Real-time behavior monitoring and dynamic optimization system for advertisement placement strategy

Abstract

The application discloses a kind of real-time behavior monitoring and dynamic optimization system of advertisement delivery strategy, it is related to advertisement delivery and user behavior analysis technical field, real-time multi-source user behavior data acquisition module acquires data;Distributed storage and index module supports sharding storage, expansion and multidimensional index;Characteristic extraction analysis module extracts feature, identifies timing law;Delivery effect evaluation module compares data through index system, locates effect short board;User portrait dynamic update module adjusts label weight according to latest data;Strategy optimization decision module combines portrait and evaluation result and optimizes parameter;Execution scheduling module issues instruction and monitors state;History data mining and model training module mines law and iteratively trains model;Visual interactive configuration module provides interface, supports parameter configuration and data, effect and optimization suggestion display.The application realizes user behavior full-scene real-time monitoring and cross-platform identity association, and provides support for fine advertising marketing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of advertisement delivery and user behavior analysis, and particularly relates to a real-time behavior monitoring and dynamic optimization system for advertisement delivery strategy. BACKGROUND

[0002] With the rapid development of the digital marketing industry, advertisement delivery has shifted from traditional mass coverage to precise reach, and multi-platform and multi-scenario delivery modes have become mainstream. However, in the current advertisement delivery process, there are obvious shortcomings in the user behavior monitoring link. User behavior data on different platforms is stored in a scattered manner, the collection method is single and has high delay, and it is difficult to realize real-time data integration in all scenarios. At the same time, unstructured data such as user comments and advertisement screenshots lack processing capacity, and valuable information cannot be effectively extracted, resulting in a lack of completeness of behavior data. The problem of fragmentation of user identity on multiple platforms is prominent, and there is a lack of unified identification association mechanism, which causes the user cross-platform behavior track to be broken and cannot form a coherent behavior analysis, directly affecting the pertinence of the delivery strategy. In addition, the data cleaning and desensitization process is not perfect, which not only has the problem of repeated data and error data interfering with the analysis results, but also is difficult to guarantee the safety of user privacy, further restricting the usability of monitoring data.

[0003] The lack of static and adaptability of advertisement delivery strategy is another core pain point. Existing delivery strategies are mostly based on fixed parameters configured initially, and lack a mechanism for dynamic adjustment according to real-time user behavior and delivery effect. User interest preferences and purchase intentions change rapidly over time and with scenarios, and static strategies cannot respond to these changes in a timely manner, resulting in a mismatch between advertisement reach and user needs. Advertisement creatives lack personalized adaptation capabilities and cannot flexibly adjust content and display forms according to user interest tags and device information, and there is a lack of creative layout optimization for different screen size devices, reducing the attractiveness of advertisements to target users. In terms of audience targeting, the expansion of seed users lacks scientific algorithm support and is prone to problems of too narrow or too wide targeting, either missing potential users or causing precision to decline, wasting delivery resources.

[0004] The closed loop of effect evaluation and strategy optimization has not been formed. The current evaluation system focuses on basic indicators such as exposure and click, and pays insufficient attention to deep value indicators such as conversion quality and user life cycle value, so it is difficult to fully reflect the actual benefit of advertisement delivery. The user portrait is updated with a lag, the weight of historical behavior tags is not dynamically adjusted over time, and the tags of users who have not produced new behaviors for a long time still maintain the initial weight, which cannot accurately reflect the current state of the users. Strategy optimization relies on manual experience, historical data mining is insufficient, machine learning model training lacks continuous actual data iteration, and the prediction accuracy is limited, resulting in strong subjectivity and insufficient scientificity of optimization decisions. At the same time, there is a lack of convenient visualization tools, and users cannot intuitively grasp the delivery dynamics and optimization direction, and the manual adjustment process is cumbersome, further affecting the delivery efficiency and effect. These problems together lead to serious waste of advertising delivery resources, low input-output ratio, and difficulty in meeting the needs of advertisers for fine marketing. SUMMARY

[0005] The real-time behavior monitoring and dynamic optimization system of the advertisement delivery strategy is proposed to solve the problems mentioned in the prior art.

[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: a real-time behavior monitoring and dynamic optimization system of an advertisement delivery strategy, comprising the following modules:

[0007] A real-time multi-source user behavior data acquisition module acquires real-time data of user interaction with advertisements on different platforms through the methods of SDK, API interface call, and log capture, cleans, desensitizes, and structures the acquired data;

[0008] A distributed real-time data storage and indexing module adopts a distributed database cluster architecture, supports data sharding storage and dynamic expansion, establishes multi-dimensional indexes based on user identification, timestamp, and advertisement ID, and sets a data backup mechanism;

[0009] A real-time behavior feature extraction and analysis module extracts behavior features from user data, identifies behavior timing rules, and accurately calculates user interest intensity to provide a basis for subsequent decision-making;

[0010] An advertisement delivery effect real-time evaluation module sets an evaluation index system, calculates real-time values of indicators based on real-time user behavior data, compares and analyzes deviation rates with preset target values, and locates channels, creatives, or people with poor delivery effects;

[0011] A user portrait dynamic updating module adjusts portrait tag weights and purchase intention levels based on the latest user behavior and delivery effect data to maintain the accuracy and timeliness of the user portrait;

[0012] The putting strategy dynamic optimization decision module combines the user portrait and the effect evaluation result to intelligently optimize the crowd targeting, advertisement bidding, channel distribution and budget configuration, and generates strategy parameters;

[0013] The advertisement putting strategy execution and scheduling module converts the optimized strategy parameters into putting instructions, and issues the putting instructions to each platform through an API interface. The execution state is monitored in real time, and when the deviation exceeds the threshold, the instructions are automatically adjusted or an alarm is triggered, and the strategy is executed according to the optimization target;

[0014] The historical data mining and strategy model training module stores historical data, mines the effect rules and trains the optimization model, and iteratively updates the model through actual data to continuously improve the prediction accuracy of the model;

[0015] The visual interaction and strategy configuration module provides a visual interface for users to configure the advertisement putting basic parameters, target indicators and strategy optimization rules, and real-time displays user behavior data statistics, putting effect dynamic curve and optimization suggestions.

[0016] Further, it further includes a cross-platform user identity recognition module, which constructs a user unique identification mapping relationship by collecting user device fingerprints, login accounts, behavior habit characteristics on each platform, calculates the similarity of the behavior characteristics of the user when the user generates behavior on a new platform and the existing user identification, and associates the user identification with a similarity exceeding a preset threshold, to form a unified user global identification.

[0017] Further, it further includes an advertisement creative dynamic generation and adaptation module, which dynamically generates or adjusts advertisement creative content according to the interest tags in the user portrait and the device information, optimizes the creative display layout for devices with different screen sizes, and selects creative element combinations based on user interaction data on historical creatives using A / B testing methods.

[0018] Further, in the real-time multi-source user behavior data collection module, natural language processing and image recognition technologies are used for the conversion of unstructured data. For user comment text data, keywords and sentiment tendencies are extracted; for advertisement screenshot image data, objects, scenes and text information in the image are recognized and converted into structured labels. At the same time, the real-time index of data collection is calculated. The calculation method of the real-time index R is , wherein is the behavior occurrence time, is the data storage completion time, is the preset maximum allowed delay time.

[0019] Further, in the real-time behavior feature extraction and analysis module, the calculation of user interest intensity combines the user behavior type and behavior time, and uses a weighted calculation method. The calculation method of the interest intensity I is , wherein is the i-th behavior type, and The weighting of different behavior types is as follows: click behavior has a higher weighting than browsing behavior. For the first The frequency of this behavior For the first The time interval from the occurrence of this behavior to the present. This is the attenuation coefficient.

[0020] Furthermore, the real-time evaluation module for advertising performance establishes a tiered evaluation system. The first tier is the basic performance tier, which includes impressions and clicks; the second tier is the conversion performance tier, which includes click-through rate and conversion cost; and the third tier is the value performance tier, which includes return on investment and user lifetime value. Weights are assigned to the indicators at each tier, and a comprehensive performance score is calculated.

[0021] Furthermore, in the user profile dynamic update module, the update of user purchase intention tags combines the user's browsing depth, add-to-cart behavior, inquiry behavior and historical purchase records to set intention scores corresponding to different behaviors. When the accumulated intention score reaches a preset threshold, the purchase intention level is increased. When the user makes a purchase, the intention score is reset and the purchase preference is recorded.

[0022] Furthermore, in the dynamic optimization decision-making module of the delivery strategy, the target audience targeting optimization adopts the audience expansion algorithm. The initial target audience is seed users. The feature similarity between potential users and seed users is calculated, and users with high similarity and not yet covered are selected as the expanded audience. At the same time, an upper limit for the expansion ratio is set, and the delivery effect data of the expanded audience is fed back to the system in real time.

[0023] Furthermore, the ad placement strategy execution and scheduling module is equipped with a multi-level fault tolerance mechanism. When an ad platform API call fails, it automatically switches to a backup API. When the number of consecutive API call failures exceeds a preset value, it suspends the issuance of new instructions to that platform and initiates a manual intervention process. During strategy execution, the traffic acquisition cost of each platform is calculated in real time. When the cost exceeds a preset limit, the placement ratio of that platform is automatically reduced.

[0024] Furthermore, in the historical data mining and strategy model training module, a time series prediction algorithm is used to predict advertising traffic within a preset time period in the future. The calculation takes into account historical traffic volume, trend factors, and the impact of holidays. The calculation method is as follows: ,in This is the average flow rate for the same period in history. The trend growth rate For time intervals, This represents the impact coefficient of holidays.

[0025] Compared with existing technologies, the beneficial effects of this invention are:

[0026] The real-time multi-source user behavior data acquisition module integrates various acquisition methods, covering user behavior data across all scenarios. It efficiently processes structured and unstructured data, quickly completing data cleaning, de-identification, and format conversion to ensure data integrity, timeliness, and security, providing high-quality data support for subsequent analysis and decision-making. The distributed storage and indexing module enables fast data reading, writing, and related retrieval, supports dynamic system expansion, meets the needs of large-scale data storage and access, and provides stable underlying support for real-time monitoring.

[0027] The cross-platform user identification module establishes a unified global user identifier, effectively linking user identities across different platforms, resolving the issue of fragmented identities, and making cross-platform user behavior analysis more coherent. This lays the foundation for accurately depicting user behavior trajectories and interest preferences. The real-time behavioral feature extraction and analysis module deeply mines the temporal patterns and interest tendencies of user behavior, accurately identifying user interest tags and their intensity, enabling advertising to precisely match user needs and improve reach effectiveness.

[0028] The ad creative dynamic generation and adaptation module flexibly adjusts creative content and display layout based on user profiles and device information. By scientifically selecting the optimal creative combination, it enhances the appeal of ads to target users and improves ad interaction conversion rates. The user profile dynamic update module adjusts tag weights in real time based on the latest user behavior data, and reasonably decays tags that have not been updated for a long time, ensuring that user profiles accurately reflect the user's current status and providing precise basis for strategy optimization.

[0029] The real-time advertising performance evaluation module constructs a tiered evaluation system covering multiple dimensions such as basic performance, conversion performance, and value performance. This comprehensively and objectively reflects advertising performance, accurately pinpoints areas of underperformance, and provides clear direction for strategy adjustments. The dynamic optimization decision-making module for advertising strategies, based on real-time data and dynamic user profiles, automatically generates or adjusts advertising parameters. This enables intelligent optimization of strategies such as target audience targeting, bidding, and channel allocation, allowing advertising strategies to quickly adapt to market changes and user needs, avoiding resource waste.

[0030] The historical data mining and strategy model training module fully utilizes historical data to continuously iterate and optimize model parameters, improving the accuracy of strategy predictions and making optimization decisions more scientific and forward-looking. The visualization and strategy configuration module provides an intuitive user interface and data display, allowing users to flexibly configure parameters and adjust strategies, lowering the operational threshold and improving campaign management efficiency.

[0031] Overall, the present application realizes the whole process closed loop from user behavior monitoring, data processing, analysis and evaluation to strategy optimization, execution feedback, greatly improves the precision and timeliness of advertisement delivery, optimizes the resource allocation efficiency, reduces the invalid delivery consumption, improves the input-output ratio of advertisement delivery, and provides efficient and intelligent fine marketing solution for advertisers. BRIEF DESCRIPTION OF DRAWINGS

[0032] Fig. 1 The schematic block diagram of the real-time behavior monitoring and dynamic optimization system of the advertisement delivery strategy proposed by the present application is shown in the figure.

[0033] Fig. 2 The real-time click-through rate time series line chart for advertisement delivery is shown in the figure.

[0034] Fig. 3 The conversion effect comparison column chart for each delivery channel is shown in the figure. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0036] In the description of the present application, it should be understood that the orientations or positional relationships indicated by the terms “center”, “longitudinal”, “transverse”, “length”, “width”, “thickness”, “upper”, “lower”, “front”, “rear”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inner”, “outer”, “clockwise”, “counterclockwise” and the like are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0037] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0038] Reference Figs. 1 to 3 A real-time behavior monitoring and dynamic optimization system for advertising delivery strategies, comprising the following modules:

[0039] The real-time multi-source user behavior data collection module is used to collect real-time data on user interactions with advertisements on different platforms. The data types cover user identifiers, behavior types, behavior occurrence times, behavior scenarios, device information, and advertisement attributes. The collection methods include event tracking SDK, API interface calls, and real-time capture of log files. At the same time, the collected data is cleaned to remove duplicate data and data with incorrect formats, sensitive information is de-identified, and unstructured data is converted into structured data. The data collection latency is controlled within 1 second.

[0040] The distributed real-time data storage and indexing module is used to store user behavior data processed by the acquisition module. It adopts a distributed database cluster architecture, supports data sharding and dynamic expansion, and establishes multi-dimensional indexes based on user identifiers, timestamps, and ad IDs to enable fast data query and related retrieval. At the same time, a data backup mechanism is set up to ensure the security and integrity of data storage, and the data read and write response time is controlled within 500 milliseconds.

[0041] The real-time behavior feature extraction and analysis module is used to extract user behavior features from stored user behavior data. The features include user basic attribute features, behavior sequence features, interest preference features and advertising interaction features. The sliding window algorithm is used to calculate the frequency, interval and conversion rate of user behavior in a preset time window in real time. The sequence pattern mining algorithm is used to identify the temporal pattern of user behavior. The user interest tags and interest intensity are determined based on the user's interaction data with different types of advertisements.

[0042] The advertisement delivery effect real-time evaluation module is configured to evaluate the effect of advertisement delivery in real time, set an evaluation index system, including exposure volume, click volume, click conversion rate, conversion cost, input-output ratio and user retention rate, calculate the real-time value of each index based on real-time collected user behavior data, compare the real-time value with a preset target value, analyze the index deviation rate, and locate the advertisement channel, advertisement creative or target population with poor delivery effect;

[0043] The user portrait dynamic updating module is configured to dynamically update the user portrait based on real-time extracted user behavior features and advertisement delivery effect data. The user portrait includes user demographic attributes, consumption ability, interest preference, purchase intention and advertisement sensitivity. The weight of each label in the portrait is adjusted according to the latest behavior data of the user. For users who have not produced new behaviors for a long time, the weight of their historical behavior labels is reduced according to a preset decay rule. The timeliness and accuracy of the user portrait are maintained in this way.

[0044] The delivery strategy dynamic optimization decision module is configured to generate or adjust the advertisement delivery strategy according to the advertisement delivery effect evaluation result and the dynamically updated user portrait. The strategy content includes target population targeting, advertisement bidding, advertisement creative selection, delivery channel distribution and budget allocation. A multi-objective optimization algorithm is used to improve the input-output ratio, reduce the conversion cost and improve the target population coverage rate as the optimization target. The optimal delivery strategy parameters are generated in combination with the real-time bidding rules of the advertisement platform and the traffic prediction data.

[0045] The advertisement delivery strategy execution and scheduling module is configured to convert the optimized delivery strategy parameters into delivery instructions recognizable by the advertisement platform, and deliver them to each advertisement delivery platform in real time through an API interface. The execution status of the strategy is monitored in real time, including the success rate of instruction delivery, the deviation of actual advertisement exposure volume and planned exposure volume. When the deviation exceeds a preset threshold, the delivery instruction is automatically adjusted or the alarm mechanism is triggered. The strategy is executed according to the optimization target.

[0046] The historical data mining and strategy model training module is configured to store historical user behavior data, advertisement delivery strategies and corresponding effect data. Machine learning algorithms are used to mine historical data, analyze the effect rules of different delivery strategies in different scenarios, train strategy optimization models, and output predicted delivery effect indicators. The model input is user portrait data, advertisement attribute data and environment data. The model parameters are continuously iterated and optimized through newly generated actual data to improve the prediction accuracy of the model.

[0047] The visual interaction and strategy configuration module is configured to provide a visual interface for a user to configure basic parameters, target indicators and strategy optimization rules of advertisement delivery, display user behavior data statistics results, advertisement delivery effect dynamic curves and strategy optimization suggestions in real time, support the user to manually adjust delivery strategy parameters, set trigger conditions and frequency of strategy automatic optimization, and provide a data report export function.

[0048] In the application, the cross-platform user identity recognition module is further included to realize association and matching of user identities of different platforms, to construct a user unique identification mapping relationship by collecting device fingerprints, login accounts and behavior habit features of the user on each platform, to calculate similarity of behavior features of the user when the user generates a behavior on a new platform and an existing user identification, to associate user identifications with similarity exceeding a preset threshold, and to form a unified user global identification, so as to solve the problem of fragmentation of user identities on multiple platforms and to improve coherence and accuracy of user behavior analysis.

[0049] In the application, the advertisement creative dynamic generation and adaptation module is further included to dynamically generate or adjust advertisement creative content according to interest labels and device information in a user portrait, to support automatic combination of multiple creative forms such as text, picture and video, to optimize creative display layout for devices with different screen sizes, to select an optimal creative element combination by using an A / B test method based on user interaction data on historical creatives, and to improve the attractiveness of advertisements to target users.

[0050] In the application, in the real-time multi-source user behavior data collection module, natural language processing and image recognition technologies are used for conversion of unstructured data, keywords and emotional tendencies are extracted for user comment text data, objects, scenes and text information in advertisement screenshot image data are recognized, and the data are converted into structured labels, and a real-time index of data collection is calculated, the calculation method of the real-time index R is , wherein is a behavior occurrence time, is a data storage completion time, is a preset maximum allowable delay time, and the value closer to 1 indicates better real-time performance.

[0051] In the application, in the real-time behavior feature extraction and analysis module, the calculation of user interest intensity combines a user behavior type and a behavior time, and a weighted calculation method is used, the calculation method of the interest intensity I is , wherein is a weight of the th behavior type, the weight of a click behavior is higher than that of a browsing behavior, is a frequency of the th behavior, is a weight of the a time interval from a behavior occurrence to the present, is a decay coefficient, in this way, the recent behavior has a greater impact on the interest intensity, and the long-term behavior is attenuated over time.

[0052] In the present application, in the real-time evaluation module of the advertising effect, a hierarchical evaluation system is established, the first layer is the basic effect layer, including exposure and click volume; the second layer is the conversion effect layer, including click conversion rate and conversion cost; the third layer is the value effect layer, including input-output ratio and user life cycle value, the weight of each layer index is set, the comprehensive effect score is calculated, and the comprehensive effect score is used for horizontal comparison of the overall performance of different advertising campaigns.

[0053] In the present application, in the user portrait dynamic updating module, for the update of the user purchase intention label, the user's browsing depth, adding behavior, consulting behavior and historical purchase record are combined, and the intention score corresponding to different behaviors is set, when the cumulative intention score reaches the preset threshold, the purchase intention level is improved, and when the user generates a purchase behavior, the intention score is reset and the purchase preference is recorded, the purchase intention label accurately reflects the current purchase possibility of the user through this way.

[0054] In the present application, in the dynamic optimization decision module of the advertising strategy, the target population orientation optimization adopts a population expansion algorithm, takes the initial target population as a seed user, calculates the feature similarity between potential users and the seed user, selects users with high similarity and not covered as an expanded population, and sets an upper limit for the expansion ratio to prevent the population orientation from being too broad and causing a decrease in precision, and the delivery effect data of the expanded population is fed back to the system in real time, for adjusting the subsequent expansion strategy.

[0055] In the present application, in the advertising strategy execution and scheduling module, a multi-level fault tolerance mechanism is set, when a certain advertising platform API interface call fails, it is automatically switched to a backup interface; when the number of consecutive interface calls exceeds the preset value, the new instructions are suspended to the platform and the manual intervention process is started; in the process of strategy execution, the traffic acquisition cost of each platform is calculated in real time, when the cost exceeds the preset upper limit, the delivery ratio of the platform is automatically reduced, to ensure the stability and economy of the advertising delivery.

[0056] In the present application, in the historical data mining and strategy model training module, a time series prediction algorithm is used to predict the advertising traffic in a future preset time period, and the predicted traffic is calculated by considering historical same period traffic, trend factor and holiday influence, and the calculation method is , wherein is the historical same period average traffic, is the trend growth rate, is the time interval, is a holiday influence coefficient, during a holiday Value greater than 1, non-holiday Value is 1, through the accurate flow prediction for budget allocation and bidding strategy optimization to provide data support.

[0057] The specific embodiments of the system are further illustrated by two embodiments as follows:

[0058] Example 1: Real-time optimization of advertising during e-commerce platform promotion

[0059] This embodiment is aimed at the annual promotion activities of a certain comprehensive e-commerce platform. The platform covers APP, small program and web terminal, involving 12 categories such as clothing, home appliances and food, with more than 50 million daily active users. During the promotion, three types of advertisements, including brand advertisements, category promotion advertisements and shop traffic advertisements, are planned to be launched. The system needs to realize real-time monitoring of user behavior and dynamic optimization of launching strategy to improve the conversion efficiency of advertisements and the input-output ratio. The system deployment adopts a distributed architecture, including 8 data collection servers, 12 distributed database servers and 6 computing node servers, supporting 200 online operations at the same time, adapting to PC end operation background, mobile terminal monitoring APP and large screen command center.

[0060] The real-time multi-source user behavior data collection module starts full-scene data collection. The data types cover user identification, including device ID and mobile phone hash value; the behavior types include browsing goods, clicking advertisements, adding shopping carts, submitting orders and canceling orders; the behavior occurrence time is accurate to milliseconds; the behavior scene includes APP home page, goods detail page, shopping cart page and settlement page; the device information includes mobile phone model, operating system, screen size and network type; the advertisement attributes include advertisement ID, advertisement type, promotion category and landing page link. The collection methods include deploying a buried point SDK on APP, small program and web terminal to capture user interaction behavior in real time; through API interface calling, the e-commerce platform transaction system, payment system and logistics system are connected to synchronize order data and payment status; real-time server log files are extracted to extract user access track data. In the data cleaning process, the repeated data detection algorithm is used to remove the repeated reporting data of the same user and the same behavior, and the format verification tool is used to filter the data with missing fields and format errors; sensitive information desensitization adopts mobile phone hash and device ID encryption processing, and unstructured data conversion adopts natural language processing technology to extract keywords and sentiment tendency in user comments, and image recognition technology to identify product models and promotion texts in product advertisement screenshots, and converts them into structured labels. The real-time index calculation adopts the formula , wherein is the behavior occurrence time of user clicking the advertisement, is the data storage completion time, is preset to 1 second, and - = 0.3 seconds, calculated = 1 - 0.3 / 1 = 0.7, meeting the real-time requirements. The data acquisition delay is controlled within 1 second, ensuring the timeliness of subsequent analysis and optimization.

[0061] The distributed real-time data storage and indexing module receives the processed data, adopts a distributed database cluster architecture, and stores the data in shards according to user identification hash values, with a total of 100 data shards, each corresponding to an independent database node and supporting dynamic expansion. Based on user identification, timestamp, and advertisement ID, a joint index is established, and auxiliary indexes are established for behavior types and behavior scenarios, enabling multi-dimensional data query. Data backup adopts a multi-active mechanism, with real-time synchronization of main node data to two backup nodes, automatic switching of backup nodes in the event of main node failure, ensuring data storage security and integrity. The data read-write response time is controlled within 500 milliseconds, and when an operator queries user behavior data within 24 hours for a certain advertisement, the response time is only 300 milliseconds, significantly improving operational efficiency.

[0062] The real-time behavior feature extraction and analysis module extracts user behavior features from stored data. User basic attribute features include age, gender, region, and consumption level; behavior sequence features include behavior trajectory and sequence within the last 1 hour; interest preference features include preferred categories, price sensitivity, and brand inclination; and advertisement interaction features include advertisement click frequency, dwell time, and landing page browsing depth. A 1-hour time window is set using a sliding window algorithm to calculate user behavior frequency, behavior interval, and behavior conversion rate in real time. For example, a user views home appliance advertisements 3 times and clicks on them 2 times within 1 hour, with a behavior conversion rate of 66.7%. The SPADE sequence pattern mining algorithm is used to identify user behavior timing rules, finding that users have a higher probability of submitting orders within 30 minutes after clicking on home appliance advertisements. The user interest intensity calculation uses the formula , with a click behavior weight = 2, a browse behavior weight = 1, an add-to-cart behavior weight = 3, a purchase behavior weight = 5, a decay coefficient = 0.1, a user clicks on a home appliance advertisement 2 hours ago, = 2, = 2, adds to cart 1 hour ago, = 1, = 1, resulting in I = 2 × 2 × e^(-0.1 × 2) + 3 × 1 × e^(-0.1 × 1) = 4 × 0.8187 + 3 × 0.9048 = 3.2748 + 2.7144 = 5.9892, indicating that the user has a high interest intensity in home appliance advertisements.

[0063] The advertisement delivery effect real-time evaluation module establishes a hierarchical evaluation system. The basic effect layer includes exposure and click volume, with a weight of 0.3; the conversion effect layer includes click conversion rate and conversion cost, with a weight of 0.4; and the value effect layer includes input-output ratio and user life cycle value, with a weight of 0.3. Based on real-time data, each index is calculated, for example, a certain household appliance brand advertisement has an exposure of 100,000 within 2 hours, a click volume of 8,000, a click conversion rate of 8%, a conversion cost of 150 yuan, and an input-output ratio of 2.5. Compare the real-time value with the preset target value, the click conversion rate target is 6%, the deviation rate is 33.3%, the input-output ratio target is 2.0, the deviation rate is 25%, and the positioning of the advertisement delivery effect is good; another food advertisement click conversion rate is 3%, which is lower than the target of 5%, the deviation rate is 40%, and the positioning of the delivery effect is poor, and the target population and advertisement creative need to be optimized.

[0064] The user portrait dynamic updating module updates the user portrait based on the extracted features and effect data. The user portrait includes demographic attributes such as age 35, gender male, region first-tier city, consumption ability medium-high, interest preference home appliances and digital, purchase intention high, and advertisement sensitivity medium. According to the user's latest behavior of adding a home appliance to the shopping cart, the weight of the home appliance category interest label is increased, and the weight of the long-term non-interaction clothing category label is reduced. A certain user has not generated new behavior for 30 days, according to the preset decay rule, the historical behavior label weight is reduced by 50%, to ensure that the portrait can reflect the current state of the user. For the purchase intention label, combined with the user's 10-minute stay in the deep product detail page, the add-to-cart behavior, and the customer service record, set the deep browsing score to 5, the add-to-cart score to 10, and the consultation score to 15. The cumulative intention score is 30, which reaches the preset threshold of 25, and the purchase intention level is increased to "high intention". When the user completes the purchase, the intention score is reset to 0, and the purchased home appliance model and price are recorded to update the purchase preference label.

[0065] The cross-platform user identity recognition module realizes the association of the identities of three terminal users. Collect the device fingerprint, login mobile phone number in APP, WeChat open platform ID in mini program, and Cookie information in web page, and build a mapping relationship of user unique identifier. A user logs in with a mobile phone number in APP and uses the same WeChat account to log in to a mini program. The system calculates the similarity of the behavior characteristics of the two, including interest categories, browsing time periods, and purchase habits. The similarity reaches 85%, which exceeds the preset threshold of 80%. The two are associated to form a unified global identifier, realizing coherent analysis of cross-terminal behavior tracks, discovering that the user browses home appliance advertisements on the APP and completes purchases through the mini program, and providing a basis for cross-platform delivery strategy optimization.

[0066] The advertisement creative dynamic generation and adaptation module generates personalized creatives according to the user portrait. For high-interest intensity home appliance users, text creatives containing their added models and time-limited discount information are generated, and product detail pictures are matched. For devices with different screen sizes, the creative display layout is optimized. For mobile phones, vertical pictures and simple copy are used, and for tablets, horizontal pictures and detailed parameters are used. Based on user historical interaction data, three groups of creative schemes are generated simultaneously using A / B testing methods, showing discount strength, product function, and user evaluation. Through real-time monitoring of click-through rates, the highest "discount strength + product function" combination creative is selected to improve the attractiveness of the advertisement.

[0067] The delivery strategy dynamic optimization decision module optimizes the strategy based on the evaluation results and the updated portrait. The target audience targeting uses a crowd expansion algorithm, taking high-conversion home appliance users as seed users, calculating the feature similarity between potential users and seed users, selecting the top 30% of users with similar degrees and not covered as an expanded crowd, and setting an upper limit of 50% for the expansion ratio to avoid over-wide targeting. The advertisement bid combines real-time bidding rules, with a 20% increase in bid for high-intention users and a maintenance of the base bid for ordinary users. The distribution of delivery channels is adjusted according to the conversion effect of each terminal, with a 60% budget allocated to the APP end, 30% to the mini program, and 10% to the web end. The budget allocation is tilted towards home appliances and digital products, accounting for 70% of the total budget. The multi-objective optimization algorithm aims to improve the input-output ratio, reduce conversion costs, and improve target audience coverage, combining with traffic prediction data to generate optimal parameters. Traffic prediction uses the formula where is the average traffic of home appliance category advertisements during the same period last year, is the trend growth rate of 0.2, is the time interval of 1 year, is the holiday impact coefficient of 1.5, and the big promotion belongs to the holiday category, resulting in =100×(1+0.2×1)×1.5=1.8 million, providing data support for budget allocation.

[0068] The advertisement delivery strategy execution and scheduling module converts the optimized parameters into delivery instructions and delivers them to the three terminals and third-party advertising platforms in real time through API interfaces. Real-time monitoring of instruction delivery success rate ensures 99.9% success rate. When a third-party platform interface call fails, it automatically switches to a backup interface. If it fails continuously for three times, it suspends the delivery of new instructions and triggers an alarm to notify the operator to intervene. Monitor the deviation between actual and planned exposure of the advertisement. When the deviation exceeds 10%, automatically adjust the delivery instructions to increase the exposure of high-conversion channels. Real-time calculation of the cost of traffic acquisition on each platform. When the cost of the web end exceeds the preset upper limit, automatically reduce its delivery proportion by 5% to ensure the stability and economy of delivery.

[0069] The historical data mining and strategy model training module stores user behavior data, delivery strategy and effect data for nearly 3 years, with a total amount of 50 TB. The random forest algorithm is used to mine the historical data, analyze the effect rules of different bidding strategies and creative types in different user groups, and train the strategy optimization model. The model input is user portrait data, ad attribute data, environment data period, weather, and the output is predicted click conversion rate and conversion cost. The model parameters are iteratively optimized through newly generated actual data, and the model is updated every 24 hours to improve the prediction accuracy.

[0070] The visual interaction and strategy configuration module provides a visual interface, and the operation personnel configures the basic parameter delivery period, budget upper limit and target index of the large promotion advertisement, sets the trigger condition of strategy automatic optimization, triggers optimization when the conversion cost exceeds the target by 20%, and the optimization frequency is once an hour. The interface real-time displays user behavior data statistics results user regional distribution, behavior type proportion, and advertisement delivery effect dynamic curve exposure, click time series change, and strategy optimization suggestion adjusts the target audience of a certain food advertisement to 25-35 year-old women. Export data report is supported, including real-time value, deviation rate, optimization record of each index, and convenient for operation personnel to review and analyze.

[0071] Table 1 Comparison of effects before and after optimization of large promotion advertisement delivery of e-commerce platform

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[0073] Table 1 clearly presents the optimization effect of the system. Before optimization, due to high user behavior monitoring delay and data fragmentation, the advertisement delivery lacks targeting, and the click conversion rate is only 4.2%, the conversion cost is as high as 210 yuan, and the input-output ratio is 1.8. After optimization, through real-time multi-source data collection and cross-platform identity recognition, user behavior is monitored in all scenarios and continuously, combined with dynamically updated user portraits and personalized creative generation, the advertisement accurately reaches the target users, the click conversion rate is improved to 8.5%; the delivery strategy is dynamically optimized based on real-time effect data, the budget is tilted to high conversion channels and categories, the conversion cost is reduced to 135 yuan, and the input-output ratio is improved to 2.9; the user retention rate is improved from 28% to 45%, benefiting from the good user experience brought by strategy optimization and reducing invalid advertisement interference; the cross-platform behavior continuity is improved from 62% to 91%, solving the problem of identity fragmentation and realizing cross-terminal accurate delivery. The data fully verifies the significant value of the system in improving advertisement conversion efficiency, optimizing resource allocation and improving user experience.

[0074] Example two: real-time optimization application of content platform information flow advertisement delivery

[0075] This embodiment is aimed at a certain comprehensive content platform, which covers information, short video, live broadcast and other content forms, and the users are mainly young groups with more than 80 million daily active users. It plans to put information stream ads of three categories of information stream ads, such as beauty, digital and fast-moving consumer goods, and needs to realize real-time behavior monitoring and dynamic optimization through the system to solve the problems of insufficient precision of information stream ads and high user resistance. The system deployment adopts a distributed architecture, including 10 data collection servers, 15 distributed database servers and 8 computing node servers, supporting 300 online operations at the same time, and adapting to PC-side operation platform, mobile-side management APP and data large screen.

[0076] The real-time multi-source user behavior data collection module collects all-scene data. The data types include user identification, including device ID and account ID; the behavior types include browsing information, watching short video, clicking on ads, commenting and interacting, sharing and forwarding; the behavior occurrence time; the behavior scene includes the home page information stream, short video detail page and live room; the device information includes terminal type, operating system, screen resolution and network status; the ad attributes include ad ID, ad type, promoted product and jump link. The collection methods include deploying a buried point SDK in each board of the content platform to capture user interaction behavior in real time; API interface docking platform content management system and user management system to synchronize content consumption data and user account information; real-time server access log extraction to extract user browsing track. In the data cleaning process, the repeated reporting of click behavior data is removed, and the format error of device information data is filtered; sensitive information desensitization adopts account ID hash processing, and unstructured data conversion extracts product feedback keywords and sentiment tendency in user comments through natural language processing, and image recognition technology identifies product features in ad pictures and converts them into structured labels. In the calculation of real-time index R, t1 is the time when the user clicks on the ad while watching the short video, t2 is the data storage completion time, T=1 second, and the collection t2-t1=0.2 seconds, R=1-0.2 / 1=0.8, which meets the real-time requirement, and the data collection delay is controlled within 1 second.

[0077] The distributed real-time data storage and indexing module adopts a distributed database cluster architecture, divides 120 data shards according to user account ID hash values, and supports dynamic expansion for each shard corresponding to an independent database node. Based on user identification, timestamp and ad ID, the main index is established, and the behavior type, behavior scene and ad type are established as auxiliary index to realize multi-dimensional data fast retrieval. The data backup adopts a three-active mechanism in different places, and the main node data is synchronized to three standby nodes in real time to ensure data security and integrity. The data read-write response time is controlled within 500 milliseconds, and the response time of the operation personnel to query the real-time click data of a certain beauty ad is only 250 milliseconds, meeting the real-time operation requirements.

[0078] The real-time behavior feature extraction and analysis module extracts user behavior features. User basic attribute features include age, gender, interest label, and consumption ability; behavior sequence features include content consumption order and advertisement interaction order in the last 30 minutes; interest preference features include preferred content type, attention brand, and consumption willingness; and advertisement interaction features include advertisement click frequency, dwell time, and jump completion rate. A 30-minute time window is set by the sliding window algorithm to calculate user behavior frequency, interval, and conversion rate. A user browses 5 makeup information streams in 30 minutes, clicks on 3 advertisements, and has a 60% conversion rate. The SPADE algorithm is used to identify the behavior timing rules, and it is found that the probability of a user clicking on related advertisements after watching makeup short videos is relatively high. The user interest intensity calculation uses the formula , with a click weight of 2, a browsing weight of 1, a comment interaction weight of 3, a sharing and forwarding weight of 4, and a decay coefficient = 0.08. A user clicked on a makeup advertisement 1.5 hours ago, = 2, = 1.5, commented on an advertisement 30 minutes ago, = 1, = 0.5, and the calculation result is = 2 × 2 × e^(-0.08 × 1.5) + 3 × 1 × e^(-0.08 × 0.5) = 4 × 0.8869 + 3 × 0.9608 = 3.5476 + 2.8824 = 6.43, indicating that the user has a high interest intensity in makeup advertisements.

[0079] The real-time evaluation module of the advertisement delivery effect establishes a hierarchical evaluation system. The basic effect layer has an exposure and click weight of 0.3, the conversion effect layer has a click conversion rate and conversion cost weight of 0.4, and the value effect layer has an input-output ratio and user life cycle value weight of 0.3. Real-time calculation of each index, a certain makeup advertisement has an exposure of 150,000, a click volume of 13,500, a conversion rate of 9%, a conversion cost of 120 yuan, and an input-output ratio of 2.7 in 3 hours. Compared with the preset target, the conversion rate target is 7%, the deviation rate is 28.6%, the input-output ratio target is 2.2, the deviation rate is 22.7%, and the delivery effect is good; the conversion rate of a certain digital advertisement is 4%, which is lower than the target of 6%, the deviation rate is 33.3%, and the positioning needs to be optimized to target the target audience and advertisement creativity.

[0080] The user portrait dynamic updating module updates the portrait based on the feature and effect data. The user portrait includes age 25, gender female, interest label makeup, fashion, short video, consumption ability medium, purchase intention medium-high, and advertisement sensitivity low. According to the user's latest comment on the behavior of the makeup advertisement, the interest weight of the makeup category is increased, and the weight of the long-term non-interaction digital category is reduced. A certain user has not generated new behavior for 20 days, and the historical behavior tag weight is reduced by 40%. In the purchase intention label update, the user browses the makeup advertisement for 8 minutes, comments on the product efficacy, sets the browsing time score to 8, the comment score to 12, the cumulative intention score to 20, reaches the preset threshold of 18, and increases the purchase intention level to "medium-high". After the user completes the purchase, the score is reset and the purchased makeup product model and efficacy are recorded, and the purchase preference is updated.

[0081] The cross-platform user identity recognition module associates the identities of users in the content platform APP, web end, and third-party login accounts. The device fingerprint of the APP, the Cookie of the web end, and the social account ID of the third-party login are collected to build an identification mapping relationship. A certain user logs in to the APP through a WeChat account and logs in to the web end through the same WeChat account. The system calculates the similarity of the behavior characteristics of the two, including interest labels, browsing time periods, and interaction habits. The similarity reaches 88%, which exceeds the threshold of 80%. A unified global identifier is formed to realize cross-terminal behavior track analysis and find that the user clicks on a makeup advertisement on the APP and completes the purchase through the web end, providing support for cross-platform optimization.

[0082] The advertisement creative dynamic generation and adaptation module generates personalized creatives based on the user portrait. For users interested in makeup, text creatives containing their concerned efficacy and user reviews are generated, and short videos of product use are matched. Different screen size devices are adapted to, mobile phones use vertical short videos + simple scripts, and computers use horizontal pictures + detailed efficacy explanations. Based on historical interaction data, A / B testing of three creative schemes is performed to display product efficacy, use scenarios, and user feedback, respectively. The click-through rate is monitored in real time, and the "product efficacy + use scenario" combination creative is selected to improve the attractiveness of the advertisement.

[0083] The delivery strategy dynamic optimization decision module optimizes the delivery parameters. The target audience orientation uses a population expansion algorithm, taking high-conversion makeup users as seed users, expanding potential users with a similarity of the top 25%, and setting an upper limit of 40% for the expansion ratio. The advertisement bid is increased by 15% for high-intention users and maintained at the base bid for ordinary users. The distribution of delivery channels is adjusted according to the conversion effect of each content panel. The short video panel has good conversion effect, allocating 50% of the budget, the information panel allocates 30%, and the live panel allocates 20%. The budget is tilted towards makeup and fast-moving consumer goods, accounting for 80% of the total budget. The multi-objective optimization algorithm generates optimal parameters combined with traffic prediction data. The traffic prediction uses the formula , The average traffic of the makeup advertisement last year was 1.2 million, 0.3 for the trend growth rate, 1 year, 1.4 for the holiday impact coefficient, platform anniversary activities, calculated =120x(1+0.3x1)x1.4=2,184,000,000, which provides the basis for budget allocation.

[0084] The advertisement delivery strategy execution and scheduling module converts the parameters into delivery instructions and issues them to each board of the content platform and third-party advertising channels through the API interface. Real-time monitoring of the success rate of instruction issuance is ensured to be 99.9%. When a third-party channel interface call fails, the backup interface is automatically switched, and after three consecutive failures, the issuance is suspended and an alarm is raised. Monitor the actual exposure and plan deviation, and automatically adjust the instruction when the deviation exceeds 10%. Real-time calculation of the cost of acquiring traffic for each board, and when the cost of the live board exceeds the upper limit, reduce the delivery proportion by 3% to ensure the stability and economy of delivery.

[0085] The historical data mining and strategy model training module stores nearly 4 years of historical data, totaling 80TB. Random forest algorithm is used to mine historical data patterns and train strategy optimization models. Input user portrait, ad attributes, environment data period, content type, and output predicted conversion rate and conversion cost. Every 12 hours, the model parameters are iteratively optimized with new data to improve prediction accuracy.

[0086] The visual interaction and strategy configuration module provides a visual interface for operations personnel to configure advertisement basic parameters such as delivery period, budget, and target indicators, and set optimization trigger conditions such as when the conversion rate is lower than the target by 30% and optimization frequency every 30 minutes. The interface displays real-time user behavior statistics such as age distribution, interest proportion, and delivery effect dynamic curve such as exposure and conversion rate changes, and strategy optimization suggestions such as adjusting the creative type of digital advertising to product evaluation. Supports exporting data reports to facilitate operations personnel to review.

[0087] Table 2 Comparison of effects before and after optimization of content platform information flow advertisement delivery

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[0089] The data in Table 2 reflects the significant optimization effect of the system. Before optimization, the content platform information flow advertisement had low matching degree between advertisement and user demand due to incomplete user behavior monitoring and lagging portrait update, with click conversion rate of only 3.8%, conversion cost of 185 yuan, input-output ratio of 1.6, and user resistance rate of up to 23%. After optimization, the system captures user behavior comprehensively through real-time multi-source data collection and cross-platform identity recognition, analyzes cross-terminal trajectories coherently, generates personalized creative based on dynamically updated user portrait, and accurately reaches target users through precise advertising, with click conversion rate increasing to 9.2%. The delivery strategy is dynamically optimized based on real-time effects, with resources concentrated in high-conversion channels and categories, conversion cost reduced to 110 yuan, and input-output ratio improved to 3.1. Personalized delivery reduces ineffective advertising interference, with user resistance rate decreasing from 23% to 8%. Cross-platform conversion coherence increases from 58% to 93%, achieving smooth conversion of users on different terminals. The data fully proves the core value of the system in improving the precision of information flow advertising, reducing user resistance, and optimizing delivery efficiency, effectively solving the core pain points of content platform advertising delivery.

[0090] Referring to Fig. 2 The line chart intuitively shows the real-time improvement trend of click conversion rate during the advertising delivery process, which gradually increases from 4.2% at 10:00 to 8.5% at 14:00, doubling the conversion rate within 5 hours. This change fully embodies the closed-loop value of the system "real-time monitoring-dynamic optimization": the system continuously analyzes the delivery effect by real-time collection of user behavior data, dynamically adjusts the target audience targeting, advertisement bidding, and creative display strategy. For example, after 11:00, the user group with low conversion rate was excluded by optimizing the audience targeting, and the high-attractive advertisement creative was switched at 13:00, which directly promoted the conversion rate. The time trend verifies the system's rapid response capability to delivery effect, which can timely capture user behavior changes and adjust strategies to avoid the effect stagnation caused by static delivery.

[0091] Referring to Fig. 3 The column chart clearly compares the conversion effects of different delivery channels. The APP end ranks first with a conversion rate of 9.2%, followed by live streaming with a conversion rate of 8.1%, the small program end with a conversion rate of 7.8%, the third-party platform with a conversion rate of 6.3%, and the web end with the lowest conversion rate of only 4.5%. This data provides clear basis for the dynamic optimization decision module of the system's delivery strategy: the system can allocate more budget and traffic to APP, live streaming, and other efficient channels, appropriately reduce the proportion of web end delivery, or optimize the advertisement creative and landing page design for the web end. The data verifies the scientificity of the system's channel allocation optimization, which realizes resource concentrated delivery by accurately identifying efficient channels, improves the overall advertising conversion efficiency, and avoids the waste caused by equal allocation of resources in each channel.

[0092] The above merely describes preferred embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can make equivalent replacements or changes within the technical scope disclosed by the present application and according to the technical solutions and inventive concept of the present application, which should be covered within the protection scope of the present application.

Claims

1. A real-time behavior monitoring and dynamic optimization system for advertising placement strategies, characterized in that, Includes the following modules: The real-time multi-source user behavior data collection module collects real-time data on user interactions with advertisements across different platforms through methods such as event tracking SDK, API calls, and log capture; the collected data is then cleaned, anonymized, and structurally transformed. The distributed real-time data storage and indexing module adopts a distributed database cluster architecture, supports data sharding and dynamic expansion, establishes multi-dimensional indexes based on user identifiers, timestamps, and ad IDs, and sets up a data backup mechanism. The real-time behavior feature extraction and analysis module extracts behavioral features from user data, identifies behavioral temporal patterns, and accurately calculates the intensity of user interests to provide a basis for subsequent decision-making. In the real-time behavior feature extraction and analysis module, the calculation of user interest intensity combines user behavior type and behavior time, using a weighted calculation method. The calculation method for interest intensity I is as follows: ,in For the first The weighting of different behavior types is as follows: click behavior has a higher weighting than browsing behavior. For the first The frequency of this behavior For the first The time interval from the occurrence of this behavior to the present. The attenuation coefficient; The real-time advertising performance evaluation module sets an evaluation indicator system, calculates real-time indicator values ​​based on real-time user behavior data, compares and analyzes the deviation rate with preset target values, and identifies channels, creatives, or audiences with poor advertising performance. The user profile dynamic update module adjusts the profile tag weights and purchase intention levels based on the latest user behavior and campaign performance data to maintain the accuracy and timeliness of user profiles. In the user profile dynamic update module, the update of user purchase intention tags combines the user's browsing depth, add-to-cart behavior, inquiry behavior and historical purchase records to set the intention score corresponding to different behaviors. When the accumulated intention score reaches the preset threshold, the purchase intention level is increased. When the user makes a purchase, the intention score is reset and the purchase preference is recorded. The dynamic optimization decision-making module for ad placement strategies combines user profiles and performance evaluation results to intelligently optimize audience targeting, ad bidding, channel allocation, and budget configuration, and generate strategy parameters. The advertising strategy execution and scheduling module converts the optimized strategy parameters into delivery instructions and sends them to various platforms via API interfaces; it monitors the execution status in real time, and automatically adjusts the instructions or triggers an alarm when the number of consecutive failed API calls exceeds a preset value, and executes the strategy according to the optimization goals; In the advertising strategy execution and scheduling module, a multi-level fault tolerance mechanism is set up. When an API call to an advertising platform fails, it automatically switches to a backup interface. When the number of consecutive failed API calls exceeds a preset value, the issuance of new instructions to the platform is suspended and a manual intervention process is initiated. During strategy execution, the traffic acquisition cost of each platform is calculated in real time. When the cost exceeds the preset limit, the ad placement ratio on that platform is automatically reduced. The historical data mining and strategy model training module stores historical data, mines performance patterns, and trains and optimizes the model. It iterative updates based on real data continuously improve the model's prediction accuracy. In the historical data mining and strategy model training module, a time series prediction algorithm is used to predict advertising traffic within a preset time period in the future. The calculation of the predicted traffic Q takes into account historical traffic from the same period, trend factors, and the impact of holidays. The calculation method is as follows: ,in This is the average flow rate for the same period in history. The trend growth rate For time intervals, This refers to the impact coefficient of holidays; The visualization interaction and strategy configuration module provides a visual interface for users to configure basic parameters, target metrics, and strategy optimization rules for ad placement, and displays real-time user behavior data statistics, dynamic curves of ad placement performance, and optimization suggestions.

2. The real-time behavior monitoring and dynamic optimization system for advertising placement strategies according to claim 1, characterized in that, It also includes a cross-platform user identification module, which collects users' device fingerprints, login accounts, and behavioral habits on various platforms to build a unique user identifier mapping relationship. When a user engages in behavior on a new platform, the similarity between the user's behavioral characteristics and existing user identifiers is calculated. User identifiers with similarity exceeding a preset threshold are associated to form a unified global user identifier.

3. The real-time behavior monitoring and dynamic optimization system for advertising placement strategies according to claim 1, characterized in that, It also includes a module for dynamic generation and adaptation of advertising creatives, which dynamically generates or adjusts advertising creative content based on user profile interest tags and device information, optimizes the creative display layout for devices with different screen sizes, and uses A / B testing to select creative element combinations based on user interaction data of historical creatives.

4. The real-time behavior monitoring and dynamic optimization system for advertising placement strategies according to claim 1, characterized in that, In the real-time multi-source user behavior data acquisition module, natural language processing and image recognition technologies are used to transform unstructured data. For user comment text data, keywords and sentiment are extracted; for advertisement screenshot image data, objects, scenes, and text information in the image are identified and converted into structured tags. Simultaneously, the real-time performance index R of the data acquisition is calculated. ,in For the time when the behavior occurs, Data storage completion time, This is the preset maximum allowable delay time.

5. The real-time behavior monitoring and dynamic optimization system for advertising placement strategies according to claim 1, characterized in that, The real-time evaluation module for advertising performance establishes a tiered evaluation system. The first tier is the basic performance tier, which includes impressions and clicks; the second tier is the conversion performance tier, which includes click-through rate and conversion cost; and the third tier is the value performance tier, which includes return on investment and user lifetime value. Weights are assigned to the indicators at each tier, and a comprehensive performance score is calculated.

6. The real-time behavior monitoring and dynamic optimization system for advertising placement strategies according to claim 1, characterized in that, In the dynamic optimization decision-making module of the delivery strategy, the target audience targeting optimization adopts the audience expansion algorithm. The initial target audience is seed users. The feature similarity between potential users and seed users is calculated. Users with high similarity and not covered are selected as the expanded audience. At the same time, an upper limit for the expansion ratio is set. The delivery effect data of the expanded audience is fed back to the system in real time.

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