Programmed advertisement putting system based on multi-source dynamic price comparison and intelligent quantity control

Abstract

The invention discloses a programmed advertisement putting system based on multi-source dynamic price comparison and intelligent quantity control. After receiving an advertisement request of a client, the system initiates bidding requests to a plurality of advertisement transaction platforms in parallel in the same request period, collects bidding responses in a preset response time window, and determines and displays target advertisement content based on a dynamic price comparison rule. And meanwhile, the system performs full-link data acquisition on advertisement display, click and conversion behaviors, and dynamically regulates and controls the putting quantity or putting frequency of subsequent advertisement requests according to the full-link data acquisition so as to realize intelligent control on the putting rhythm and budget consumption. In addition, through unified management of application downloading and installation processes, accurate attribution of advertisement conversion behaviors is realized. On the premise of ensuring the response efficiency of the system, the advertisement resource utilization efficiency and the putting effect can be improved, and the method has good engineering practicability and popularization value.

Description

Technical Field

[0001] This invention relates to the field of mobile internet application technology, and in particular to a programmatic advertising delivery system based on multi-source dynamic price comparison and intelligent volume control. Background Technology

[0002] With the continuous expansion of mobile internet applications, in-app advertising has gradually become one of the important ways for mobile applications to generate revenue. To improve the efficiency of advertising resource utilization, programmatic advertising technology is gradually replacing manual configuration methods, dynamically selecting advertising content for display when an ad request occurs through a real-time bidding mechanism.

[0003] Existing programmatic advertising systems typically interface with a single ad exchange or make sequential requests across multiple ad exchanges based on preset priorities. When an ad request is generated, the system can often only obtain a limited number of ad candidates, making it difficult to compare the bidding results from different ad exchanges within the same request period. As a result, it cannot guarantee that each ad impression will achieve the best possible return on investment.

[0004] In addition, although some systems have introduced a multi-ad platform access mechanism, they still use serial requests or polling to obtain ad responses. This not only increases the system response latency, but also easily leads to a decrease in ad fill rate due to timeouts or unstable responses in high-concurrency scenarios, making it difficult to meet the engineering requirements of mobile applications for low latency and high stability.

[0005] In terms of ad delivery control, existing technologies mostly limit ad impressions or budget consumption using fixed rules or static thresholds, lacking the ability to dynamically adjust based on real-time delivery data. When user behavior, traffic volume, or ad performance changes, problems such as excessive ad exposure, premature budget exhaustion, or a mismatch between delivery performance and cost can easily occur, affecting overall delivery efficiency.

[0006] On the other hand, key behaviors such as ad display, clicks, downloads and installations are often recorded in different systems or third-party platforms, resulting in fragmented data links and difficulty in forming a complete closed loop for performance evaluation. This makes it impossible to accurately attribute the effectiveness of ad conversions, and adjustments to the campaign strategy rely on human experience, with a low level of automation and intelligence.

[0007] Therefore, existing programmatic advertising technologies still have significant shortcomings in real-time optimization of multi-source advertising resources, dynamic control of advertising delivery pace, and accurate attribution of advertising performance. There is an urgent need for a programmatic advertising technology solution that can achieve parallel bidding across multiple ad exchanges during the ad request stage and combine full-link data for intelligent volume control and performance attribution, in order to improve the overall efficiency and stability of advertising delivery.

[0008] Therefore, existing technologies still need to be improved. Summary of the Invention

[0009] Given that existing programmatic advertising systems struggle to connect with multiple ad exchanges and conduct real-time horizontal price comparisons during the ad request phase, and lack dynamic volume control and performance attribution mechanisms based on end-to-end data during ad delivery, resulting in low ad resource utilization efficiency, difficulty in controlling delivery pace, and inaccurate performance evaluation, there is an urgent need for a technical solution that can achieve multi-source parallel bidding within the same ad request cycle and intelligently regulate ad delivery behavior based on real-time data, in order to improve the overall efficiency and stability of programmatic advertising.

[0010] The technical solution of the present invention is as follows: This invention provides a programmatic advertising delivery system based on multi-source dynamic price comparison and intelligent volume control, comprising: An advertising decision engine is used to convert an advertising request initiated by a client into a bidding request in a unified format after receiving the advertising request, and to send the bidding request to at least two advertising trading platforms in an asynchronous and parallel manner within the same advertising request cycle. The advertising decision engine is configured to receive bidding responses from various advertising trading platforms within a preset response time window, and extract bidding information from the bidding responses, including at least bidding parameters and advertising material parameters. The price comparison processing unit is used to compare and process the bidding information based on preset dynamic price comparison rules in order to determine the target advertising content; The data statistics and intelligent volume control module is used to collect full-link data on the display, click and conversion behavior of the target advertising content on the client, and dynamically adjust the number or frequency of subsequent advertising requests based on the collected data. The download and attribution module is used to manage the application download and installation process corresponding to the target advertising content, and to associate and attribute the installation behavior with the corresponding advertising request.

[0011] In one embodiment, the advertising decision engine is configured to not participate in the dynamic price comparison process for advertising exchanges that have not returned a bidding response at the end of the preset response time window.

[0012] In one embodiment, the dynamic price comparison rule includes at least one of the following: a highest-price-price-first rule based on the bidding parameters; or a comprehensive scoring rule based on the bidding parameters and combined with historical click-through rates or conversion rates.

[0013] In one embodiment, the dynamic price comparison rules are configured by the configuration and management module and can be updated online without stopping the advertising service.

[0014] In one embodiment, the data statistics and intelligent volume control module is configured to limit or increase the response volume of subsequent advertising requests based on the ad slot dimension or the global budget dimension.

[0015] In one embodiment, the traffic limiting or volume control is performed based on real-time collected exposure data, click data, or conversion data.

[0016] Another aspect of the present invention provides a programmatic advertising delivery method based on multi-source dynamic price comparison and intelligent quantity control, comprising: S1, Receive advertising requests initiated by the client; S2. Within the same advertising request period, the advertising request is sent in parallel to at least two advertising trading platforms to obtain bidding responses; S3. Collect the bidding responses within the preset response time window and determine the target advertising content based on dynamic price comparison rules; S4. Return the target advertisement content to the client for display; S5. Collect the display, click and conversion data corresponding to the target advertisement content, and dynamically adjust the delivery behavior of subsequent advertisement requests based on the data.

[0017] In one embodiment, the dynamic price comparison rule includes at least a comparison process based on bidding parameters, or a comprehensive comparison process based on bidding parameters and combined with historical performance data.

[0018] In one embodiment, the dynamic control includes controlling the number of responses or response frequency of ad requests based on ad placement level or global budget level.

[0019] In one embodiment, it also includes: S6. After a user triggers a download and completes the installation, associate the installation behavior with the corresponding ad request or ad click behavior to complete the attribution analysis of ad performance.

[0020] In summary, this invention addresses the challenges of real-time optimization of ad resources, dynamic control of ad delivery pace, and accurate evaluation of ad performance in programmatic advertising. It provides a holistic design from both system architecture and delivery process perspectives. By introducing a parallel bidding mechanism across multiple ad exchanges during the ad request phase, each ad request can be compared across multiple ad sources, thereby improving the rationality and stability of ad resource selection while ensuring system response efficiency.

[0021] Furthermore, this invention does not merely stop at the level of advertising content selection, but further incorporates key behaviors such as ad display, clicks, downloads, and installations into a unified data collection and analysis system. Based on real-time delivery data, it dynamically adjusts the number and frequency of subsequent ad requests, enabling the ad delivery process to have continuous feedback and self-adjustment capabilities, effectively avoiding problems such as excessive ad exposure or abnormal budget consumption.

[0022] Furthermore, by unifying the management of the application download and installation process and attributing conversion behavior to corresponding ad requests or ad clicks, this invention constructs a complete closed loop for advertising effectiveness evaluation, providing a reliable data foundation for optimizing subsequent campaign strategies. Overall, this invention, while ensuring engineering feasibility, achieves collaborative work between advertising campaign decisions, campaign control, and effectiveness evaluation. It can adapt to complex and ever-changing advertising environments and has high practical value and promotional significance.

[0023] Compared to existing programmatic advertising technologies, this invention does not simply increase ad fill rate by increasing the number of ad sources. Instead, it introduces a parallel bidding mechanism across multiple ad exchanges within a single ad request cycle, allowing for horizontal comparison of bidding results from different ad platforms at the same time. This approach breaks through the serial request or fixed priority selection mode commonly used in existing technologies, achieving real-time optimization of ad resources without significantly increasing system response latency. The balance it achieves between engineering feasibility and campaign effectiveness is remarkably unexpected.

[0024] Furthermore, this invention combines dynamic price comparison decision-making with real-time data feedback after ad delivery. By collecting data across the entire process of ad display, clicks, and conversions, it eliminates reliance on static rules or manual experience for ad delivery control. Instead, it dynamically adjusts the quantity and frequency of subsequent ad requests based on real-time data. This closed-loop control method enables continuous self-adaptation in the ad delivery process, maintaining relatively stable delivery results even under traffic fluctuations or changes in user behavior, significantly outperforming existing solutions that rely solely on fixed thresholds for control.

[0025] Furthermore, this invention, through unified management of the advertising application download and installation process, and by attributing the final conversion behavior to the corresponding advertising requests or clicks, transforms advertising effectiveness evaluation from post-event statistics into a decision-making basis that can be used for real-time optimization. This design not only improves the accuracy of conversion data but also enables the advertising delivery system to proactively avoid inefficient traffic during the delivery process, concentrating resources on high-conversion paths and further enhancing overall advertising revenue.

[0026] In summary, this invention, through the synergistic cooperation of parallel bidding, dynamic price comparison, intelligent volume control, and attribution analysis, achieves a deep integration between advertising placement decisions, placement control, and performance evaluation while ensuring the system's real-time responsiveness. The effect of this technology is not simply the sum of its individual features, but rather a comprehensive result generated by the reconstruction of the overall architecture and placement process, possessing technical advantages that are difficult to anticipate with existing technologies. Attached Figure Description

[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This invention provides a structural block diagram of a programmatic advertising delivery system based on multi-source dynamic price comparison and intelligent quantity control. Figure 2 This invention provides a method step diagram for a programmatic advertising delivery method based on multi-source dynamic price comparison and intelligent quantity control; Figure 3 The flowcharts for steps S1 and S2 of the programmatic advertising delivery method based on multi-source dynamic price comparison and intelligent quantity control provided by the present invention are as follows: Figure 4 A flowchart of step S3 of a programmatic advertising delivery method based on multi-source dynamic price comparison and intelligent quantity control provided by the present invention; Figure 5 The flowcharts for S4 and S5 of the programmatic advertising delivery method based on multi-source dynamic price comparison and intelligent quantity control provided by the present invention are shown below. Figure 6 A flowchart of step S6 of a programmatic advertising delivery method based on multi-source dynamic price comparison and intelligent quantity control provided by the present invention; Figure 7 The present invention provides an overall flowchart of a programmatic advertising delivery method based on multi-source dynamic price comparison and intelligent quantity control. Detailed Implementation

[0028] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention is further described in detail below. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. The embodiments of the invention are described below in conjunction with the accompanying drawings.

[0029] One embodiment of the present invention provides a programmatic advertising delivery system based on multi-source dynamic price comparison and intelligent quantity control. Please refer to [link to relevant documentation]. Figure 1 ,include: The advertising decision engine 1 is used to convert the advertising request initiated by the client into a bidding request in a unified format after receiving the advertising request, and send the bidding request to at least two advertising trading platforms in an asynchronous and parallel manner within the same advertising request cycle. The advertising decision engine 1 is configured to receive bidding responses from various advertising trading platforms within a preset response time window, and extract bidding information from the bidding responses, including at least bidding parameters and advertising material parameters. Price comparison processing unit 2 is used to compare and process the bidding information based on preset dynamic price comparison rules in order to determine the target advertising content; The data statistics and intelligent volume control module 3 is used to collect full-link data on the display, click and conversion behavior of the target advertising content on the client, and to dynamically adjust the number or frequency of subsequent advertising requests based on the collected data. The download and attribution module 4 is used to manage the application download and installation process corresponding to the target advertising content, and to associate the installation behavior with the corresponding advertising request.

[0030] In one specific embodiment, the programmatic advertising delivery system of the present invention is deployed on the server side to provide advertising content to client applications. When a client needs to display an advertisement, the client sends an advertisement request to the advertising decision engine 1. The advertisement request includes at least an advertisement slot identifier, terminal device information, and parameter information related to the advertisement display environment.

[0031] Upon receiving the advertising request, the advertising decision engine 1 first parses and encapsulates the request, converting advertising requests from different clients into bidding requests in a unified format, so that bidding can be initiated on multiple advertising exchange platforms subsequently. In this embodiment, the bidding requests are sent to at least two advertising exchange platforms asynchronously and in parallel within the same advertising request cycle, thereby avoiding the accumulation of response delays caused by serial requests.

[0032] During the parallel bidding process, the advertising decision engine 1 sets a unified preset response time window for each ad exchange platform to limit the time limit for receiving bidding responses. Within the response time window, the advertising decision engine 1 receives bidding responses returned from each ad exchange platform and performs unified parsing on the bidding responses to extract at least the ad bid parameters and ad creative parameters related to ad display, in order to form bidding information that can be used for subsequent price comparison processing.

[0033] After extracting the bidding information, the price comparison processing unit 2 compares and processes the bidding information from different advertising trading platforms based on pre-set dynamic price comparison rules. In one embodiment, the dynamic price comparison rules can determine the target advertising content based on bidding parameters; in another embodiment, the bidding information can be comprehensively evaluated based on the bidding parameters, combined with data such as historical click-through rates or conversion effects, to determine the final target advertising content to be displayed.

[0034] Once the target ad content is determined, the ad decision engine 1 encapsulates the target ad content into an ad response and returns it to the client for display. During ad display and user interaction, the data statistics and intelligent volume control module 3 collects end-to-end data on the display behavior, click behavior, and subsequent conversion behavior of the target ad content on the client, and stores and analyzes the collected data in a unified manner.

[0035] Based on the full-link data collection results, the data statistics and intelligent volume control module 3 dynamically regulates the delivery behavior of subsequent advertising requests. In one embodiment, the dynamic regulation includes limiting or increasing the number of advertisements returned by the advertising request; in another embodiment, the advertising delivery frequency can also be adjusted based on the ad slot or global budget dimension to achieve intelligent control of the advertising delivery rhythm.

[0036] When a user triggers an application download based on the target advertisement content, the download and attribution module 4 manages the application download process and tracks the application installation process after the download is complete. After the application is installed, the download and attribution module 4 associates the installation behavior with the corresponding advertisement request or advertisement click behavior, thereby completing the attribution analysis of the advertisement's performance.

[0037] Through the coordinated operation of the above modules, this invention can achieve dynamic price comparison between multiple advertising trading platforms while ensuring system response efficiency, and continuously regulate advertising placement behavior in conjunction with real-time data, thereby improving the efficiency of advertising resource utilization and the stability of placement effect.

[0038] In a further embodiment, the advertising decision engine 1 is configured to not participate in the dynamic price comparison process for advertising trading platforms that have not returned a bidding response at the end of the preset response time window.

[0039] Specifically, to ensure the real-time performance of the parallel bidding process and the overall system response efficiency, the advertising decision engine 1 sets a uniform preset response time window for each advertising request when sending bidding requests to multiple advertising exchanges. The response time window is used to limit the effective time range within which the advertising exchanges return bidding responses.

[0040] In one specific implementation, the advertising decision engine 1 initiates a timing mechanism simultaneously with sending a bidding request. When the response time window ends, the advertising decision engine 1 only performs subsequent parsing and price comparison processing on the bidding responses returned within that time window. For advertising exchanges that have not returned a bidding response by the end of the response time window, the system no longer waits for their response results, nor does it include them in the dynamic price comparison range of the current advertising request.

[0041] By employing the above methods, the system can avoid affecting the overall processing efficiency of ad requests due to response delays or anomalies of individual platforms when multiple ad trading platforms are connected, thus maintaining stable response latency and a high ad fill rate even in high-concurrency scenarios.

[0042] In a further embodiment, the dynamic price comparison rule includes at least one of the following: a highest price priority rule based on the bidding parameters; or a comprehensive scoring rule based on the bidding parameters and combined with historical click-through rates or conversion rates.

[0043] Specifically, in this embodiment, after collecting and analyzing the bidding responses, the price comparison processing unit 2 compares and processes the bidding information from different advertising trading platforms based on preset dynamic price comparison rules to determine the target advertising content. The dynamic price comparison rules are not fixed, but at least include a highest-price-priority rule based on bidding parameters, or a comprehensive scoring rule based on bidding parameters combined with historical performance data.

[0044] In one implementation, the price comparison processing unit 2 uses the bidding parameters returned by each advertising exchange platform as the main comparison basis. By comparing the size of the bidding parameters in different bidding responses, it selects the advertising material with the highest bid as the target advertising content, thereby achieving a direct increase in advertising revenue while ensuring the simplicity of the processing logic.

[0045] In another implementation, the price comparison processing unit 2 incorporates historical click-through rates or conversion rates, among other performance data, into the bidding parameters to comprehensively evaluate the bidding response. By weighting the bidding parameters and historical performance data, a comprehensive score is generated, which is then used as the basis for determining the target advertising content, thereby balancing short-term gains with long-term campaign effectiveness to some extent.

[0046] In a further embodiment, the dynamic price comparison rules are configured by the configuration and management module and can be updated online without stopping the advertising service.

[0047] To improve the system's flexibility and maintainability, the aforementioned dynamic price comparison rules are uniformly configured and managed by the configuration and management module. This module stores price comparison rule parameters for different ad placements, different campaign scenarios, or different time periods.

[0048] In one specific implementation, the configuration and management module supports online updates of dynamic price comparison rules. When the advertising strategy needs to be adjusted, the system can update the price comparison rules by modifying configuration parameters without stopping the advertising service. When processing subsequent advertising requests, the advertising decision engine 1 automatically loads the updated price comparison rules and performs the corresponding price comparison processing.

[0049] The above configuration method enables the system to flexibly adjust the price comparison strategy according to the actual campaign performance, advertiser needs, or changes in the market environment, avoiding a decline in campaign performance due to rigid rules, while also reducing the cost of system maintenance and strategy adjustment.

[0050] In a further embodiment, the data statistics and intelligent volume control module 3 is configured to limit or increase the flow of responses to subsequent advertising requests based on the ad slot dimension or the global budget dimension.

[0051] The data statistics and intelligent volume control module 3 does not merely display the advertising results statistically, but further serves as an important constraint on advertising decisions, controlling the response to subsequent advertising requests. In this embodiment, the intelligent volume control module can implement traffic limiting or volume control on advertising behavior based on ad placement dimensions or global budget dimensions.

[0052] Specifically, in one implementation, the system configures corresponding control parameters for different ad positions. When the exposure, clicks, or conversions of an ad position reach a preset threshold within a unit of time, the intelligent control module limits the number of responses to subsequent ad requests for that ad position, reducing the ad return frequency to prevent the ad from being overexposed in that position.

[0053] In another implementation, the intelligent volume control module manages ad delivery uniformly from a global budget perspective. When the system detects that the overall ad budget is being consumed too quickly or too slowly, it limits or increases the volume of subsequent ad requests to ensure that the ad delivery pace is consistent with the expected budget consumption target, thereby avoiding situations where the budget is exhausted prematurely or insufficiently delivered.

[0054] By introducing the aforementioned control mechanism during the ad request response phase, ad delivery control directly impacts the ad request processing flow itself, rather than being adjusted afterward. This improves the timeliness and effectiveness of ad delivery rhythm control from an engineering implementation perspective.

[0055] In a further embodiment, the rate limiting or volume control is performed based on real-time collected exposure data, click data, or conversion data.

[0056] The traffic limiting or volume control is not based on static parameters, but is dynamically adjusted based on real-time collected advertising data. This advertising data includes at least ad impression data, click data, or conversion data.

[0057] In one specific implementation, the data statistics and intelligent traffic control module 3 continuously receives exposure events and click events reported by the client during the ad display and user interaction process, and performs real-time statistical analysis on the data. When the system detects that the click-through rate of a certain ad slot is significantly lower than the historical level or the expected threshold, the intelligent traffic control module automatically reduces the ad placement frequency of that ad slot to reduce inefficient traffic consumption.

[0058] In another implementation, the intelligent volume control module adjusts the volume control strategy based on conversion data. When an ad demonstrates high conversion efficiency within a certain timeframe, the system can correspondingly increase the placement ratio of that ad or its corresponding ad slot to fully leverage the revenue advantages brought by high-conversion paths.

[0059] By dynamically adjusting the traffic control strategy based on real-time data, the advertising delivery control has the ability to provide continuous feedback and self-correction, thus maintaining a relatively stable overall delivery effect even when traffic structure, user behavior, or advertising performance changes.

[0060] Another aspect of this invention provides a programmatic advertising delivery method based on multi-source dynamic price comparison and intelligent volume control. Please refer to [link to relevant documentation]. Figures 2-7 ,include: S1, Receive advertising requests initiated by the client; S2. Within the same advertising request period, the advertising request is sent in parallel to at least two advertising trading platforms to obtain bidding responses; S3. Collect the bidding responses within the preset response time window and determine the target advertising content based on dynamic price comparison rules; S4. Return the target advertisement content to the client for display; S5. Collect the display, click and conversion data corresponding to the target advertisement content, and dynamically adjust the delivery behavior of subsequent advertisement requests based on the data.

[0061] In one specific embodiment, the programmatic advertising delivery method based on multi-source dynamic price comparison and intelligent quantity control of the present invention is applied to an advertising delivery system on the server side, and is used to complete the dynamic selection and delivery control of advertising content when the client requests the display of an advertisement.

[0062] First, the system receives an advertising request initiated by the client. This request is triggered by the client when it needs to display an advertisement and includes at least an ad placement identifier, terminal device information, and parameters related to the ad display environment. Upon receiving the request, the system parses the request content to determine the ad placement type and available delivery strategies corresponding to the request.

[0063] After parsing the ad request, the system converts the ad request into a unified format bidding request within the same ad request cycle and sends the bidding request to multiple ad exchanges in an asynchronous parallel manner. Initiating bidding in parallel allows multiple ad exchanges to participate in the bidding process for this ad request simultaneously, thus providing a basis for subsequent dynamic price comparison.

[0064] Subsequently, within a preset response time window, the system collects bidding responses from various ad exchanges. For bidding responses returned within the response time window, the system performs unified analysis to extract bidding parameters and ad creative information related to ad display. Ad exchanges that do not return a response within the response time window will no longer participate in the price comparison process for this ad request.

[0065] After parsing the bidding responses, the system compares and processes each bidding response based on preset dynamic bidding rules to determine the target advertising content for this display. In one implementation, the dynamic bidding rules are executed based on the bidding parameters in the bidding responses; in another implementation, the bidding responses can be comprehensively evaluated based on the bidding parameters and historical performance data to determine the target advertising content.

[0066] Once the target ad content is determined, the system encapsulates it into an ad response and returns it to the client, which then displays the ad. During the ad display process, the system simultaneously collects display events, click events, and subsequent conversion events corresponding to the target ad content, and records and statistically analyzes the relevant data.

[0067] After collecting the aforementioned ad delivery data, the system dynamically adjusts the delivery behavior of subsequent ad requests based on the display, click, and conversion data. Specifically, the system can adjust the number of responses or response frequency to ad requests based on real-time delivery data to achieve intelligent control of the ad delivery rhythm, thereby ensuring delivery effectiveness while avoiding the ineffective consumption of ad resources.

[0068] By continuously executing the above methods and steps, each advertising request can be dynamically compared across multiple ad exchanges, and real-time data can be used to provide feedback and adjustments to subsequent ad placements, thus forming a complete closed loop for programmatic ad placement.

[0069] In a further embodiment, the dynamic price comparison rule includes at least a comparison process based on bidding parameters, or a comprehensive comparison process based on bidding parameters and combined with historical performance data.

[0070] The dynamic price comparison rule is further limited to being executed based on the bidding parameters in the bidding response, or a comprehensive comparison process based on the bidding parameters and combined with historical campaign performance data.

[0071] In one specific implementation, after collecting bidding responses from multiple advertising exchange platforms, the system directly uses the bidding parameters contained in each bidding response as a comparison basis to sort the different bidding responses, and selects the advertising content corresponding to the bidding response with the highest bidding parameters as the target advertising content. This method has simple calculation logic and can maintain low processing latency in high-concurrency scenarios.

[0072] In another specific implementation, the system incorporates historical performance data as a supplementary decision-making basis while comparing bidding parameters. This historical performance data includes, but is not limited to, historical click-through rates or historical conversion rates. The system performs a weighted calculation based on the bidding parameters and historical performance data to generate a comprehensive score, and determines the target ad content based on this comprehensive score. This approach ensures that dynamic bidding not only considers the level of a single bid but also reflects the long-term performance of the ad during actual campaign execution.

[0073] In a further embodiment, the dynamic control includes controlling the number of responses or response frequency of ad requests based on the ad placement level or the global budget level.

[0074] The dynamic control is further limited to controlling the number of responses or response frequency of ad requests based on the ad placement level or the global budget level.

[0075] In one implementation, the system separately tracks the number of ad impressions and clicks for different ad slots within a predetermined time period, and adjusts the response frequency for subsequent ad requests for that ad slot based on the statistical results. When the system detects that the ad delivery volume for a certain ad slot has reached a preset threshold, it reduces the response frequency of ad requests for that ad slot to prevent concentrated ad display in that location.

[0076] In another implementation, the system uniformly controls ad delivery from a global budget perspective. The system continuously monitors the overall ad budget consumption rate. When the budget consumption rate exceeds the expected target, it slows down the budget consumption pace by reducing the number of responses to subsequent ad requests or lowering the response frequency. When the budget consumption rate is lower than the expected target, it increases the response ratio of ad requests accordingly to ensure the progress of ad delivery.

[0077] In a further embodiment, the method further includes: S6. After a user triggers a download and completes the installation, associate the installation behavior with the corresponding ad request or ad click behavior to complete the attribution analysis of ad performance.

[0078] Specifically, after a user triggers a download based on the target advertisement content and completes the application installation, the system further performs attribution analysis of the advertisement's performance. When an advertisement is displayed or clicked, the system generates identification information for the corresponding advertisement request and carries this identification information during the download process. When an application installation completion event is detected, the system matches the installation completion event with the carried identification information, thereby associating the installation behavior with the corresponding advertisement request or advertisement click.

[0079] By employing the aforementioned attribution processing methods, the system can accurately determine the source of ad conversions, providing a basis for adjusting subsequent dynamic price comparison rules or optimizing volume control strategies, thereby achieving a closed-loop linkage between ad placement decisions and placement results.

[0080] In summary, this invention systematically designs the overall process of programmatic advertising by focusing on the generation, processing, and feedback of ad requests. In its implementation, starting with ad requests, it introduces a parallel bidding mechanism across multiple ad exchanges, enabling each ad request to acquire multiple ad candidates simultaneously, thereby improving the rationality of ad resource selection from the outset.

[0081] Building upon this foundation, this invention dynamically compares bid responses and incorporates configurable comparison rules, enabling advertising decisions to meet both real-time requirements and the flexibility to continuously adjust based on actual performance. By combining comparison rules with historical performance data, it avoids short-term decision-making biases arising from relying solely on single bids, resulting in more stable advertising outcomes.

[0082] Meanwhile, this invention incorporates ad display, click, and conversion behaviors into a unified data collection system, and directly applies the collected results to the subsequent ad request delivery control, creating a continuous feedback closed-loop structure in the ad delivery process. By implementing traffic limiting or volume control at the ad request response level, the system can adjust the delivery pace in a timely manner in response to traffic changes or budget fluctuations, thereby effectively reducing invalid exposures and resource waste.

[0083] Furthermore, by unifying the management of the application download and installation process and attributing installation behavior to corresponding ad requests or ad clicks, advertising effectiveness evaluation is no longer limited to post-event statistics but can provide direct evidence for ad placement decisions. The various implementation methods described above work together to ensure the ad placement system has good scalability and stability in its engineering implementation.

[0084] Overall, the specific implementation of the present invention is not a simple superposition of existing technologies, but a complete solution formed by the collaborative design of key links such as ad request processing, delivery control and effect evaluation, which can effectively improve the overall efficiency and practical application value of programmatic advertising.

[0085] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A programmatic advertising delivery system based on multi-source dynamic price comparison and intelligent quantity control, characterized in that, include: An advertising decision engine is used to convert an advertising request initiated by a client into a bidding request in a unified format after receiving the advertising request, and to send the bidding request to at least two advertising trading platforms in an asynchronous and parallel manner within the same advertising request cycle. The advertising decision engine is configured to receive bidding responses from various advertising trading platforms within a preset response time window, and extract bidding information from the bidding responses, including at least bidding parameters and advertising material parameters. The price comparison processing unit is used to compare and process the bidding information based on preset dynamic price comparison rules in order to determine the target advertising content; The data statistics and intelligent volume control module is used to collect full-link data on the display, click and conversion behavior of the target advertising content on the client, and dynamically adjust the number or frequency of subsequent advertising requests based on the collected data. The download and attribution module is used to manage the application download and installation process corresponding to the target advertising content, and to associate and attribute the installation behavior with the corresponding advertising request.

2. The programmatic advertising delivery system according to claim 1, characterized in that, The advertising decision engine is configured to not participate in the dynamic price comparison process for advertising exchanges that have not returned a bidding response at the end of the preset response time window.

3. The programmatic advertising delivery system according to claim 1, characterized in that, The dynamic price comparison rules include at least one of the following: the highest price priority rule based on the bidding parameters; or a comprehensive scoring rule based on the bidding parameters and combined with historical click-through rates or conversion rates.

4. The programmatic advertising delivery system according to claim 3, characterized in that, The dynamic price comparison rules are configured by the configuration and management module and can be updated online without stopping the advertising service.

5. The programmatic advertising delivery system according to claim 1, characterized in that, The data statistics and intelligent volume control module is configured to limit or increase the flow of responses to subsequent ad requests based on ad placement dimensions or global budget dimensions.

6. The programmatic advertising delivery system according to claim 5, characterized in that, The traffic limiting or volume control is executed based on real-time collected exposure data, click data, or conversion data.

7. A programmatic advertising delivery method based on multi-source dynamic price comparison and intelligent quantity control, characterized in that, include: S1, Receive advertising requests initiated by the client; S2. Within the same advertising request period, the advertising request is sent in parallel to at least two advertising trading platforms to obtain bidding responses; S3. Collect the bidding responses within the preset response time window and determine the target advertising content based on dynamic price comparison rules; S4. Return the target advertisement content to the client for display; S5. Collect the display, click and conversion data corresponding to the target advertisement content, and dynamically adjust the delivery behavior of subsequent advertisement requests based on the data.

8. The programmatic advertising delivery method according to claim 7, characterized in that, The dynamic price comparison rules include at least a comparison process based on bidding parameters, or a comprehensive comparison process based on bidding parameters and combined with historical performance data.

9. The programmatic advertising delivery method according to claim 7, characterized in that, The dynamic control includes controlling the number of responses or response frequency of ad requests based on the ad placement level or the global budget level.

10. The programmatic advertising delivery method according to claim 7, characterized in that, Also includes: S6. After a user triggers a download and completes the installation, associate the installation behavior with the corresponding ad request or ad click behavior to complete the attribution analysis of ad performance.

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