to the preset time window duration adjustment value as the adjustment step.

[0042] Specifically, compression level optimization involves adjusting the compression level based on efficient algorithms to balance behavior synchronization latency. After compression level optimization, behavior synchronization interference optimization verification is performed. Specifically, user-ad interaction data is input into a multi-task learning model, and the output quantifies the independent and joint probability distributions of user behavior after ad exposure reaches the user, thus reflecting the predicted behavior probability of multiple behaviors. Specifically, user-ad interaction data and predicted behavior probabilities are divided by pre-defined personnel to obtain training data. This training data is input into the MMoE model for training to obtain a trained multi-task learning model. Newly acquired user-ad interaction data is then input into the trained multi-task learning model, outputting predicted behavior probabilities. These predicted behavior probabilities are compared with a pre-defined behavior probability range. When the predicted behavior probability is within the pre-defined range, and the behavior synchronization interference response value re-acquired in the next adjacent specified behavior synchronization time period is not greater than the pre-defined behavior synchronization interference response value, an ad delivery data feedback latency assessment is performed; otherwise, a time window duration is set. The pre-defined behavior probability range is pre-set by pre-defined personnel. This is achieved using efficient algorithms, such as DTA (Dynamic Threshold). The algorithm (dynamic threshold algorithm) adjusts the compression level to balance the behavior synchronization latency, which helps to control CPU (Central Processing Unit) consumption while reducing the amount of data transmission, thereby shortening the end-to-end synchronization time and reducing long-tail latency. By gradually reducing the time window duration step by step with the magnitude corresponding to the preset time window duration adjustment value, it helps to quickly converge latency fluctuations while maintaining throughput and avoid lag feedback caused by excessively wide windows.

[0043] It should be added that the time window duration setting is as follows: The behavioral synchronization interference response value and the time window throughput monitored by a network layer general tool such as iperf3 are input into a time window duration mapping set in the database to obtain a preset time window duration adjustment value. Within the preset time window duration adjustment range, the time window duration is gradually reduced step by step, using the magnitude corresponding to the preset time window duration adjustment value as the adjustment step. The preset time window duration adjustment range is pre-set by preset personnel. The database contains a mapping set that reflects the mapping relationship between the behavioral synchronization interference response value, the time window throughput, and the corresponding preset time window duration adjustment value. After the time window duration is set, when the re-acquired predicted behavior probability is within the preset behavior probability range, and the behavioral synchronization interference response value re-acquired in the next adjacent specified behavior synchronization time period is not greater than the preset behavioral synchronization interference response value, an advertising data feedback delay assessment is performed; otherwise, a prediction anomaly prompt is sent.

[0044] In this embodiment, determining whether to optimize behavior synchronization interference based on the quantification results helps to establish a correlation between the quantification results and the optimization, ensuring that the optimization action is always aimed at the most prominent synchronization bottleneck. By performing behavior synchronization interference optimization verification after compression level optimization, it helps to achieve online trade-off verification between compression and latency, ensuring the stability of ad delivery. At the same time, the correlation between compression level optimization and time window duration setting helps to smoothly transition and complementarily enhance the latency of behavior synchronization interference.

[0045] Furthermore, the specific process for evaluating the delay in ad delivery data feedback is as follows: The ad delivery data feedback data to be reconciled is processed by harmonic averaging to obtain the ad delivery data feedback delay value. The reconciled strategy delivery feedback data includes the reconciled strategy delivery delay feedback value and the reconciled synchronization delay quantification value. The reconciled strategy delivery delay feedback value represents the result of weighting the strategy delivery feedback delay interference value and the preset strategy delivery feedback delay harmonic index. The time interval from the moment the ad is delivered to the first receipt of user feedback data related to that ad is monitored using a timer. The result of the ratio of this time interval to the preset strategy delivery feedback duration is used as the strategy delivery feedback delay interference value. The preset strategy delivery feedback duration is the time interval from the moment the ad is delivered to the first receipt of user feedback data related to that ad over a historical period. The average value is represented by the value of the synchronization delay to be harmonized. The quantified value of the synchronization delay to be harmonized is represented by the sum of the qualified behavior synchronization feedback value and the preset behavior synchronization feedback harmonization index, and the preset behavior synchronization feedback constant. The qualified behavior synchronization feedback value is represented by the behavior synchronization interference response value, which is not greater than the preset behavior synchronization interference response value. The preset behavior synchronization feedback constant is set in advance by preset personnel to avoid the synchronization delay to be harmonized being non-zero. Harmonized averaging of the feedback data of the strategy to be harmonized helps to realize the correlation analysis of the feedback data of the strategy to be harmonized to comprehensively quantify the feedback delay of the advertising data. Specifically, the larger the synchronization delay to be harmonized is, the greater the degree of behavior synchronization delay, which may lead to a lag in the response of the advertising backend to real-time strategy adjustments, and thus a larger feedback value of the strategy to be harmonized.

[0046] It should be added that, in the embodiments of this application, a set of mapping groups obtained from the database and pre-configured by preset personnel is presented, which includes multiple mapping sets. The mapping relationships defined in the mapping group are variable; they can be either a one-to-one correspondence between single parameters or a many-to-one relationship where multiple parameters correspond to one parameter. Specifically, the preset advertising delivery data feedback harmonization data provided in this embodiment is determined based on the proportion of the corresponding strategy delivery feedback delay interference value and qualified behavior synchronization feedback value in the overall data. The preset advertising delivery data feedback harmonization data includes a preset strategy delivery feedback delay harmonization index and a preset behavior synchronization feedback harmonization index, which are used to reflect the degree of influence of the strategy delivery feedback delay interference value and qualified behavior synchronization feedback value on the strategy delivery feedback data to be harmonized. A one-to-one or many-to-one mapping relationship can be established between the strategy delivery feedback delay interference value and qualified behavior synchronization feedback value and the preset advertising delivery data feedback harmonization data. By inputting the real-time collected strategy delivery feedback delay interference value and qualified behavior synchronization feedback value into the corresponding mapping group, the corresponding preset advertising delivery data feedback harmonization data is output according to the preset mapping relationship. The value range of the preset advertising delivery data feedback harmonization data is limited to the range of 0-1.

[0047] The determination of whether to conduct a value weight qualification assessment based on the obtained ad delivery feedback delay results is as follows: Based on the ad delivery data feedback delay value: If the ad delivery data feedback delay value is greater than the preset ad delivery data feedback value obtained from the database, the corresponding ad delivery data reflecting the real-time ad delivery effect is marked as unqualified ad delivery data, and value weight adjustment and optimization are performed. Conversely, the corresponding ad delivery data is marked as qualified ad delivery data, and value weight qualification assessment is performed. The preset ad delivery data feedback value is represented by the average of ad delivery data feedback delay values ​​over a historical time period. Value weight adjustment and optimization are used to adjust the Bandit update frequency to improve the Bandit algorithm's responsiveness to ad delivery feedback delay and the accuracy of its update control, thereby improving real-time delivery performance. The specific process of value weight adjustment and optimization is as follows: Mapping the ad delivery data feedback delay value to the Bandit update frequency mapping set in the database, which is monitored by software tools such as AppsFlyer, to obtain the preset Bandit update frequency mapping value. The Bandit update frequency ratio is adjusted step-by-step, gradually increasing the Bandit update frequency. The database contains a mapping set that reflects the mapping relationship between ad delivery data feedback latency and ad delivery traffic, and the corresponding preset Bandit update frequency ratio. When the ad delivery data feedback latency value for the next adjacent specified delivery data feedback time period is not greater than the preset ad delivery data feedback value, the value weight adjustment and optimization stops, and a value weight qualification assessment is performed. If the Bandit update frequency reaches the preset maximum Bandit update frequency, and the ad delivery data feedback latency value for the next adjacent specified delivery data feedback time period is still greater than the preset ad delivery data feedback value, an abnormal Bandit update prompt is sent. By gradually increasing the Bandit update frequency step-by-step, using the amplitude corresponding to the preset Bandit update frequency ratio as the adjustment step-by-step, it helps to quickly improve the value weight refresh speed when the feedback latency exceeds the standard, while preventing jitter caused by a one-time large frequency adjustment, achieving smooth and controllable real-time optimization.

[0048] Specifically, the value weight qualification assessment process is as follows: The value weight combination is updated based on the Bandit algorithm to obtain the corresponding value weight; the resource allocation delay value and the ad click count assessment value are harmonic averaged to obtain the value weight qualification assessment value; the resource allocation delay value represents the result of weighting the preset harmonic value against the preset maximum resource allocation delay time and resource allocation response time, and is superimposed with the preset resource allocation constant used to ensure the resource allocation delay value is non-zero. The preset maximum resource allocation delay time is represented by the average of the resource allocation response times over historical periods, monitored by a timer from receiving the ad request to completing resource allocation and issuing the ad delivery instruction. The ad click count assessment value represents the result of weighting the preset harmonic value against the ad click increase count in the previous specified ad delivery data feedback period, and is superimposed with the preset constant used to ensure the ad click count assessment value is non-zero. The results are obtained by superimposing preset ad click constants; by monitoring the total number of clicks of the ad corresponding to the value weight obtained in the next adjacent specified delivery data feedback period through a counter, when the total number of clicks of the ad corresponding to the value weight obtained in the next adjacent specified delivery data feedback period is greater than the total number of clicks of the ad in the previous adjacent specified delivery data feedback period, the corresponding number of times greater than is counted as the ad click increment; the qualified value weight assessment value is compared with the preset qualified value weight assessment value; if the qualified value weight assessment value is greater than the preset qualified value weight assessment value, a delivery strategy qualified prompt is sent, otherwise a delivery strategy unqualified prompt is sent. Here, the preset delivery resource allocation constant and the preset ad click constant are both set in advance by preset personnel; by harmonic averaging the delivery resource allocation delay value and the ad click count assessment value, it is helpful to correlate and quantify the qualification of value weight updates and the ad delivery effect. Specifically, the larger the ad click count assessment value, the longer the delivery resource allocation response to the ad click increment may be, which may lead to a greater deviation between the preset maximum delay time of delivery resource allocation and the delivery resource allocation response time, and may lead to a smaller delivery resource allocation delay value.

[0049] It should be added that, in the embodiments of this application, a set of mapping groups obtained from the database and pre-configured by preset personnel is presented, which includes multiple mapping sets. The mapping relationships defined in the mapping group are variable; they can be either a one-to-one correspondence between single parameters or a many-to-one relationship where multiple parameters correspond to one parameter. Specifically, the preset value weight qualified harmonic data provided in this embodiment is determined based on the proportion of the corresponding resource allocation response time and the number of ad clicks in the overall data. The preset value weight qualified harmonic data includes a preset strategy delivery feedback delay harmonic index and a preset behavior synchronization feedback harmonic index, which are used to reflect the degree of influence of resource allocation response time and the number of ad clicks on the resource allocation delay value and the ad click count evaluation value. A one-to-one or many-to-one mapping relationship can be established between the resource allocation response time and the number of ad clicks and the preset value weight qualified harmonic data. By inputting the real-time collected resource allocation response time and the number of ad clicks into the corresponding mapping group, the corresponding preset value weight qualified harmonic data is output according to the preset mapping relationship. The value range of the preset value weight qualified harmonic data is limited to the range of 0-1.

[0050] In this embodiment, the evaluation of delayed advertising data feedback, the optimization of value weight adjustment, and the evaluation of value weight qualification are interconnected. The evaluation of delayed advertising data feedback triggers the optimization of value weight adjustment, and the evaluation of value weight qualification verifies the effect of the optimization of value weight adjustment. The coupling of these three aspects ensures the timeliness of value weight updates for advertising, thereby achieving the timeliness and stability of closed-loop optimization of advertising in click event accumulation scenarios.

[0051] like Figure 4 The diagram shown is a structural schematic of the multi-task learning model provided in an embodiment of this application. Figure 4As can be seen, user behavior data (exposure, clicks, adding to cart, favorites, purchases, resource consumption, etc.) are input into the MMoE model, and the output predicts the probabilities of multiple behaviors such as clicks, adding to cart, favorites, and purchases. The core components of the MMoE model include an input layer, a shared expert layer, an expert weighted combination, a gating network, a task-specific tower, and an output layer. The input layer is connected to the shared expert layer, the expert weighted combination, and the gating network, respectively. The shared expert layer in the example includes three expert networks, each of which is connected to the task 1 weighted group, task 2 weighted group, task 3 weighted group, and task 4 weighted group contained in the expert weighted combination. The expert weighted combination is connected to the task-specific tower, including task 1 specific, task 2 specific, task 3 specific, and task 4 specific. The gating network includes the task 1 gating network, task 2 gating network, task 3 gating network, and task 1 gating network, which are connected to the corresponding expert weighted combination. The task-specific tower is connected to the output layer. Multi-task joint learning is achieved by inputting user behavior data.

[0052] In summary, this application embodiment assesses the interference of the delivery storage space to determine whether click event accumulation interference quantification should be performed. If click event accumulation interference quantification is not performed, a delivery storage qualified prompt is sent; otherwise, the click event accumulation interference optimization is determined based on the generated click event accumulation interference quantification result. In-depth analysis of various interference factors that may affect the storage space during operation, such as interference caused by storage capacity limitations, helps reduce interference caused by click event accumulation, improving the stability and response speed of the ad delivery backend. After the click event accumulation interference quantification is qualified, behavior synchronization interference quantification is performed to generate behavior synchronization interference quantification results. The determination of whether to optimize behavior synchronization interference based on these results helps reduce uplink traffic and make batch processing granularity finer, thereby ensuring the accuracy and timeliness of behavior synchronization. After the behavior synchronization interference quantification is qualified, ad delivery data feedback delay assessment is performed to generate ad delivery feedback delay results. The determination of whether to perform value weight qualification assessment based on these results helps improve the timeliness and accuracy of data feedback time information collection and analysis during ad delivery, thereby improving the timeliness of ad delivery backend allocation and solving the problem of low timeliness in ad delivery backend allocation in existing technologies.

[0053] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0054] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0055] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0056] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0057] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0058] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights, characterized in that, The method includes: The system evaluates the storage space interference during ad delivery to obtain the delivery storage result. If the delivery storage result is qualified, click event accumulation interference quantification, which reflects the impact of event accumulation on event processing speed, is not performed. Otherwise, the system determines whether to perform click event accumulation interference optimization based on the generated click event accumulation interference quantification result to reduce the interference of memory overflow caused by click event accumulation on ad delivery processing speed. The click event accumulation interference optimization includes thread number allocation and parallel call number setting. After the click event accumulation interference quantification is qualified, the latency of synchronizing the user behavior data corresponding to the click event to the server is quantified to reflect the interference on the feedback of the advertising data. Based on the obtained behavior synchronization interference quantification results, it is determined whether to perform behavior synchronization interference optimization to reduce the latency of synchronizing user behavior data to the server. The behavior synchronization interference optimization includes compression level optimization and time window duration setting. After the behavior synchronization interference quantification is qualified, the advertising delivery data feedback delay is evaluated to reflect the degree of impact of the advertising delivery data feedback delay on the real-time delivery effect. Based on the obtained advertising delivery feedback delay results, it is determined whether to conduct a value weight qualification evaluation to reflect the real-time performance when updating the value weight based on the Bandit algorithm. The specific process for the value weight eligibility assessment is as follows: Update the value weight combination based on the Bandit algorithm and obtain the corresponding value weight; The resource allocation delay value and the ad click count evaluation value are harmonized and averaged to obtain the qualified evaluation value of the value weight. The resource allocation delay value represents the result of weighting the preset resource allocation harmonization value by comparing it with the preset maximum resource allocation delay time and the resource allocation response time, and then superimposing it with the preset resource allocation constant used to ensure that the resource allocation delay value is non-zero. The resource allocation response time represents the time interval from receiving the ad request from the ad placement backend to completing resource allocation and issuing the ad placement instruction. The ad click count evaluation value represents the result of weighting the pre-set ad click harmonic value by comparing the ad click increase with the ad click increase during the previous specified delivery data feedback time period, and then adding it to the pre-set ad click constant used to ensure that the ad click count evaluation value is non-zero. The increase in ad clicks refers to the number of times the total number of clicks on the ad corresponding to the value weight obtained in the next adjacent specified delivery data feedback period is greater than the total number of clicks on the ad in the previous adjacent specified delivery data feedback period. Compare the qualified value of the value weight with the preset qualified value of the value weight; If the value weight qualification assessment value is greater than the preset value weight qualification assessment value, a qualified notification for the delivery strategy will be sent; otherwise, a qualified notification for the delivery strategy will be sent.

2. The delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights according to claim 1, characterized in that, The process of evaluating the interference of storage space during the ad delivery process to obtain the delivery storage results is as follows: By quantifying the ad delivery storage space utilization rate and the preset storage space utilization rate, which reflect the storage space usage of the ad delivery end during the ad delivery process, the ad delivery storage space interference value is obtained, which is used to reflect the strength of the impact of the ad delivery end's storage space usage on the click event accumulation rate. If the interference value of the deployed storage space is not greater than the preset interference value of the deployed storage space obtained from the database, the deployment storage result is recorded as qualified; otherwise, the deployment storage result is recorded as unqualified and click event accumulation interference quantification is performed.

3. The delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights according to claim 2, characterized in that, The specific process for quantifying click event stacking interference is as follows: The click event accumulation interference data, which reflects the click event accumulation situation, is harmonic averaged to obtain the click event accumulation interference quantification result. The click event accumulation interference data includes the request waiting time interference value, the click event accumulation interference value, and the unqualified delivery storage interference value. The request waiting time interference value represents the result of weighting the comparison between the request waiting time and the preset request waiting time by the preset request waiting time interference harmonic value, and superimposing it with the preset request waiting time constant used to ensure that the request waiting time interference value is non-zero. The request waiting time represents the total time for the ad request to reach the ad delivery backend and be responded to within the specified click event accumulation interference period. The click event accumulation interference value represents the result of weighting the comparison between the click event accumulation quantity and the preset click event accumulation quantity interference harmonic value, and superimposing it with the preset click event accumulation quantity constant used to ensure that the click event accumulation interference value is non-zero. The click event accumulation quantity represents the total number of click events to be processed contained in the queue used to store the events to be processed within the specified click event accumulation interference time period. The unqualified delivery storage interference value represents the result of weighting the delivery storage space interference value that is greater than the preset delivery storage space interference value by a preset delivery storage harmonic value, and then superimposing it with a preset delivery storage constant used to ensure that the unqualified delivery storage interference value is non-zero. If the click event accumulation interference quantification result is greater than the preset click event accumulation interference quantification result obtained from the database, click event accumulation interference optimization is performed; otherwise, behavior synchronization interference quantification is performed.

4. The delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights according to claim 3, characterized in that, The specific process for optimizing click event stacking interference is as follows: The thread allocation value obtained by mapping the click event accumulation interference quantification result and the number of concurrent requests into the thread allocation mapping set in the database is used as the number of threads to be allocated for the user and ad interaction data corresponding to the click event accumulation interference quantification result. The specific process for setting the number of parallel calls is as follows: input the click event accumulation interference quantification result and the number of concurrent requests into the parallel call number mapping set in the database to obtain the parallel call number adjustment ratio. Within the preset parallel call number adjustment range, the magnitude corresponding to the parallel call number adjustment ratio is used as the adjustment step size to gradually increase the number of parallel calls. During the click event backlog interference optimization process, if the newly acquired click event backlog interference quantification result for the next adjacent specified click event backlog interference time period is not greater than the preset click event backlog interference quantification result, the click event backlog interference optimization will stop, and behavior synchronization interference quantification will be performed. If, at the end of the click event backlog interference optimization process, the newly acquired click event backlog interference quantification result for the next adjacent specified click event backlog interference time period is still greater than the preset click event backlog interference quantification result, a click event backlog interference alarm will be sent.

5. The delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights according to claim 1, characterized in that, The specific process for determining whether to perform behavior synchronization interference optimization based on the obtained behavior synchronization interference quantification results is as follows: The behavior synchronization interference response value is obtained by harmonic averaging the maximum behavior synchronization delay interference value and the qualified click event accumulation interference result. This is used to quantify the risk of synchronization failure caused by the superposition of terminal storage pressure and click event accumulation during the user behavior data upload process, and thus reflects the delay of user behavior data synchronization to the server. If the behavior synchronization interference response value is greater than the preset behavior synchronization interference response value, behavior synchronization interference optimization is performed; otherwise, advertising data feedback delay assessment is performed. The maximum behavior synchronization delay interference value represents the result of weighting the comparison between the maximum behavior synchronization delay and the preset maximum synchronization delay by the preset synchronization delay harmonic value, and then adding it to the sum of the preset synchronization delay constant used to ensure that the maximum behavior synchronization delay interference value is non-zero. The maximum behavior synchronization delay is represented by the difference between the longest time it takes for all user behavior data to be synchronized to the server within a specified behavior synchronization period and the preset longest synchronization time. The qualified click event accumulation interference result represents the result of weighting a preset click event accumulation interference harmonic value (not greater than the preset click event accumulation interference quantization result) with the sum of the preset click event accumulation interference constant used to ensure that the qualified click event accumulation interference result is non-zero.

6. The delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights according to claim 5, characterized in that, The compression level optimization refers to adjusting the compression level based on an efficient algorithm to balance the behavior synchronization delay; After optimization of the compression level, the behavior synchronization interference optimization verification is carried out. Specifically, the user and advertisement interaction data are input into the multi-task learning model, and the output is used to quantify the independent probability and joint probability distribution of user behavior after the advertisement is exposed and reaches the user, thereby reflecting the predicted behavior probability of multiple behaviors. When the predicted behavior probability is within the preset behavior probability range, and the behavior synchronization interference response value re-acquired in the next adjacent specified behavior synchronization time period is not greater than the preset behavior synchronization interference response value, an evaluation of the delay in advertising data feedback is performed; otherwise, the time window duration is set.

7. The delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights according to claim 6, characterized in that, The time window duration setting is as follows: Input the behavior synchronization interference response value and the time window throughput into the time window duration mapping set in the database to obtain the preset time window duration adjustment value; Within the preset time window duration adjustment range, the time window duration is gradually reduced step by step, with the magnitude corresponding to the preset time window duration adjustment value as the adjustment step. After setting the time window duration, when the re-acquired predicted behavior probability is within the preset behavior probability range, and the behavior synchronization interference response value re-acquired in the next adjacent specified behavior synchronization time period is not greater than the preset behavior synchronization interference response value, an ad delivery data feedback delay assessment is performed; otherwise, a prediction anomaly prompt is sent.

8. The delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights according to claim 7, characterized in that, The specific process for evaluating the delay in advertising delivery data feedback is as follows: The advertising delivery data feedback delay value is obtained by harmonic averaging the delivery feedback data of the strategy to be harmonized. The delivery feedback data of the strategy to be harmonized includes the delivery delay feedback value of the strategy to be harmonized and the synchronization delay quantification value to be harmonized. The strategy delivery delay feedback value to be harmonized represents the result of weighting the strategy delivery feedback delay interference value and the preset strategy delivery feedback delay harmonization index. The strategy delivery feedback delay interference value represents the result of the ratio calculation between the time interval from the moment the advertisement is delivered to the first receipt of user feedback data related to the advertisement and the preset strategy delivery feedback duration. The quantized value of the synchronization delay to be harmonized is represented by the sum of the qualified behavior synchronization feedback value and the preset behavior synchronization feedback harmonization index after weighting. The qualified behavior synchronization feedback value is represented by the behavior synchronization interference response value that is not greater than the preset behavior synchronization interference response value.

9. The delivery strategy method based on multi-behavior probability modeling and dynamic optimization of value weights according to claim 8, characterized in that, The specific process for determining whether to conduct a value weight eligibility assessment based on the obtained advertising delivery feedback delay results is as follows: If the delay value of the ad delivery data feedback is greater than the preset ad delivery data feedback value obtained from the database, the ad delivery data reflecting the real-time ad delivery effect during the corresponding ad delivery process will be marked as unqualified ad delivery data and its value weight will be adjusted and optimized. Conversely, the corresponding ad delivery data will be marked as qualified ad delivery data and its value weight qualification will be evaluated. The value weight adjustment optimization is used to adjust the Bandit update frequency to improve the Bandit algorithm's responsiveness to ad delivery feedback delay and the accuracy of its update control over ad delivery feedback delay. The specific process of value weight adjustment and optimization is as follows: Map the advertising delivery data feedback delay value and the advertising delivery traffic input database Bandit update frequency mapping set to obtain a preset Bandit update frequency ratio, and use the amplitude corresponding to the preset Bandit update frequency ratio as the adjustment step size to gradually increase the Bandit update frequency. When it is detected that the delay value of the ad delivery data feedback in the next adjacent specified delivery data feedback time period is not greater than the preset ad delivery data feedback value, the value weight adjustment and optimization will be stopped, and the value weight qualification assessment will be carried out. If the Bandit update frequency reaches the preset maximum Bandit update frequency, and the delayed value of the ad delivery data feedback for the next adjacent specified delivery data feedback time period is still greater than the preset ad delivery data feedback value, an abnormal Bandit update prompt will be sent.

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