An advertisement delivery whole-process automation management system and method

Abstract

This application provides an automated management system and method for the entire advertising delivery process, comprising: acquiring high-frequency data streams from an advertising exchange platform, parsing the key business fields contained therein, processing the heterogeneous structure in the high-frequency data streams, using a data compression algorithm to remove redundant content, and generating a simplified advertising data packet format; constructing a custom protocol framework for communication between advertising systems based on the simplified advertising data packet format, wherein the framework supports adding business fields through a dynamic field extension mechanism, and determining the field parsing rules of the protocol framework; acquiring interface specification data from different advertising delivery platforms, and processing the differences between the specification data using field mapping and type conversion algorithms; if the field definitions do not match, performing a format standardization operation to obtain a unified advertising business data model.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an automated management system and method for the entire process of advertising placement. Background Technology

[0002] In the field of digital marketing, an automated management system for the entire advertising delivery process is the core of achieving precise marketing and efficient operations. The efficiency of its communication mechanism directly determines the real-time performance and accuracy of advertising campaigns. Current systems rely on various general communication protocols and platform interfaces for data exchange. These methods have limitations when handling the high-frequency, heterogeneous data streams unique to advertising. General protocols are not designed for advertising scenarios; their data packet structures and processing logic often contain significant redundancy. When dealing with processes requiring extremely fast responses, such as real-time bidding, protocol overhead leads to increased latency, making it difficult to guarantee millisecond-level decision-making speeds. Furthermore, different advertising platforms provide varying interface specifications, data formats, and update mechanisms, requiring significant resources for customized adaptation and conversion. This not only increases complexity but also makes cross-platform state synchronization and data aggregation slow and error-prone.

[0003] These limitations collectively point to a core technical challenge in this field: the contradiction between the dual demands of "high real-time performance" and "strong compatibility" in communication protocols. High real-time performance requires protocols to be extremely lightweight, capable of quickly parsing and transmitting critical information, such as instantly completing a bid in a bidding request. Strong compatibility, on the other hand, requires protocols to possess sufficient flexibility and scalability to accommodate business data from different sources and with different structures, such as converting diverse platform data back into a format that the system can understand. One pursues simplicity and speed, while the other requires inclusiveness and adaptability; these two aspects create an inherent tension in design and implementation.

[0004] This tension presents unique technical challenges in practical applications. For example, in the synchronization of ad creative updates and delivery status, to ensure compatibility with various platform interfaces, the system might use complete data packets for synchronization. However, this contradicts real-time requirements, causing network bandwidth to be consumed by a large amount of repetitive data, while critical status update information is overwhelmed and delayed. Conversely, if a highly streamlined custom protocol is used in pursuit of real-time performance, it becomes difficult to adapt to the constantly emerging new media platforms, requiring redevelopment for each integration and drastically increasing system expansion and maintenance costs. Therefore, designing a communication mechanism that can meet millisecond-level real-time interaction while flexibly adapting to the data specifications of various platforms has become a key issue in building an efficient and stable automated ad delivery system. Summary of the Invention

[0005] This invention provides an automated management system and method for the entire advertising delivery process, mainly including: The system acquires high-frequency data streams from advertising trading platforms, parses the key business fields contained therein, processes the heterogeneous structure in the high-frequency data streams, uses data compression algorithms to remove redundant content, and generates a simplified advertising data packet format. Based on the simplified advertising data packet format, a custom protocol framework for communication between advertising systems is constructed. The framework supports adding business fields through a dynamic field extension mechanism and determines the field parsing rules of the protocol framework. The interface specification data of different advertising platforms are obtained. For the differences between the specification data, field mapping and type conversion algorithms are used for processing. If the field definitions do not match, the format standardization operation is performed to obtain a unified advertising business data model. In the real-time bidding process, the bid and material information of the bidding request are extracted from the unified advertising business data model, and the request is transmitted to the bidding engine using a lightweight communication protocol. By monitoring the response latency and comparing it with a preset threshold, the decision of whether to participate in the bidding is determined. In response to the need for updating advertising creatives, the updated information is received through the unified advertising business data model. If the updated information conflicts with the current creative status on the server, a conflict resolution algorithm based on version comparison is used to process it and obtain a globally consistent creative status. Obtain the update log of the globally consistent material status, filter high-priority change records from it, and push the change records to the cross-platform advertising material synchronization module using incremental data compression and transmission methods until the synchronization completion confirmation flag of the target platform is received; When accessing a new advertising platform, the field definitions of the protocol framework are extended to be compatible with the new platform. If the new platform specification introduces unknown advertising data types, the corresponding data format adaptation plugin is loaded to generate an extended communication protocol version. Based on the extended communication protocol version, the throughput and latency performance of cross-platform advertising material synchronization are tested. If the network bandwidth usage exceeds a preset threshold, the data block size and compression ratio parameters of incremental transmission are adjusted to obtain an optimized cross-platform advertising communication mechanism.

[0006] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses an automated management system and method for the entire advertising delivery process. Addressing core business scenarios such as high-frequency data stream processing, cross-platform compatibility, real-time bidding decisions, and consistency of creative content synchronization in advertising delivery, it proposes a logically interconnected solution. To address the heterogeneous structure and redundancy of high-frequency data streams, this invention simplifies data packets through data compression algorithms and constructs a custom protocol framework to support dynamic field expansion, ensuring efficient and compatible cross-platform communication. For the real-time bidding stage, this invention extracts key information from a unified data model and optimizes decision-making efficiency by combining a lightweight communication protocol and a latency monitoring mechanism. Simultaneously, it resolves creative content update conflicts through version comparison algorithms and achieves cross-platform synchronization using incremental compression transmission. Ultimately, this invention achieves automation, efficiency, and consistency throughout the entire advertising delivery process, significantly improving cross-platform collaboration capabilities and system performance. Attached Figure Description

[0007] Figure 1 This is a flowchart of an automated management system and method for the entire advertising delivery process according to the present invention.

[0008] Figure 2 This is a schematic diagram of an automated management system and method for the entire advertising delivery process according to the present invention.

[0009] Figure 3 This is another schematic diagram of an automated management system and method for the entire advertising delivery process according to the present invention. Detailed Implementation

[0010] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0011] like Figures 1-3 This embodiment of an automated management system and method for the entire advertising delivery process may specifically include: Step S101: Obtain the high-frequency data stream from the advertising exchange platform, parse the key business fields contained therein, process the heterogeneous structure in the high-frequency data stream, use a data compression algorithm to remove redundant content, and generate a simplified advertising data packet format.

[0012] Obtain high-frequency data streams from the advertising exchange platform. Parse key business fields within the data stream. Process heterogeneous structures in the parsed data. Determine if the heterogeneous structures contain redundant content. If redundant content exists, apply compression algorithms. Generate a streamlined advertising data package.

[0013] High-frequency data streams obtained from ad exchanges typically exist in the form of real-time bidding requests.

[0014] For example, a request might contain key business fields such as ad slot identifiers, user profile tags, and bidding information. Parsing these fields requires identifying specific markers in the data stream or following predefined protocol formats, such as extracting the values ​​with keys "user_id" and "ad_slot" from a JSON string.

[0015] Specifically, if a request contains "user_id" as "12345" and "ad_slot" as "homepage banner", the parsing engine will accurately capture this information for subsequent processing. Handling the heterogeneous structure of the parsed data is a core challenge.

[0016] In one possible implementation, heterogeneous structures may manifest as the same business field having different formats in different data sources.

[0017] For example, user geolocation information might be a three-level string of "province-city-district" in data source A, but an array containing latitude and longitude values ​​in data source B. Processing this requires establishing a unified field mapping rule to normalize both "Beijing Haidian District" and "[116.3,39.9]" to the standard field "geo," but their internal structures will still differ. Determining whether such heterogeneous structures contain redundant content requires analyzing the degree of information redundancy when different structures express the same semantics. For example...

[0018] In one embodiment, an advertisement record contains both a structured "Age: 25" field and an unstructured text description, "This user is a 25-year-old young white-collar worker." Clearly, the "25 years old" in the text description is redundant with the structured age field. Furthermore, if the geographic location information provides both a structured province / city field and a complete detailed address string, the latter often includes information from the former, which also constitutes redundancy. If redundant content exists, a compression algorithm is used to process it.

[0019] It should be noted that the compression here refers not only to reducing the size of bytes, but also to logical simplification.

[0020] For example, to address the age redundancy mentioned above, the text description field can be discarded, retaining only the structured numerical values. For geographic location redundancy, a strategy can be designed to retain only the most accurate latitude and longitude coordinates, as these can be used to deduce province and city information, but not vice versa. This business logic-based compression effectively eliminates information duplication and reduces data packet size. Generating a streamlined advertising data packet is the ultimate goal. After the above parsing, normalization, and compression, the previously redundant raw data is refined into a standardized data packet containing only core, non-redundant information.

[0021] For example, an original request might contain 50 fields, but the processed data packet contains only 15 key fields, such as user ID, ad placement, bid, and standardized geographic location. This significantly improves data transmission and storage efficiency, reduces the processing load on downstream systems such as bidding engines or analytics platforms, and allows data analysis to focus more on core business metrics.

[0022] Step S102: Based on the simplified advertising data packet format, a custom protocol framework for communication between advertising systems is constructed. The framework supports adding business fields through a dynamic field extension mechanism and determines the field parsing rules of the protocol framework.

[0023] Obtain the protocol data stream containing the version number and field set. Determine the metadata structure of the current protocol body from the mapping table based on the version number. Use a parser to read the extension bits in the data stream. If the extension bits indicate the existence of dynamic fields, extract the business key and value content based on the type code and length field. Update the field set with the parsed business key-value pairs. Determine the complete field set as the parsed output of the protocol framework.

[0024] For example, when processing protocol data streams related to advertising exchange platforms, obtaining the data stream containing the version number and field set is the primary task. The version number, as an identifier for the protocol data, helps the system quickly locate the corresponding metadata structure. Suppose a data stream contains a version number of 2.0; the system will use a pre-defined mapping table to find the metadata structure corresponding to version 2.0, such as a 32-bit field length and five fixed fields. This mapping mechanism ensures the accuracy of subsequent parsing and avoids field misalignment issues caused by version differences.

[0025] For example, when parsing the extension bits in a data stream, the parser can read the identifier bits at specific positions. Assuming the extension bit value is 1, indicating the presence of a dynamic field, the system will further read the subsequent type code and length field. The type code might be 3, indicating a string-type business key, while the length field value is 10, indicating that the following 10 bytes contain the business value content. In this way, the system can dynamically extract key-value pairs, such as a key of "ad slot ID" and a value of "AD12345678". This dynamic parsing method adapts to the diversity of data structures in advertising exchange platforms.

[0026] For example, regarding the process of updating business key-value pairs to a field set, consider this scenario: the initial field set already contains fixed fields such as "transaction time" and "advertiser ID," while the dynamic field "ad slot ID," obtained through extended bit parsing, is appended to the field set. The final field set might contain information such as "transaction time: 2023-10-01 10:00:00," "advertiser ID: CUST001," and "ad slot ID: AD12345678." This update mechanism ensures the integrity of the field set, laying the foundation for subsequent protocol framework output.

[0027] In one possible implementation, the field set can be validated when determining the complete field set as the parsed output of the protocol framework.

[0028] For example, the system checks if all required fields in the field set are present. Assuming "Advertiser ID" is a required field, if its value is empty, the system will mark the parsing as failed and log it. This validation mechanism improves the reliability of data processing and ensures that the output protocol framework meets the requirements of the advertising exchange platform.

[0029] In one possible implementation, a caching mechanism can be introduced for the extraction and updating of dynamic fields. Assuming the system frequently processes data streams with version number 2.0, the corresponding metadata structure and common dynamic field type codes can be cached. This way, during the next parsing iteration, the system doesn't need to query the mapping table again; it can directly access the cached data. This approach is particularly suitable for high-frequency data stream processing scenarios in advertising exchange platforms, significantly improving parsing efficiency.

[0030] In one possible implementation, the output of the field set can also be customized according to business needs.

[0031] For example, an ad exchange platform might need to prioritize displaying certain fields from its field set to the upstream system, such as "ad slot ID" and "transaction time." The system can adjust the field order during output to ensure that higher-priority fields are displayed first. This customized output better meets the needs of the business and improves the ease of data interaction. Through the above implementation methods, it can be seen that the entire process, from version number mapping to field set updates to the final output, is interconnected and logically rigorous. Especially in scenarios like ad exchanges where real-time data accuracy is extremely important, the above methods effectively ensure smooth data processing and meet business requirements.

[0032] Step S103: Obtain interface specification data from different advertising platforms. To address the differences between the specification data, use field mapping and type conversion algorithms for processing. If the field definitions do not match, perform format standardization operations to obtain a unified advertising business data model.

[0033] The process begins by acquiring interface specification data from different advertising platforms. Preliminary analysis of the data structure and field content of each platform is performed to determine if there are data discrepancies or inconsistencies in field definitions. If discrepancies exist, the discrepancies and mismatched fields are recorded, yielding preliminary discrepancy analysis results. To address field inconsistencies in the preliminary analysis, a field mapping method is used to map the field names and content of different platforms to a pre-established standard field library. If fields cannot be directly mapped, they are marked as items to be converted, and an intermediate data set after field mapping is determined. Type conversion is then performed on the items to be converted in the intermediate data set. A type conversion algorithm is used to adjust inconsistent data types to standard types. If format mismatches still exist after type conversion, a format standardization operation is performed to obtain a temporary data set with a unified format. Based on this temporary data set, the logical relationships between fields are analyzed, and the field definitions and data content are integrated. If data is missing or abnormal during integration, supplementary data is retrieved from the advertising platforms, and the integrity of the integrated data is assessed to obtain a complete data set. For the complete dataset, a unified advertising business model is constructed, mapping the fields and data content of each platform to a fixed structure within the business model. If structural conflicts are found during the mapping process, field priorities are adjusted to determine the final business model data. The final business model data is then validated to determine if it conforms to preset business logic rules. If it does not conform, the process reverts to the field mapping stage for correction, resulting in a unified advertising data model that meets business requirements.

[0034] In one possible implementation, obtaining the interface specification data of different advertising platforms is the first step in data integration.

[0035] Specifically, this requires actively pulling or receiving API documentation and data samples from multiple advertising platforms, such as Platform A and Platform B.

[0036] For example, in the ad creative data returned by Platform A's interface, the field identifying the uniqueness of an ad is named "ad_id" with a numeric value; while the corresponding field on Platform B is named "creativeId" with a string value. Preliminary analysis clearly identifies this difference in naming and data type, and records "inconsistent ad identifier field naming" and "inconsistent identifier data type" as key differences in the preliminary difference analysis results. To address the field inconsistency issue, a field mapping method is crucial. A pre-established standard field library defines "ad identifier" as the standard field, with a standard type of string. During the mapping process, both Platform A's "ad_id" and Platform B's "creativeId" can be mapped to the standard field "ad identifier" according to the mapping rules. However, if a platform has a field named "campaign_name," while the standard library only has "ad campaign name," and the business meanings differ slightly, this field may be marked as an item to be converted and not directly mapped, thus forming an intermediate data set containing mapped fields and items to be converted.

[0037] Understandably, for items to be converted in the intermediate data set, type conversion and format standardization are required.

[0038] For example, if platform A's "budget" field value is an integer in cents, while the standard type requires it to be a floating-point number in yuan, the type conversion algorithm will divide it by 100.0 to obtain a floating-point value in yuan. If platform C's "deployment time" field value is the string "2024-05-20", while the standard format requires "YYYY / MM / DD", then a format standardization operation needs to be performed to adjust it to "2024 / 05 / 20". After these processes, a temporary data set with a unified format is obtained.

[0039] In one embodiment, when integrating data based on a uniformly formatted temporary data set, it is necessary to analyze the logical relationships between fields.

[0040] For example, the standard field "Ad Status" might be determined by both the platform's original fields "status" and "review_status". If "status" is "ACTIVE" but "review_status" is "PENDING", the integrated "Ad Status" should be marked as "Under Review". During this process, if the data returned by platform D is found to be missing the "Click-Through Rate" field, a retry mechanism needs to be triggered to retrieve supplementary data from that platform, ensuring that the final integrated dataset does not lack critical information. The ultimate goal is to construct a unified advertising business model for the complete dataset. This model defines fixed structures such as "Ad Campaign", "Ad Group", and "Ad Creative" and their included fields. Structural conflicts may occur when mapping data from various platforms to this model.

[0041] For example, platform E places the "bid" information at the ad creative level, while the business model defines it at the ad group level. In this case, the field priority needs to be adjusted according to business rules. The decision must be made to either elevate the field value from platform E to the ad group level or retain a copy at the creative level and establish a relationship, thus determining the final business model data that conforms to the model structure. Finally, validating the business model data is a crucial step to ensure data usability. Pre-defined business logic rules might include "the ad campaign budget must be greater than the sum of the budgets of all its ad groups." The validator will traverse the data, calculate, and compare relevant values. If it finds that an ad campaign budget is 1000 yuan, but its two ad groups have budgets of 600 yuan and 500 yuan respectively, totaling 1100 yuan, then this data does not conform to the rule. The system will backtrack to the field mapping or data integration stage to check for errors in the mapping and calculation process of the "budget" field and correct them until all data passes validation, ultimately producing a high-quality, unified advertising data model that meets business needs.

[0042] Step S104: In the real-time bidding process, the bid and material information of the bidding request are extracted from the unified advertising business data model, and the request is transmitted to the bidding engine using a lightweight communication protocol. By monitoring the response delay and comparing it with a preset threshold, the decision of whether to participate in the bidding is determined.

[0043] The bidding request data required for the real-time bidding process is extracted from the advertising business data model. The bidding information and creative information in this data are structured to obtain complete bidding request content, determining the foundational data for subsequent transmission. A lightweight communication protocol is used to transmit the structured bidding request content to the bidding engine. By monitoring the network status and data integrity during transmission in real time, it is determined whether the transmission meets preset standards, resulting in a transmission quality assessment. If the transmission quality assessment meets the preset standards, the bidding engine processes the received bidding request content to obtain response latency data and determine the specific range of response latency. The response latency data is compared with a preset threshold. If the response latency exceeds the preset threshold, the current bidding request is marked as not participating in the bidding, resulting in a preliminary decision. Based on the preliminary decision, requests marked as not participating in the bidding are logged. By analyzing the reasons for latency and bidding information in the logs, it is determined whether the priority processing strategy for subsequent requests needs to be adjusted. By further processing the requests for participation in the bidding process, and combining the bidding information and material information, the matching logic is executed in the bidding engine to obtain the final bidding participation decision result.

[0044] Extracting real-time bidding request data from advertising business data models hinges on filtering and conversion.

[0045] In one possible implementation, a unified advertising business data model includes user profiles, contextual information, ad placement attributes, and complete information on multiple candidate ads.

[0046] Specifically, the real-time bidding process doesn't require all data; for example, advertiser settlement information or long-term campaign strategies are not included in the calculations. Therefore, the extraction process first filters data based on a pre-defined list of bidding fields. This list explicitly specifies the fields required by the bidding engine, such as user identifiers, ad placement size, page URL, candidate ad bids, creative IDs, and target redirect links. This filtering operation allows for the precise extraction of a lightweight dataset for real-time decision-making from the massive business model. The structured processing of bid and creative information is to meet the stringent requirements of communication protocols and engine parsing.

[0047] For example, bid information might exist in multiple currencies and units in the original model, such as "CNY cents" or "USD dollars," but in the bidding request, it must be uniformly converted to micro-dollar values ​​in the base currency unit. Simultaneously, creative material information might contain links to multiple sizes of images and videos. Structured processing requires selecting the most suitable creative material link based on the current ad placement's technical specifications, such as only supporting 960x640 pixel images, and encapsulating it into specific fields within a JSON object recognizable by the engine. This process ensures that the request content is both complete and formatted correctly, avoiding parsing errors or bidding failures on the engine side due to data format ambiguity. Using a lightweight communication protocol for transmission is primarily to balance speed and reliability.

[0048] In one embodiment, a private UDP-based protocol can be selected, with simple sequence numbers and checksums added at the application layer.

[0049] For example, each bidding request data packet is assigned an incrementing ID, and the receiving end monitors the continuity of these IDs. If network monitoring detects the loss of three consecutive data packets, or if the checksum error rate exceeds one in a thousand, the transmission quality is deemed to fail to meet the preset standard, and the transmission quality assessment result for that request is "failed." This design can detect network anomalies extremely quickly, providing a basis for subsequent decisions. The response latency of the bidding engine is crucial in determining whether to participate in the bidding.

[0050] Understandably, real-time bidding typically has a strict time window, such as the time from sending a request to receiving a decision must be completed within 100 milliseconds.

[0051] Specifically, after receiving a request, the bidding engine needs to perform a series of complex calculations, including ad retrieval, click-through rate estimation, and overall ranking calculation. The system records the time from when the request is sent to when the engine returns the first byte in real time. If this time exceeds a preset threshold of 80 milliseconds, even if the highest bid is subsequently calculated, the system will mark the request as "not participating in the bidding." This is because excessive delays may result in the returned ad creative not being delivered before the user's page finishes loading after winning the bid, thus causing the ad slot to be blank and wasting the exposure opportunity. Log analysis of requests marked as not participating in the bidding aims to optimize the overall strategy.

[0052] For example, the logs record the specific stage at which the delay occurred, such as "DNS resolution delay of 50 milliseconds" or "engine internal sorting logic timeout". Analysis revealed that during peak traffic periods, the proportion of abandoned bids due to engine internal sorting timeouts increased significantly. Based on this, the priority processing strategy for subsequent requests can be adjusted.

[0053] For example, requests from high-value media pages are allocated to a dedicated, low-load engine computing queue, thus prioritizing the bidding participation rate of high-value traffic with limited resources and improving overall revenue. Finally, for requests that are legitimately participating in the bidding, the bidding engine will execute the core matching logic.

[0054] For example, the engine combines user tags with ad creative tags to perform a relevance score, while also considering bid levels. Suppose a request has three candidate ads with bids of 500, 600, and 700 microdollars, and estimated click-through rates (CTRs) of 2%, 1.5%, and 1%, respectively. The bidding engine may use a mechanism of multiplying the bid by the estimated CTR to rank the ads. The final winner is not necessarily the ad with the highest bid, but rather the ad with the highest expected overall revenue. Therefore, the system outputs the final bidding decision result, namely the winning ad ID and the settlement price to be paid, completing a full real-time bidding process.

[0055] Step S105: For the advertising material update requirement, receive material update information through the unified advertising business data model. If the update information conflicts with the current material status on the server, use a conflict resolution algorithm based on version comparison to process it and obtain a globally consistent material status.

[0056] The system retrieves updated ad creative information from the client using a unified business model. The received data is initially parsed to obtain the specific content and timestamp of the update. Based on the parsed update information, the system queries the server-side creative status to determine if there are any discrepancies. If the timestamp of the received update information is later than the timestamp recorded on the server, a conflict is identified. For conflicting creative states, a version comparison-based conflict resolution algorithm is used. By comparing the update information with the version identifier recorded on the server, the specific conflicting fields are identified. Based on the specific conflicting fields, a field-level state synchronization operation is performed, overwriting the corresponding fields on the server with the newer fields from the update information, determining the synchronized temporary state. The temporary state is compared with global consistency rules. If the temporary state meets the preset global consistency conditions, it is updated to the server-side official state, resulting in the final creative state. After obtaining the final creative state, a synchronization confirmation message is sent to the client. Detailed update process information is logged to determine if the synchronization operation is complete. For the log data after the synchronization operation is completed, automatic archiving is performed. The log data is categorized and saved according to preset storage rules to determine the archiving result.

[0057] In one possible implementation, the core of obtaining advertising material update information from the client is to ensure the consistency of the states at both ends of the business model.

[0058] For example, an advertiser updates the image of a banner ad from "Version A.jpg" to "Version B.jpg" through their management backend client. Simultaneously, the system generates a millisecond-based timestamp for this operation, such as "1717654321000". Upon receiving this structured data packet, the server first parses it, extracting the creative ID, the new creative file identifier, and this precise timestamp. This forms the basis for all subsequent conflict detection and synchronization operations.

[0059] Specifically, the server then queries the current record of the material ID in the database. Suppose the server's last update timestamp is "1717654320500", while the timestamp sent by the client is "1717654321000". Through a simple comparison, since the client's timestamp is larger, it indicates that the client's update occurred after the latest known state on the server. Therefore, the system determines that there is a state difference, i.e., a conflict has occurred. This timestamp comparison is crucial for resolving concurrent updates; it establishes the basic logical starting point of using the latest operation as the standard. More refined solutions are needed for identified conflicts.

[0060] For example, the status of the material may include multiple fields such as "material file URL", "review status", "effective date" and "target audience tags".

[0061] In one embodiment, the version comparison algorithm checks these fields one by one. It might find that the "Material File URL" and "Effective Time" fields carry new version identifiers in the client-side update information, while the version identifiers of the "Review Status" and "Target Audience Tags" fields are consistent with those on the server side. Therefore, the conflict is specifically located in the "Material File URL" and "Effective Time" fields. This field-level comparison avoids full overwrite, preserving the existing values ​​of non-conflicting fields, improving processing accuracy and efficiency. Next, the system performs field-level synchronization.

[0062] For example, the server-side value of "Material File URL" is updated from "cdn.abc.com / A.jpg" to "cdn.abc.com / B.jpg", while the "Effective Time" is changed from "2024-06-10 00:00:00" to "2024-06-11 12:00:00" specified by the client. This creates a temporary state containing some new fields and some old fields. This temporary state must be validated against global consistency rules.

[0063] For example, a key global rule is that "materials in the 'Rejected' status must have an empty 'Effective Time' field." If the update only modifies the material file and resubmits it for review, but the "Review Status" in the temporary state remains the old "Pending Review," while the "Effective Time" field is set to a future time, this violates the rule. The system will intercept this synchronization at this stage to prevent data errors in business logic. Only after the temporary state passes all consistency rule checks will it be submitted as the server's official state. Subsequently, the server will send a confirmation message to the client that initiated the update, informing them of successful synchronization, and record information such as the material ID, conflicting fields, resolution strategy, final status, and timestamp involved in this update operation in the operation log.

[0064] For example, a log might record "Material ID: 12345, Conflict Resolution: Override field [File URL, Effective Time], Final Status: Under Review, Timestamp: 1717654321000". This log data is then automatically transferred to archive storage according to a preset strategy, such as by date or material type, facilitating subsequent auditing and issue tracing, while relieving storage pressure on the main database. The entire process ensures eventual consistency and business compliance of ad creative data updates in a distributed environment.

[0065] Step S106: Obtain the update log of the globally consistent material status, filter high-priority change records from it, and push the change records to the cross-platform advertising material synchronization module using incremental data compression and transmission until the synchronization completion confirmation flag of the target platform is received.

[0066] Obtain the globally consistent material status update log. Extract the timestamp and operation type fields from the update log. If the operation type field belongs to a preset high-priority operation set, the record is determined to be a high-priority change record. Compress the high-priority change record using a differential algorithm to obtain an incremental data packet. Transmit the incremental data packet to the cross-platform ad material synchronization module via a message queue. Listen for messages returned by the synchronization module; if the message content contains a preset synchronization completion flag, terminate the push process.

[0067] In one possible implementation, obtaining a globally consistent media status update log is fundamental for subsequent analysis. This log records a complete snapshot and change operations after each instance of globally consistent media status.

[0068] For example, after resolving a conflict and updating the status of material A, the server generates a log entry. This entry includes not only the final title, description, and image link values ​​of material A after the update, but also the type of operation that triggered the update, such as "emergency replacement," "scheduled update," or "strategy adjustment," along with a timestamp accurate to milliseconds. This log is the only trusted data source for subsequent change tracking and synchronized distribution.

[0069] Specifically, after extracting the timestamp and operation type fields from the update log, the system needs to determine the priority of the operation type according to preset rules.

[0070] For example, a high-priority operation set can be predefined, including key operation types such as "regulatory compliance modifications," "urgent fixes for major errors," and "core ad creative switching." If a log record's operation type field value is "urgent fix for major errors," the system will immediately classify it as a high-priority change record. This classification mechanism ensures that critical changes are identified and processed first, avoiding online ad display incidents due to information delays. For identified high-priority change records, differential algorithms are used for compression to improve data transmission efficiency. The principle of the differential algorithm is to compare the creative state before and after the change, recording only the changed field content, rather than transmitting the complete creative data package.

[0071] For example, a high-priority change might only modify the landing page link field of Creative B, changing it from "old-url.com" to "new-url.com," while other fields such as the title and image remain unchanged. In this case, the incremental data packet generated by the differential algorithm will only contain the creative identifier, the timestamp of the change, and the key-value pair "landing page link: new-url.com," reducing the data volume by more than 90% compared to the complete creative packet. This significantly reduces network transmission load and speeds up synchronization. Transmitting incremental data packets to the cross-platform ad creative synchronization module via a message queue is a decoupled and reliable asynchronous communication method. The message queue acts as a buffer; even if the downstream synchronization module temporarily reaches its processing capacity or experiences a brief failure, the incremental data packet will be persistently stored in the queue, ensuring that critical change information is not lost.

[0072] For example, when an urgent compliance modification incremental package for material C is added to a message queue named "high_priority_sync", synchronization module consumers deployed in multiple geographic regions will pull and process the data package from the queue in parallel. Listening for messages returned by the synchronization module is a crucial step in ensuring a closed synchronization loop. After successfully applying the changes in the incremental data package to the target platform (such as the material library of an external advertising network), the synchronization module sends a confirmation message to a specified callback address, containing a preset synchronization completion flag, such as the status code "SYNC_200_OK". The upstream system continuously listens for such messages. Once it receives a response containing this flag, it indicates that the high-priority change has been successfully reached and taken effect on the target end. At this point, the push process for this record can be safely terminated, and the internal task status can be updated. This mechanism provides end-to-end visibility, ensuring the eventual consistency of critical change synchronization.

[0073] Step S107: When accessing a new advertising platform, extend the field definitions from the parsing rules of the protocol framework to be compatible with the new platform. If the new platform specification introduces unknown advertising data types, load the corresponding data format adaptation plugin and generate an extended communication protocol version.

[0074] By obtaining parsing rules from the protocol framework and performing extended matching on the field definitions of the new platform, a preliminary set of compatible fields is obtained. Based on this preliminary set, the presence of unknown data types is analyzed. If an unknown data type is detected, a data type identification process is triggered to determine its structural characteristics. Using the structural feature analysis results, the corresponding data format adaptation plugin is loaded to perform format conversion on the unknown data type, obtaining the converted data format content. Key field information is extracted from the converted data format content and subjected to secondary verification using the protocol framework's parsing rules to determine if the field information conforms to the communication protocol requirements. If the field information conforms to the communication protocol requirements, the verified field information is integrated into the communication protocol, generating updated communication protocol version data. For the updated communication protocol version data, a version compatibility detection process is executed to obtain the detection results and confirm whether the version update meets the advertising requirements. Based on the detection results, the updated communication protocol version data is automatically deployed to the integration environment of the new platform, completing the entire compatibility extension process.

[0075] In one possible implementation, the protocol framework can be viewed as a predefined rule base used to standardize data exchange formats between different platforms. When integrating with a brand new advertising platform, the basic parsing rules must first be obtained from this framework.

[0076] For example, the basic rules might specify that the key for the "Ad Title" field is "title," and its value type is string. However, a new platform might use "headline" as the key, or require the title length to be no more than 25 characters. Through extended matching, the system recognizes "headline" as a compatible field synonymous with "title," thus forming an initial set.

[0077] Specifically, when analyzing the initial set, you may encounter unknown data types.

[0078] For example, a new "Dynamic Creative Parameter" field has been introduced on the platform. Its value is not a simple string or number, but a nested structure containing a material ID, a list of replacement texts, and conditional rules. This triggers a data type identification process. This process analyzes sample data to determine the structural characteristics of the field, such as identifying it as a JSON object with three fixed keys. Subsequently, based on the identified "nested JSON object" characteristic, the system loads the corresponding data format adaptation plugin. This plugin transforms the complex nested structure of the external platform into a flattened list of key-value pairs that the internal protocol can handle.

[0079] For example, the "Dynamic Creative Parameters" object is converted into a set of independent fields: "Creative ID: 12345", "Replacement Text A: Summer Promotion", and "Trigger Condition: User Tag is X". This conversion process is crucial for compatibility and extension. Next, key field information, such as "Creative ID", is extracted from the converted content and subjected to secondary validation. This secondary validation checks the field's length, type, and required status according to the protocol framework's rules.

[0080] For example, the validation rule requires that the "Creative ID" must be a number and no longer than 10 characters. If the converted "12345" meets the requirements, the field passes the validation. Fields that pass the validation will be integrated into the existing communication protocol to generate a new version.

[0081] For example, a new section, "dynamic_creative_params," is added to protocol V2.1, defining its subfields. After generating the new version, a version compatibility check must be performed. This check simulates the interaction between the historical version client and the new version server, ensuring that the old system will not encounter parsing errors when encountering the newly added fields, thus guaranteeing the stability of ad delivery. Finally, once all checks pass, the updated protocol version data will be automatically deployed to the environment interfacing with the new platform.

[0082] For example, the system pushes the new protocol configuration file to the gateway server responsible for communicating with the platform and restarts the relevant services, so that all subsequent advertising material data sent to the platform can be packaged and transmitted in the new, compatible format, thus seamlessly completing the entire extension process.

[0083] Step S108: Based on the extended communication protocol version, test the throughput and latency performance of cross-platform advertising material synchronization. If the network bandwidth usage exceeds a preset threshold, adjust the data block size and compression ratio parameters of incremental transmission to obtain an optimized cross-platform advertising communication mechanism.

[0084] The system compares the network bandwidth utilization during cross-platform ad creative synchronization with a preset threshold. If the network bandwidth utilization exceeds the preset threshold, an incremental transmission parameter adjustment process is triggered. Based on the difference between the current network bandwidth utilization and the preset threshold, the direction and magnitude of the data block size adjustment are determined. A preset compression algorithm is used, and the corresponding compression ratio parameter is calculated based on the adjusted data block size. The adjusted data block size and compression ratio parameter are applied to the incremental transmission process. The ad creative synchronization throughput and latency performance metrics are monitored after applying the new parameters.

[0085] For example, in cross-platform ad creative synchronization scenarios, network bandwidth utilization is a key metric. Suppose an ad delivery system detects that the current network bandwidth utilization has reached 85% while synchronizing ad creatives, while the preset threshold is 70%, clearly exceeding the limit. Excessive bandwidth utilization can lead to data transmission congestion, affecting synchronization efficiency. Therefore, it is necessary to dynamically adjust transmission parameters to optimize resource allocation.

[0086] Specifically, the comparison between network bandwidth utilization and a preset threshold can be achieved using real-time monitoring tools. Assuming the system collects network traffic data every minute and finds that the utilization rate consistently exceeds the threshold by 15 percentage points, an incremental transmission parameter adjustment process will be triggered. The adjustment is based on the magnitude of the difference; for example, the larger the difference, the greater the reduction in data block size to reduce bandwidth pressure. If the difference is 15%, the system might adjust the data block size from 10MB to 5MB to reduce the amount of data transmitted per transaction.

[0087] For example, when determining the direction and magnitude of data block size adjustments, historical transmission data can be analyzed. Suppose that the peak bandwidth occurred at 8 PM daily over the past week, the system can predict this in advance and reduce the data block size to 3MB before the peak, while restoring it to 8MB during off-peak hours. This dynamic adjustment method effectively balances bandwidth load and avoids data backlog during peak periods.

[0088] Specifically, when applying the preset compression algorithm, the system may choose a lightweight compression method to process the adjusted data blocks. Assuming a compression ratio of 50%, this means that a 5MB data block will only occupy 2.5MB of bandwidth after compression. This approach can further alleviate network pressure and improve transmission efficiency without affecting the integrity of the content.

[0089] For example, when applying the adjusted block size and compression ratio parameters to incremental transmissions, high-priority ad creatives can be prioritized. If an ad campaign needs to be synchronized within 24 hours, the system will adjust the block size for that task to the minimum and increase the compression ratio to 60% to ensure timely transmission. For non-urgent creatives, the default parameters will be maintained, and resources will be allocated appropriately.

[0090] Specifically, monitoring the synchronization throughput and latency performance metrics after applying new parameters can be done by setting key performance indicators to evaluate the effect. For example, if adjusting the parameters results in an increase in throughput from 10MB / s to 15MB / s and a decrease in latency from 500ms to 300ms, it indicates that the parameter adjustment effectively optimized the synchronization process. This monitoring mechanism helps to identify potential problems promptly and ensure the stability of cross-platform transmission of advertising creatives.

[0091] For example, in actual business operations, ad creative synchronization often involves files of various formats and sizes, such as images and videos. For large video creatives, the system may further subdivide the data blocks to 1MB and combine them with higher compression ratio parameters to avoid occupying bandwidth resources for extended periods. For small image creatives, the restrictions can be appropriately relaxed to speed up the synchronization process. This differentiated processing approach can improve overall transmission efficiency and ensure the timeliness of ad delivery.

[0092] If the technical solution of this application involves the acquisition of personal information, the product using this solution has clearly informed the user of the processing rules and obtained the user's consent before processing. If sensitive personal information is involved, the user's individual consent has been obtained and the "express consent" requirement has been met. For example, a clear sign is placed at the collection device to indicate the collection scope, and the user's voluntary entry is considered as consent; or authorization is obtained through pop-up windows, user uploads, etc. The processing rules include the processor, purpose, method, and type of information.

[0093] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. The present invention has been described in detail with reference to preferred embodiments. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A fully automated management system and method for the entire advertising delivery process, characterized in that, The method includes: The system acquires high-frequency data streams from advertising trading platforms, parses the key business fields contained therein, processes the heterogeneous structure in the high-frequency data streams, uses data compression algorithms to remove redundant content, and generates a simplified advertising data packet format. Based on the simplified advertising data packet format, a custom protocol framework for communication between advertising systems is constructed. The framework supports adding business fields through a dynamic field extension mechanism and determines the field parsing rules of the protocol framework. The interface specification data of different advertising platforms are obtained. For the differences between the specification data, field mapping and type conversion algorithms are used for processing. If the field definitions do not match, the format standardization operation is performed to obtain a unified advertising business data model. In the real-time bidding process, the bid and material information of the bidding request are extracted from the unified advertising business data model, and the request is transmitted to the bidding engine using a lightweight communication protocol. By monitoring the response latency and comparing it with a preset threshold, the decision of whether to participate in the bidding is determined. In response to the need for updating advertising creatives, the updated information is received through the unified advertising business data model. If the updated information conflicts with the current creative status on the server, a conflict resolution algorithm based on version comparison is used to process it and obtain a globally consistent creative status. Obtain the update log of the globally consistent material status, filter high-priority change records from it, and push the change records to the cross-platform advertising material synchronization module using incremental data compression and transmission methods until the synchronization completion confirmation flag of the target platform is received; When accessing a new advertising platform, the field definitions of the protocol framework are extended to be compatible with the new platform. If the new platform specification introduces unknown advertising data types, the corresponding data format adaptation plugin is loaded to generate an extended communication protocol version. Based on the extended communication protocol version, the throughput and latency performance of cross-platform advertising material synchronization are tested. If the network bandwidth usage exceeds a preset threshold, the data block size and compression ratio parameters of incremental transmission are adjusted to obtain an optimized cross-platform advertising communication mechanism.

2. The fully automated management system and method for advertising delivery according to claim 1, characterized in that, The process of acquiring high-frequency data streams from advertising exchange platforms, parsing key business fields contained therein, processing heterogeneous structures in the high-frequency data streams, and using data compression algorithms to remove redundant content to generate a simplified advertising data packet format includes: Obtain high-frequency data streams from advertising exchange platforms; Analyze key business fields in the data stream; Process heterogeneous structures in the parsed data; Determine whether the heterogeneous structure contains redundant content; If there is redundant content, a compression algorithm will be used to process it; Generate a streamlined ad data package.

3. The fully automated management system and method for advertising placement according to claim 1, characterized in that, Based on the simplified advertising data packet format, a custom protocol framework for communication between advertising systems is constructed. This framework supports adding business fields through a dynamic field extension mechanism and determines the field parsing rules of the protocol framework, including: Obtain the protocol data stream containing the version number and field set; The meta-information structure of the current protocol body is determined from the mapping table based on the version number; Use a parser to read the extended bits from the data stream; If the extended bit identifier has a dynamic field, the business key and value content are extracted based on the type code and length field; Update the field set with the parsed business key-value pairs; Determine the complete set of fields as the parsed output of the protocol framework.

4. The fully automated management system and method for advertising delivery according to claim 1, characterized in that, The process involves acquiring interface specification data from different advertising platforms. To address the differences between these specifications, field mapping and type conversion algorithms are used. If field definitions do not match, a format standardization operation is performed to obtain a unified advertising business data model, including: Obtain the interface specification data of different advertising platforms, perform preliminary analysis on the data structure and field content of each platform, determine whether there are data differences and inconsistencies in field definitions, and if there are differences, record the differences and mismatched fields to obtain preliminary difference analysis results. To address the inconsistency of fields in the preliminary difference analysis results, a field mapping method was adopted to map the field names and contents of different platforms to a pre-established standard field library. If a field cannot be directly mapped during the mapping process, it is marked as an item to be converted, and the intermediate data set after field mapping is determined. By performing type conversion on the items to be converted in the intermediate data set, and using a type conversion algorithm, inconsistent data types are adjusted to standard types. If there are still format mismatches after type conversion, a format standardization operation is performed to obtain a temporary data set with a unified format. Based on a uniformly formatted temporary data set, the logical relationships between the fields are analyzed, and the field definitions and data content are integrated. If data is missing or abnormal during the integration process, supplementary data is obtained from the advertising platform, the integrity of the integrated data is judged, and a complete data set is obtained. For the complete dataset, a unified advertising business model is constructed, mapping the fields and data content of each platform to a fixed structure in the business model. If structural conflicts are found during the mapping process, the field priority is adjusted to determine the final business model data. By validating the final business model data, it is determined whether the data conforms to the preset business logic rules. If it does not conform to the rules, the process is traced back to the field mapping stage for correction, resulting in a unified advertising data model that meets business requirements.

5. The fully automated management system and method for advertising delivery according to claim 1, characterized in that, In the real-time bidding process, the bid and creative information of the bidding request are extracted from the unified advertising business data model, and the request is transmitted to the bidding engine using a lightweight communication protocol. The decision to participate in the bidding is determined by monitoring the response latency and comparing it with a preset threshold, including: The bidding request data required for the real-time bidding process is extracted from the advertising business data model. The bidding information and material information in the data are structured to obtain the complete bidding request content and determine the basic data for subsequent transmission. A lightweight communication protocol is used to transmit the structured bidding request content to the bidding engine. By monitoring the network status and data integrity during the transmission process in real time, it is determined whether the transmission meets the preset standards and the transmission quality assessment result is obtained. If the transmission quality assessment result meets the preset standard, the received bidding request content is processed through the bidding engine to obtain the bidding engine's response latency data and determine the specific numerical range of the response latency. The response latency data is compared and analyzed with a preset threshold. If the response latency exceeds the preset threshold, the current bidding request is marked as not participating in the bidding, thus obtaining a preliminary decision result. Based on the preliminary decision, requests marked as not participating in the bidding are logged. By analyzing the reasons for the delay and the bidding information in the logs, it is determined whether the priority processing strategy for subsequent requests needs to be adjusted. By further processing the requests for participation in the bidding process, and combining the bidding information and material information, the matching logic is executed in the bidding engine to obtain the final bidding participation decision result.

6. The fully automated management system and method for advertising placement according to claim 1, characterized in that, Regarding the requirement to update advertising creatives, the unified advertising business data model receives creative update information. If the update information conflicts with the current creative status on the server, a conflict resolution algorithm based on version comparison is used to process it and obtain a globally consistent creative status, including: By using a unified business model, update information of advertising materials is obtained from the client. The received data is then preliminarily analyzed to obtain the specific content and timestamp of the update information. Based on the parsed update information, query the status of the materials on the server side to determine if there are any status differences. If the timestamp of the received update information is later than the timestamp recorded on the server side, then a conflict is determined. For conflicting material statuses, a conflict resolution algorithm based on version comparison is used to process them. By comparing the update information and the version identifier recorded on the server, the specific fields of the conflict are obtained. Based on the specific field of the conflict, perform a field-level state synchronization operation, overwriting the corresponding field on the server side with the newer field content in the update information, and determine the temporary state after synchronization. The temporary state is compared with the global consistency rules. If the temporary state meets the preset global consistency conditions, the temporary state is updated to the official state on the server side to obtain the final material state. After obtaining the final material status, a synchronization confirmation message is sent to the client. The update process details are recorded in the log to determine whether the synchronization operation is complete. For log data after synchronization is completed, automatic archiving is performed. Log data is categorized and saved according to preset storage rules to determine the archiving results.

7. The fully automated management system and method for advertising placement according to claim 1, characterized in that, The process of obtaining the update log of the globally consistent creative status, filtering high-priority change records, and pushing these change records to the cross-platform ad creative synchronization module using incremental data compression and transmission until a synchronization completion confirmation flag is received from the target platform includes: Obtain the globally consistent material status update log; Extract the timestamp and operation type fields from the update log; If the operation type field belongs to the preset high-priority operation set, then the record is determined to be a high-priority change record; The high-priority change records are compressed using a differential algorithm to obtain incremental data packets; The incremental data packet is transmitted to the cross-platform advertising creative synchronization module via a message queue; Listen for messages returned by the synchronization module. If the message content contains a preset synchronization completion flag, terminate the push process.

8. The fully automated management system and method for advertising placement according to claim 1, characterized in that, When accessing a new advertising platform, the parsing rules of the protocol framework are extended to be compatible with the new platform's field definitions. If the new platform's specifications introduce unknown advertising data types, the corresponding data format adaptation plugin is loaded to generate an extended communication protocol version, including: By obtaining the parsing rules from the protocol framework, and performing extended matching on the field definitions of the new platform, a preliminary set of compatibility fields is obtained. Based on the initial set of compatibility fields, analyze whether there are any unknown data types. If an unknown data type is detected, trigger the data type identification process to determine the structural characteristics of the unknown data type. Using the structural feature analysis results, the corresponding data format adaptation plugin is loaded to perform format conversion for unknown data types and obtain the converted data format content. Key field information is extracted from the converted data format, and secondary verification is performed in conjunction with the parsing rules of the protocol framework to determine whether the field information meets the requirements of the communication protocol. If the field information meets the requirements of the communication protocol, the field information that passes the verification will be integrated into the communication protocol to generate updated communication protocol version data; For the updated communication protocol version data, a version compatibility test process is executed to obtain the test results to confirm whether the version update meets the needs of ad delivery. Based on the test results, the updated communication protocol version data is automatically deployed to the interface environment of the newly added platform, completing the entire compatibility and expansion process.

9. The fully automated management system and method for advertising delivery according to claim 1, characterized in that, The process involves testing the throughput and latency performance of cross-platform ad creative synchronization based on the extended communication protocol version. If network bandwidth usage exceeds a preset threshold, the incremental transmission data block size and compression ratio parameters are adjusted to obtain an optimized cross-platform ad communication mechanism, including: Compare the network bandwidth usage during the cross-platform ad creative synchronization process with a preset threshold. If the network bandwidth utilization rate exceeds the preset threshold, the incremental transmission parameter adjustment process will be triggered. The direction and magnitude of data block size adjustment are determined based on the difference between the current network bandwidth utilization rate and the preset threshold. The preset compression algorithm is used to calculate the corresponding compression ratio parameter based on the adjusted data block size; The adjusted data block size and compression ratio parameters are applied to the incremental transfer process; Monitor the synchronization throughput and latency performance metrics of advertising creatives after applying the new parameters.

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