Rta packet splitting method and apparatus, computer device, and storage medium

Abstract

This application relates to an RTA data packet splitting method, apparatus, computer equipment, and storage medium, belonging to the field of software engineering technology. This application involves pre-classifying and storing RTA data packets, allowing advertisers to configure splitting strategies to form multiple splitting strategies, setting filtering rules for these strategies, selecting suitable splitting strategies, adding product parameters to the splitting strategies to form a splitting scheme, performing a filter hit against the generated splitting scheme and the classified RTA data packets, and returning the splitting result. This allows for precise targeting based on advertisers' needs, refined audience traffic splitting based on product parameters, and storage and management of advertisers' splitting configuration strategies.

Description

Technical Field

[0001] This application relates to the field of software engineering technology, and in particular to an RTA data packet splitting method, apparatus, computer equipment, and storage medium. Background Technology

[0002] With the rapid development of the internet, online advertising has become one of the important means for businesses to promote their products and services. The main methods of online advertising include CPC (Cost Per Click), CPM (Cost Per Mille), and CPA (Cost Per Action). Among these, CPM is the most common method, charging based on the number of times the ad is displayed. However, the effectiveness of CPM is not ideal because the number of ad impressions does not guarantee effective ad exposure, nor does it guarantee the target audience.

[0003] With the development of RTA technology, ad placement methods have undergone a revolutionary change. RTA is a real-time bidding technology that allows advertisers to bid on ad placements to secure ad impressions. RTA technology not only improves ad display effectiveness but also helps advertisers target their ads more precisely.

[0004] Currently, there are some advertising delivery systems on the market based on the RTA (Real-Time Acquisition) delivery model, but these systems have some shortcomings. First, the application of RTA data packets and the distribution of traffic by the advertiser are disconnected, they do not support mutual referencing, lack the ability to target specific audiences, and cannot accurately deliver content according to the advertiser's needs. Second, audience segmentation is relatively coarse, simply judging whether an ad qualifies for bidding or not, lacking a refined strategy for configuring audience traffic distribution, and also failing to meet the advertiser's needs for data storage and management. Summary of the Invention

[0005] Based on this, an RTA data packet splitting method, apparatus, computer equipment, and storage medium are provided to solve the technical problems of current advertising delivery systems lacking the ability to target specific groups, failing to accurately deliver data according to advertisers' needs, having a relatively coarse audience segmentation, and failing to meet the data storage and management needs of advertisers.

[0006] On the one hand, a method for RTA packet splitting is provided, the method comprising:

[0007] Based on various usage scenarios, corresponding RTA data packets are generated, and the RTA data packets are classified and stored.

[0008] Multiple traffic splitting configuration strategies are generated by configuring traffic splitting, and filtering rules for the traffic splitting configuration strategies are set.

[0009] Upon receiving the data filled in with the filtering rules, the system calls the corresponding traffic splitting configuration strategy according to the filtering rules to generate traffic splitting recommendation requests for users.

[0010] Upon receiving a traffic splitting recommendation request, the traffic splitting configuration strategy is identified, and product parameters are obtained and added to the traffic splitting configuration strategy to form a traffic splitting scheme.

[0011] The RTA data packets stored in the category are called and the traffic splitting scheme is used to perform real-time user recommendation traffic splitting. According to the configured business dimensions, the user is subjected to filter hit and credential stuffing, and the traffic splitting result is returned.

[0012] In one embodiment, the step of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets includes:

[0013] Acquire user information data corresponding to various usage scenarios and form the user information data into corresponding RTA data packets;

[0014] According to various usage scenarios, corresponding file storage directories are built in the cloud server, and the RTA data packets are uploaded to the corresponding file storage directories in the cloud server;

[0015] Obtain the RTA data packet uploaded to the cloud server, and read and parse the RTA data packet;

[0016] The parsing results of the RTA data packets are stored in a Bloom filter. The RTA data packets are classified and stored according to preset filtering conditions, and corresponding Bloom filter key values ​​are generated from the classified RTA data packets.

[0017] In one embodiment, storing the parsing results of the RTA data packets in a Bloom filter, classifying and storing the RTA data packets according to preset filtering conditions, and generating corresponding Bloom filter key values ​​from the classified RTA data packets include:

[0018] The Bloom filter is configured as a probabilistic data structure consisting of a bit array and k hash functions. When the Bloom filter is initialized, all elements of the bit array are 0. When a target element is added to the bit array set, the target element is mapped to k points in the bit array through the corresponding hash function, and the elements corresponding to the k mapped points are set to 1.

[0019] The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter uses k hash functions to determine whether the user identifier in the parsing result of the RTA data packet is 1 at all k points;

[0020] If so, the RTA data packet is determined to meet the preset filtering conditions, the RTA data packet is saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​that meet the preset filtering conditions.

[0021] If not, the RTA data packet is determined not to meet the target filtering conditions. The RTA data packet is then saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​corresponding to those that do not meet the preset filtering conditions.

[0022] In one embodiment, the step of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets further includes:

[0023] When the RTA data packet is read, save the reading record of the RTA data packet and count the Bloom filter key value of the RTA data packet;

[0024] Configure a custom task to periodically scan the RTA data packets stored in the cloud server;

[0025] If the current RTA packet has a read record, it is determined that the current RTA packet has been processed, and the current RTA packet is skipped to proceed to the next RTA packet scan;

[0026] If there is no read record for the current RTA data packet, it is determined that the current RTA data packet has not been processed. The current RTA data packet is read and parsed, and the parsing result of the current RTA data packet is stored in a Bloom filter for classified storage.

[0027] In response to an update of the RTA data packet, the updated RTA data packet is reread and parsed. The parsing result of the updated RTA data packet is stored in a Bloom filter for classification and storage, and the Bloom filter key value of the statistically analyzed RTA data packet is updated.

[0028] In one embodiment, the step of configuring traffic splitting to form multiple traffic splitting configuration strategies and setting the filtering rules for the traffic splitting configuration strategies includes:

[0029] Setting up the traffic splitting configuration includes fixed configuration content, which is filled in by selection.

[0030] The branching configuration settings include adding new branch configuration content, which is added via the add method.

[0031] By filling in various traffic splitting configurations according to different business scenarios, a traffic splitting configuration strategy is formed for each type of traffic splitting configuration.

[0032] The filtering rules for setting the traffic splitting configuration strategy include usage scenarios, strategy types, recommended strategies, and fallback configuration strategies, wherein the recommended strategies include influence factors and the weights corresponding to the influence factors;

[0033] When the traffic splitting configuration strategy fails to meet usage requirements, the traffic splitting configuration strategy may be modified, removed, or deleted.

[0034] In one embodiment, the step of responding to the received data of the filter rules by invoking the corresponding traffic splitting configuration strategy to generate a traffic splitting recommendation request according to the filter rules includes:

[0035] Retrieve the fixed configuration content and newly added branch configuration content from the data of the filter rules filled in;

[0036] Determine whether there is a traffic splitting configuration strategy that satisfies the filtering rules;

[0037] If it exists, the user traffic will be diverted and a traffic recommendation request will be generated according to the traffic diversion configuration strategy that meets the filtering rules.

[0038] If it does not exist, the fallback configuration strategy will be used to generate a user traffic splitting recommendation request.

[0039] In one embodiment, the step of calling the RTA data packets stored in the categorized database and using the traffic splitting scheme to perform real-time user recommendation traffic splitting, and performing filter hits on users based on the configured business dimensions, and returning the traffic splitting results includes:

[0040] Determine whether to adopt the aforementioned traffic splitting scheme for real-time user recommendation traffic splitting;

[0041] If so, the RTA data packets stored in the category are retrieved through the RTA customer group interface;

[0042] Based on the configured business dimensions, set filters to determine whether the user identification information of the RTA data packet matches the filters;

[0043] If a match is found, the RTA data packet is determined to be an RTA customer group that meets the requirements of the traffic splitting scheme. After extracting the user information data of the RTA data packet, the traffic splitting result is returned using a recommendation sub-process.

[0044] If no match is found, the RTA data packet is determined not to meet the RTA customer group of the diversion scheme. After extracting the user information data of the RTA data packet, the diversion sub-process is used to perform hash diversion based on the user identification information and return the diversion result.

[0045] On the other hand, an RTA packet splitting device is provided, the device comprising:

[0046] The RTA data packet management module is used to generate corresponding RTA data packets according to various usage scenarios and to classify and store the RTA data packets.

[0047] The traffic splitting configuration module is used to perform traffic splitting configuration to form multiple traffic splitting configuration strategies, and to set the filtering rules for the traffic splitting configuration strategies;

[0048] The push configuration center module is used to respond to the data of the filter rules filled in, and to call the corresponding traffic splitting configuration strategy to split users and generate traffic splitting recommendation requests according to the filter rules.

[0049] The query flow distribution result module is used to respond to a received flow distribution recommendation request, identify the flow distribution configuration strategy adopted, obtain product parameters and add them to the flow distribution configuration strategy to form a flow distribution scheme;

[0050] The traffic splitting module is used to call the RTA data packets stored in categories and apply the traffic splitting scheme to perform real-time user recommendation traffic splitting. Based on the configured business dimensions, the module performs filter hits on users and returns the traffic splitting results.

[0051] In another aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0052] Based on various usage scenarios, corresponding RTA data packets are generated, and the RTA data packets are classified and stored.

[0053] Multiple traffic splitting configuration strategies are generated by configuring traffic splitting, and filtering rules for the traffic splitting configuration strategies are set.

[0054] Upon receiving the data filled in with the filtering rules, the system calls the corresponding traffic splitting configuration strategy according to the filtering rules to generate traffic splitting recommendation requests for users.

[0055] Upon receiving a traffic splitting recommendation request, the traffic splitting configuration strategy is identified, and product parameters are obtained and added to the traffic splitting configuration strategy to form a traffic splitting scheme.

[0056] The RTA data packets stored in the category are called and the traffic splitting scheme is used to perform real-time user recommendation traffic splitting. According to the configured business dimensions, the user is subjected to filter hit and credential stuffing, and the traffic splitting result is returned.

[0057] In another aspect, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0058] Based on various usage scenarios, corresponding RTA data packets are generated, and the RTA data packets are classified and stored.

[0059] Multiple traffic splitting configuration strategies are generated by configuring traffic splitting, and filtering rules for the traffic splitting configuration strategies are set.

[0060] Upon receiving the data filled in with the filtering rules, the system calls the corresponding traffic splitting configuration strategy according to the filtering rules to generate traffic splitting recommendation requests for users.

[0061] Upon receiving a traffic splitting recommendation request, the traffic splitting configuration strategy is identified, and product parameters are obtained and added to the traffic splitting configuration strategy to form a traffic splitting scheme.

[0062] The RTA data packets stored in the category are called and the traffic splitting scheme is used to perform real-time user recommendation traffic splitting. According to the configured business dimensions, the user is subjected to filter hit and credential stuffing, and the traffic splitting result is returned.

[0063] The aforementioned RTA data packet splitting method, apparatus, computer equipment, and storage medium pre-classify and store RTA data packets, allowing advertisers to configure splitting strategies to form multiple splitting strategies. They also set filtering rules for these strategies, selecting suitable ones, and adding product parameters to form a splitting scheme. The generated splitting scheme is then compared with the categorized and stored RTA data packets using a filter-based matching algorithm to return the splitting result. This allows for precise targeting based on advertisers' needs, refined audience traffic splitting based on product parameters, and storage and management of advertisers' splitting configuration strategies. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a diagram illustrating the application environment of an RTA packet splitting method in one embodiment of this application.

[0066] Figure 2 This is a flowchart illustrating an RTA packet splitting method in one embodiment of this application;

[0067] Figure 3 This is a flowchart illustrating the overall process of an RTA packet splitting method in one embodiment of this application.

[0068] Figure 4This is a flowchart illustrating the steps of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets in one embodiment of this application.

[0069] Figure 5 This is a schematic diagram illustrating the steps of storing the parsing results of the RTA data packets in a Bloom filter, classifying and storing the RTA data packets according to preset filtering conditions, and generating corresponding Bloom filter key values ​​from the classified RTA data packets in one embodiment of this application.

[0070] Figure 6 This is a flowchart illustrating the steps of configuring traffic splitting to form multiple traffic splitting configuration strategies and setting the filtering rules for the traffic splitting configuration strategies in one embodiment of this application.

[0071] Figure 7 This is a flowchart illustrating the steps of generating a user traffic recommendation request by calling the corresponding traffic splitting configuration strategy according to the filtering rules when receiving the data filled in by the filtering rules in one embodiment of this application.

[0072] Figure 8 This is a flowchart illustrating the process of setting a filter based on a configured service dimension in one embodiment of the present application to determine whether the user identification information of the RTA data packet matches the filter.

[0073] Figure 9 This is a structural block diagram of an RTA data packet splitting device in one embodiment of this application;

[0074] Figure 10 This is an internal structural diagram of a computer device in one embodiment of this application. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0076] The RTA packet splitting method provided in this application can be applied to, for example... Figure 1In the application environment shown, the advertiser terminal 101 and the media advertising platform 102 communicate with the marketing system server 103 via a network. Both the advertiser terminal 101 and the media advertising platform 102 upload data of marketing users to be identified to the marketing system server 103. The marketing system server 103 identifies and analyzes the uploaded data and provides feedback on marketable user data. The advertiser terminal 101 and the media advertising platform 102 conduct promotional marketing based on the marketable user data fed back by the marketing system server 103. The promotional marketing methods include sending advertising information or pushing marketing content. The advertiser terminal 101 and the media advertising platform 102 can be, but are not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The media advertising platform 102 includes servers such as Toutiao and Tencent. The advertiser terminal 101, the media advertising platform 102, and the marketing system server 103 can be implemented using independent servers or a server cluster composed of multiple servers.

[0077] In one embodiment, such as Figure 2 , Figure 3 As shown, an RTA packet splitting method is provided, which is applied to... Figure 1 Taking the marketing system server 103 as an example, the following steps are included:

[0078] Step S1: Generate corresponding RTA data packets according to various usage scenarios, and classify and store the RTA data packets.

[0079] Step S2: Configure traffic splitting to form multiple traffic splitting configuration strategies, and set the filtering rules for the traffic splitting configuration strategies;

[0080] Step S3: In response to receiving the data of the filter rules filled in, the corresponding traffic splitting configuration strategy is invoked according to the filter rules to generate traffic splitting recommendation requests for users.

[0081] Step S4: In response to receiving a traffic splitting recommendation request, identify the traffic splitting configuration strategy adopted, obtain the product parameters and add them to the traffic splitting configuration strategy to form a traffic splitting scheme;

[0082] Step S5: The RTA data packets stored in the classification are called and the traffic splitting scheme is used to perform real-time user recommendation traffic splitting. According to the configured business dimensions, the user is subjected to filter hit and credential stuffing, and the traffic splitting result is returned.

[0083] Specifically, by pre-classifying and storing RTA data packets in the marketing system server 103, advertisers can configure traffic splitting to form multiple traffic splitting strategies and set filtering rules for these strategies. Suitable traffic splitting strategies are then selected, and product parameters are added to the traffic splitting strategies to form a traffic splitting scheme. The generated traffic splitting scheme is then compared with the classified and stored RTA data packets to perform a filter hit and credential matching, and the traffic splitting result is returned. This allows for precise targeting based on advertisers' needs, refined traffic splitting based on product parameters, and storage and management of advertisers' traffic splitting configuration strategies.

[0084] This embodiment can be flexibly and easily configured according to the needs of the advertiser, thereby achieving more efficient and accurate targeting. At the same time, it provides fast and low-cost data storage and management functions, helping advertisers better manage their campaign data.

[0085] like Figure 4 As shown, in this embodiment, the step of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets includes:

[0086] Step S11: Obtain user information data corresponding to various usage scenarios, and form the user information data into corresponding RTA data packets;

[0087] Step S12: Based on various usage scenarios, construct corresponding file storage directories in the cloud server and upload the RTA data packets to the corresponding file storage directories in the cloud server.

[0088] Step S13: Obtain the RTA data packet uploaded to the cloud server, and read and parse the RTA data packet;

[0089] Step S14: Store the parsing results of the RTA data packets in a Bloom filter, classify and store the RTA data packets according to preset filtering conditions, and generate corresponding Bloom filter key values ​​for the classified RTA data packets.

[0090] like Figure 3 As shown, it provides users with highly flexible configuration options to achieve the desired advertising results. Users can select appropriate RTA data packages and formulate rule strategies tailored to the characteristics of the current target audience to achieve more refined traffic operations. While ensuring system real-time performance, it maximizes traffic value and improves advertising effectiveness, cost recovery, and ROI.

[0091] Preferably, the cloud server is an OSS file server. The OSS file server can be set up in the marketing system server 103 or an access link can be established between the OSS file server and the marketing system server 103.

[0092] Understandably, RTA data packets are periodically uploaded to the OSS file server by the RTA post-management system. The RTA post-management system then scans and parses them, generating corresponding data packet parsing records based on the different types of RTA data packets and recording them in the database. It also generates corresponding Bloom filter key values. Each reading of an RTA data packet has a corresponding reading record, ensuring that each reading of an RTA data packet is traceable and smoothly transitions.

[0093] like Figure 5 As shown, in this embodiment, storing the parsing results of the RTA data packets in a Bloom filter, classifying and storing the RTA data packets according to preset filtering conditions, and generating corresponding Bloom filter key values ​​from the classified RTA data packets include:

[0094] The Bloom filter is configured as a probabilistic data structure consisting of a bit array and k hash functions. When the Bloom filter is initialized, all elements of the bit array are 0. When a target element is added to the bit array set, the target element is mapped to k points in the bit array through the corresponding hash function, and the elements corresponding to the k mapped points are set to 1.

[0095] The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter uses k hash functions to determine whether the user identifier in the parsing result of the RTA data packet is 1 at all k points;

[0096] If so, the RTA data packet is determined to meet the preset filtering conditions, the RTA data packet is saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​that meet the preset filtering conditions.

[0097] If not, the RTA data packet is determined not to meet the target filtering conditions. The RTA data packet is then saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​corresponding to those that do not meet the preset filtering conditions.

[0098] like Figure 5 As shown, the parsing results of RTA packets are hashed into a Bloom filter to reduce data storage space. The advantage of a Bloom filter is that it is a probabilistic data structure with high space utilization. Preferably, the database is Redis, a high-performance key-value database that supports various data structures, including strings, lists, and sets, and is completely open-source and free.

[0099] The advantages of using a Bloom filter are: very small space footprint, as the presence of an element is indicated by bits; no need to store the data itself, providing excellent confidentiality; the complexity of inserting and querying elements is O(k), where k represents the number of times the hash function is executed, and each of the k hash functions is executed once; and high performance, as the atomicity and high performance of database operations greatly improve service performance.

[0100] When querying whether the RTA data packet meets the preset filtering conditions, k hash functions are used to determine whether the requested user identifier information (preferred device number) is 1 in all k positions. If the conditions are not met, the requesting user does not meet the data packet filtering conditions, which can be used as a basic condition for judging user traffic. Furthermore, the user information data of the RTA data packet (if it exists) will be cached in the database to provide a user data foundation for subsequent accurate recommendations.

[0101] In this embodiment, the step of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets further includes:

[0102] When the RTA data packet is read, save the reading record of the RTA data packet and count the Bloom filter key value of the RTA data packet;

[0103] Configure a custom task to periodically scan the RTA data packets stored in the cloud server;

[0104] If the current RTA packet has a read record, it is determined that the current RTA packet has been processed, and the current RTA packet is skipped to proceed to the next RTA packet scan;

[0105] If there is no read record for the current RTA data packet, it is determined that the current RTA data packet has not been processed. The current RTA data packet is read and parsed, and the parsing result of the current RTA data packet is stored in a Bloom filter for classified storage.

[0106] In response to an update of the RTA data packet, the updated RTA data packet is reread and parsed. The parsing result of the updated RTA data packet is stored in a Bloom filter for classification and storage, and the Bloom filter key value of the statistically analyzed RTA data packet is updated.

[0107] This includes configuring customized tasks for scheduled scanning. When scanning RTA data packets, reading rules can be flexibly configured according to business requirements, and the Bloom filter error rate and data volume of the data packet can be specified.

[0108] After parsing the RTA data packets, the data is stored in a Bloom filter, and the data records are updated to refresh the latest Bloom filter key values ​​for the RTA data packets. The subsequent traffic splitting configuration page can then read the parsed data packets in real time and perform traffic splitting according to specified proportions based on business requirements or recommending based on user information.

[0109] like Figure 6 As shown, in this embodiment, the steps of configuring traffic splitting to form multiple traffic splitting configuration strategies and setting the filtering rules for the traffic splitting configuration strategies include:

[0110] Step S21: Set the traffic splitting configuration, which includes fixed configuration content. The fixed configuration content is filled in by selection and includes distribution ID, distribution name, distribution product and distribution result type.

[0111] Step S22: Set the branch configuration to include new branch configuration content, which is added by adding new content;

[0112] Step S23: By filling in various traffic splitting configurations according to various business scenarios, a traffic splitting configuration strategy is formed for each type of traffic splitting configuration;

[0113] Step S24: Set the filtering rules for the traffic splitting configuration strategy, including usage scenarios, strategy types, recommended strategies, and fallback configuration strategies, wherein the recommended strategies include influence factors and the weights corresponding to the influence factors;

[0114] Step S25: In response to the traffic splitting configuration strategy not meeting the usage requirements, modify, remove, or delete the traffic splitting configuration strategy.

[0115] Based on the business requirements, corresponding rules can be configured on the traffic splitting configuration page. Custom distribution IDs can be created and used in subsequent delivery pages to match the corresponding strategies. Furthermore, business users can specify the distribution dimensions of the distribution strategy and flexibly expand and add branch configuration content as needed. When configuring distribution strategies, the system also supports setting a fallback strategy to handle situations where the user is not present in the data packet; the system will perform hash-based traffic splitting based on the currently configured fallback strategy.

[0116] Alternatively, traffic splitting configuration strategies can be identified by selecting RTA data packets. Once an RTA data packet is selected, the business unit can configure the corresponding distribution strategy (i.e., traffic splitting configuration) according to specific requirements. The distribution strategy can be customized according to different business parameter dimensions. For example, parameter dimensions include, but are not limited to, user purchase history, user browsing history, and user basic attributes. The distribution strategy is submitted to the recommendation rule engine for further judgment to achieve more refined user traffic splitting and recommendation.

[0117] Once the traffic splitting configuration strategy is configured, it can be deployed and flexibly deployed. When a strategy is deployed, the configuration information is pushed to a configuration center, which can be ZooKeeper, Neptune, Nacos, etc. This decouples the configuration backend from the business system, improving the efficiency of the business system in reading configurations and service performance (including but not limited to interface timeliness, concurrency, and service stability). Only deployed strategies will take effect. Expired or no longer needed strategies can be deployed promptly. After modification, to avoid data misoperation affecting production operations, a fault-free check must be performed before deployment.

[0118] like Figure 7 As shown, in this embodiment, the step of responding to the received data of the filter rules by calling the corresponding traffic splitting configuration strategy to generate a traffic splitting recommendation request according to the filter rules includes:

[0119] Step S31: Obtain the fixed configuration content and the newly added branch configuration content from the data of the filter rules filled in;

[0120] Step S32: Determine whether there is a traffic splitting configuration strategy that satisfies the filtering rules;

[0121] Step S33: If the condition exists, the user traffic will be diverted and a traffic recommendation request will be generated using the traffic diversion configuration strategy that satisfies the filtering rules; if the condition does not exist, the user traffic will be diverted and a traffic recommendation request will be generated using the fallback configuration strategy.

[0122] Please see Figure 3 In this embodiment, the step of calling the RTA data packets stored in the classification system and using the traffic splitting scheme for real-time user recommendation traffic splitting, performing filter hits on users based on the configured business dimensions, and returning the traffic splitting results includes:

[0123] Determine whether to adopt the aforementioned traffic splitting scheme for real-time user recommendation traffic splitting;

[0124] If so, the RTA data packets stored in the category are retrieved through the RTA customer group interface;

[0125] Based on the configured business dimensions, set filters to determine whether the user identification information of the RTA data packet matches the filters;

[0126] If a match is found, the RTA data packet is determined to be an RTA customer group that meets the requirements of the traffic splitting scheme. After extracting the user information data of the RTA data packet, the traffic splitting result is returned using a recommendation sub-process.

[0127] If no match is found, the RTA data packet is determined not to meet the RTA customer group of the diversion scheme. After extracting the user information data of the RTA data packet, the diversion sub-process is used to perform hash diversion based on the user identification information and return the diversion result.

[0128] Preferably, the filter is a Bloom filter. The method of setting the filter according to the configured business dimension and determining whether the user identification information of the RTA data packet hits the filter is the same as or similar to the previous description of storing the parsing result of the RTA data packet in the Bloom filter, classifying and storing the RTA data packet according to preset filtering conditions, and generating the corresponding Bloom filter key value of the classified RTA data packet. This will not be elaborated here.

[0129] Preferably, the user identification information is a device number. For example... Figure 8 As shown, the method for determining whether the user identification information of the RTA data packet matches the filter based on the configured business dimension is as follows: For RTA customer groups that meet the traffic splitting scheme, corresponding products can be recommended to RTA users based on the recommendation sub-process; for RTA customer groups that do not meet the traffic splitting scheme, the device number can be used as the traffic splitting identifier based on the traffic splitting sub-process, and the traffic splitting result of the corresponding recommended product can be returned using hash traffic splitting. The hash traffic splitting is calculated as Hash()%100.

[0130] Based on the traffic distribution results, the data dashboard displays all traffic distribution statistics to the advertiser, including daily totals, distribution of traffic distribution results, and percentage of traffic distribution results.

[0131] In the above-mentioned RTA data packet splitting method, RTA data packets are first pre-classified and stored. Advertisers then configure splitting to form multiple splitting strategies and set filtering rules for these strategies. Suitable splitting strategies are selected, and product parameters are added to the splitting strategies to form a splitting scheme. The generated splitting scheme is then compared with the classified and stored RTA data packets to perform a filter hit and credential matching, and the splitting result is returned. This method enables precise targeting based on the advertiser's needs, refined splitting of audience traffic based on product parameters, and storage and management of the advertiser's splitting configuration strategies.

[0132] It should be understood that, although Figures 2-8 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figures 2-8At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0133] In one embodiment, such as Figure 9 As shown, an RTA data packet splitting device 10 is provided, including: an RTA data packet management module 1, a splitting configuration module 2, a push configuration center module 3, a splitting result query module 4, and a splitting processing module 5.

[0134] The RTA data packet management module 1 is used to generate corresponding RTA data packets according to various usage scenarios, and to classify and store the RTA data packets.

[0135] The traffic splitting configuration module 2 is used to perform traffic splitting configuration to form multiple traffic splitting configuration strategies, and to set the filtering rules for the traffic splitting configuration strategies.

[0136] The push configuration center module 3 is used to respond to the data of the filter rules filled in, and to call the corresponding traffic splitting configuration strategy to generate traffic splitting recommendation requests according to the filter rules.

[0137] The query result module 4 is used to respond to the received request for traffic splitting recommendation, identify the traffic splitting configuration strategy adopted, obtain the product parameters and add them to the traffic splitting configuration strategy to form a traffic splitting scheme.

[0138] The traffic splitting module 5 is used to call the RTA data packets stored in the classification and use the traffic splitting scheme to perform real-time user recommendation traffic splitting. According to the configured business dimensions, the user is subjected to filter hit and credential stuffing, and the traffic splitting result is returned.

[0139] In this embodiment, the step of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets includes:

[0140] Acquire user information data corresponding to various usage scenarios and form the user information data into corresponding RTA data packets;

[0141] According to various usage scenarios, corresponding file storage directories are built in the cloud server, and the RTA data packets are uploaded to the corresponding file storage directories in the cloud server;

[0142] Obtain the RTA data packet uploaded to the cloud server, and read and parse the RTA data packet;

[0143] The parsing results of the RTA data packets are stored in a Bloom filter. The RTA data packets are classified and stored according to preset filtering conditions, and corresponding Bloom filter key values ​​are generated from the classified RTA data packets.

[0144] In this embodiment, storing the parsing results of the RTA data packets in a Bloom filter, classifying and storing the RTA data packets according to preset filtering conditions, and generating corresponding Bloom filter key values ​​from the classified RTA data packets include:

[0145] The Bloom filter is configured as a probabilistic data structure consisting of a bit array and k hash functions. When the Bloom filter is initialized, all elements of the bit array are 0. When a target element is added to the bit array set, the target element is mapped to k points in the bit array through the corresponding hash function, and the elements corresponding to the k mapped points are set to 1.

[0146] The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter uses k hash functions to determine whether the user identifier in the parsing result of the RTA data packet is 1 at all k points;

[0147] If so, the RTA data packet is determined to meet the preset filtering conditions, the RTA data packet is saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​that meet the preset filtering conditions.

[0148] If not, the RTA data packet is determined not to meet the target filtering conditions. The RTA data packet is then saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​corresponding to those that do not meet the preset filtering conditions.

[0149] In this embodiment, the step of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets further includes:

[0150] When the RTA data packet is read, save the reading record of the RTA data packet and count the Bloom filter key value of the RTA data packet;

[0151] Configure a custom task to periodically scan the RTA data packets stored in the cloud server;

[0152] If the current RTA packet has a read record, it is determined that the current RTA packet has been processed, and the current RTA packet is skipped to proceed to the next RTA packet scan;

[0153] If there is no read record for the current RTA data packet, it is determined that the current RTA data packet has not been processed. The current RTA data packet is read and parsed, and the parsing result of the current RTA data packet is stored in a Bloom filter for classified storage.

[0154] In response to an update of the RTA data packet, the updated RTA data packet is reread and parsed. The parsing result of the updated RTA data packet is stored in a Bloom filter for classification and storage, and the Bloom filter key value of the statistically analyzed RTA data packet is updated.

[0155] In this embodiment, the steps of configuring traffic splitting to form multiple traffic splitting configuration strategies and setting the filtering rules for the traffic splitting configuration strategies include:

[0156] Setting up the traffic splitting configuration includes fixed configuration content, which is filled in by selection. The fixed configuration content includes distribution ID, distribution name, distribution product, and distribution result type.

[0157] The branching configuration settings include adding new branch configuration content, which is added via the add method.

[0158] By filling in various traffic splitting configurations according to different business scenarios, a traffic splitting configuration strategy is formed for each type of traffic splitting configuration.

[0159] The filtering rules for setting the traffic splitting configuration strategy include usage scenarios, strategy types, recommended strategies, and fallback configuration strategies, wherein the recommended strategies include influence factors and the weights corresponding to the influence factors;

[0160] When the traffic splitting configuration strategy fails to meet usage requirements, the traffic splitting configuration strategy may be modified, removed, or deleted.

[0161] In this embodiment, the step of responding to the received data of the filter rules by calling the corresponding traffic splitting configuration strategy to generate a traffic splitting recommendation request according to the filter rules includes:

[0162] Retrieve the fixed configuration content and newly added branch configuration content from the data of the filter rules filled in;

[0163] Determine whether there is a traffic splitting configuration strategy that satisfies the filtering rules;

[0164] If it exists, the user traffic will be diverted and a traffic recommendation request will be generated according to the traffic diversion configuration strategy that meets the filtering rules.

[0165] If it does not exist, the fallback configuration strategy will be used to generate a user traffic splitting recommendation request.

[0166] In this embodiment, the step of calling the RTA data packets stored in the classification system and using the traffic splitting scheme for real-time user recommendation traffic splitting, performing filter hits on users based on the configured business dimensions, and returning the traffic splitting results includes:

[0167] Determine whether to adopt the aforementioned traffic splitting scheme for real-time user recommendation traffic splitting;

[0168] If so, the RTA data packets stored in the category are retrieved through the RTA customer group interface;

[0169] Based on the configured business dimensions, set filters to determine whether the user identification information of the RTA data packet matches the filters;

[0170] If a match is found, the RTA data packet is determined to be an RTA customer group that meets the requirements of the traffic splitting scheme. After extracting the user information data of the RTA data packet, the traffic splitting result is returned using a recommendation sub-process.

[0171] If no match is found, the RTA data packet is determined not to meet the RTA customer group of the diversion scheme. After extracting the user information data of the RTA data packet, the diversion sub-process is used to perform hash diversion based on the user identification information and return the diversion result.

[0172] In the aforementioned RTA data packet splitting device, RTA data packets are pre-classified and stored. Advertisers then configure splitting strategies to form multiple splitting strategies and set filtering rules for these strategies. Suitable splitting strategies are selected, and product parameters are added to the splitting strategies to form a splitting scheme. The generated splitting scheme is then compared with the classified and stored RTA data packets using a filter hit database, and the splitting result is returned. This allows for precise targeting based on the advertiser's needs, refined splitting of audience traffic based on product parameters, and storage and management of the advertiser's splitting configuration strategies.

[0173] Specific limitations regarding the RTA packet splitting device can be found in the limitations of the RTA packet splitting method described above, and will not be repeated here. Each module in the aforementioned RTA packet splitting device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the computer device, or stored in software in the memory of the computer device, so that the processor can call and execute the corresponding operations of each module.

[0174] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0175] Based on various usage scenarios, corresponding RTA data packets are generated, and the RTA data packets are classified and stored.

[0176] Multiple traffic splitting configuration strategies are generated by configuring traffic splitting, and filtering rules for the traffic splitting configuration strategies are set.

[0177] Upon receiving the data filled in with the filtering rules, the system calls the corresponding traffic splitting configuration strategy according to the filtering rules to generate traffic splitting recommendation requests for users.

[0178] Upon receiving a traffic splitting recommendation request, the traffic splitting configuration strategy is identified, and product parameters are obtained and added to the traffic splitting configuration strategy to form a traffic splitting scheme.

[0179] The RTA data packets stored in the category are called and the traffic splitting scheme is used to perform real-time user recommendation traffic splitting. According to the configured business dimensions, the user is subjected to filter hit and credential stuffing, and the traffic splitting result is returned.

[0180] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0181] The steps of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets include:

[0182] Acquire user information data corresponding to various usage scenarios and form the user information data into corresponding RTA data packets;

[0183] According to various usage scenarios, corresponding file storage directories are built in the cloud server, and the RTA data packets are uploaded to the corresponding file storage directories in the cloud server;

[0184] Obtain the RTA data packets uploaded to the cloud server, and read and parse the RTA data packets;

[0185] The parsing results of the RTA data packets are stored in a Bloom filter. The RTA data packets are classified and stored according to preset filtering conditions, and corresponding Bloom filter key values ​​are generated from the classified RTA data packets.

[0186] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0187] The step of storing the parsing results of the RTA data packets in a Bloom filter, classifying and storing the RTA data packets according to preset filtering conditions, and generating corresponding Bloom filter key values ​​for the classified RTA data packets includes:

[0188] The Bloom filter is configured as a probabilistic data structure consisting of a bit array and k hash functions. When the Bloom filter is initialized, all elements of the bit array are 0. When a target element is added to the bit array set, the target element is mapped to k points in the bit array through the corresponding hash function, and the elements corresponding to the k mapped points are set to 1.

[0189] The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter uses k hash functions to determine whether the user identifier in the parsing result of the RTA data packet is 1 at all k points;

[0190] If so, the RTA data packet is determined to meet the preset filtering conditions, the RTA data packet is saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​that meet the preset filtering conditions.

[0191] If not, the RTA data packet is determined not to meet the target filtering conditions. The RTA data packet is then saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​corresponding to those that do not meet the preset filtering conditions.

[0192] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0193] The step of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets further includes:

[0194] When the RTA data packet is read, save the reading record of the RTA data packet and count the Bloom filter key value of the RTA data packet;

[0195] Configure a custom task to periodically scan the RTA data packets stored in the cloud server;

[0196] If the current RTA packet has a read record, it is determined that the current RTA packet has been processed, and the current RTA packet is skipped to proceed to the next RTA packet scan;

[0197] If there is no read record for the current RTA data packet, it is determined that the current RTA data packet has not been processed. The current RTA data packet is read and parsed, and the parsing result of the current RTA data packet is stored in a Bloom filter for classified storage.

[0198] In response to an update of the RTA data packet, the updated RTA data packet is reread and parsed. The parsing result of the updated RTA data packet is stored in a Bloom filter for classification and storage, and the Bloom filter key value of the statistically analyzed RTA data packet is updated.

[0199] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0200] The steps of configuring traffic splitting to form multiple traffic splitting configuration strategies and setting the filtering rules for the traffic splitting configuration strategies include:

[0201] Setting up the traffic splitting configuration includes fixed configuration content, which is filled in by selection. The fixed configuration content includes distribution ID, distribution name, distribution product, and distribution result type.

[0202] The branching configuration settings include adding new branch configuration content, which is added via the add method.

[0203] By filling in various traffic splitting configurations according to different business scenarios, a traffic splitting configuration strategy is formed for each type of traffic splitting configuration.

[0204] The filtering rules for setting the traffic splitting configuration strategy include usage scenarios, strategy types, recommended strategies, and fallback configuration strategies, wherein the recommended strategies include influence factors and the weights corresponding to the influence factors;

[0205] When the traffic splitting configuration strategy fails to meet usage requirements, the traffic splitting configuration strategy may be modified, removed, or deleted.

[0206] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0207] The step of responding to the data of the filter rule being filled in, and then invoking the corresponding traffic splitting configuration strategy to generate a traffic splitting recommendation request according to the filter rule, includes:

[0208] Retrieve the fixed configuration content and newly added branch configuration content from the data of the filter rules filled in;

[0209] Determine whether there is a traffic splitting configuration strategy that satisfies the filtering rules;

[0210] If it exists, the user traffic will be diverted and a traffic recommendation request will be generated according to the traffic diversion configuration strategy that meets the filtering rules.

[0211] If it does not exist, the fallback configuration strategy will be used to generate a user traffic splitting recommendation request.

[0212] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0213] The steps of calling the RTA data packets stored in the categorized database and using the aforementioned traffic splitting scheme for real-time user recommendation, including filtering users based on the configured business dimensions and returning the traffic splitting results, include:

[0214] Determine whether to adopt the aforementioned traffic splitting scheme for real-time user recommendation traffic splitting;

[0215] If so, the RTA data packets stored in the category are retrieved through the RTA customer group interface;

[0216] Based on the configured business dimensions, set filters to determine whether the user identification information of the RTA data packet matches the filters;

[0217] If a match is found, the RTA data packet is determined to be an RTA customer group that meets the requirements of the traffic splitting scheme. After extracting the user information data of the RTA data packet, the traffic splitting result is returned using a recommendation sub-process.

[0218] If no match is found, the RTA data packet is determined not to meet the RTA customer group of the diversion scheme. After extracting the user information data of the RTA data packet, the diversion sub-process is used to perform hash diversion based on the user identification information and return the diversion result.

[0219] For specific limitations on the steps implemented when a computer program is executed by a processor, please refer to the limitations on the RTA packet splitting method mentioned above, which will not be repeated here.

[0220] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and the database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores RTA packet splitting data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements an RTA packet splitting method.

[0221] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0222] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0223] Based on various usage scenarios, corresponding RTA data packets are generated, and the RTA data packets are classified and stored.

[0224] Multiple traffic splitting configuration strategies are generated by configuring traffic splitting, and filtering rules for the traffic splitting configuration strategies are set.

[0225] Upon receiving the data filled in with the filtering rules, the system calls the corresponding traffic splitting configuration strategy according to the filtering rules to generate traffic splitting recommendation requests for users.

[0226] Upon receiving a traffic splitting recommendation request, the traffic splitting configuration strategy is identified, and product parameters are obtained and added to the traffic splitting configuration strategy to form a traffic splitting scheme.

[0227] The RTA data packets stored in the category are called and the traffic splitting scheme is used to perform real-time user recommendation traffic splitting. According to the configured business dimensions, the user is subjected to filter hit and credential stuffing, and the traffic splitting result is returned.

[0228] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0229] The steps of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets include:

[0230] Acquire user information data corresponding to various usage scenarios and form the user information data into corresponding RTA data packets;

[0231] According to various usage scenarios, corresponding file storage directories are built in the cloud server, and the RTA data packets are uploaded to the corresponding file storage directories in the cloud server;

[0232] Obtain the RTA data packets uploaded to the cloud server, and read and parse the RTA data packets;

[0233] The parsing results of the RTA data packets are stored in a Bloom filter. The RTA data packets are classified and stored according to preset filtering conditions, and corresponding Bloom filter key values ​​are generated from the classified RTA data packets.

[0234] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0235] The step of storing the parsing results of the RTA data packets in a Bloom filter, classifying and storing the RTA data packets according to preset filtering conditions, and generating corresponding Bloom filter key values ​​for the classified RTA data packets includes:

[0236] The Bloom filter is configured as a probabilistic data structure consisting of a bit array and k hash functions. When the Bloom filter is initialized, all elements of the bit array are 0. When a target element is added to the bit array set, the target element is mapped to k points in the bit array through the corresponding hash function, and the elements corresponding to the k mapped points are set to 1.

[0237] The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter uses k hash functions to determine whether the user identifier in the parsing result of the RTA data packet is 1 at all k points;

[0238] If so, the RTA data packet is determined to meet the preset filtering conditions, the RTA data packet is saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​that meet the preset filtering conditions.

[0239] If not, the RTA data packet is determined not to meet the target filtering conditions. The RTA data packet is then saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​corresponding to those that do not meet the preset filtering conditions.

[0240] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0241] The step of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets further includes:

[0242] When the RTA data packet is read, save the reading record of the RTA data packet and count the Bloom filter key value of the RTA data packet;

[0243] Configure a custom task to periodically scan the RTA data packets stored in the cloud server;

[0244] If the current RTA packet has a read record, it is determined that the current RTA packet has been processed, and the current RTA packet is skipped to proceed to the next RTA packet scan;

[0245] If there is no read record for the current RTA data packet, it is determined that the current RTA data packet has not been processed. The current RTA data packet is read and parsed, and the parsing result of the current RTA data packet is stored in a Bloom filter for classified storage.

[0246] In response to an update of the RTA data packet, the updated RTA data packet is reread and parsed. The parsing result of the updated RTA data packet is stored in a Bloom filter for classification and storage, and the Bloom filter key value of the statistically analyzed RTA data packet is updated.

[0247] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0248] The steps of configuring traffic splitting to form multiple traffic splitting configuration strategies and setting the filtering rules for the traffic splitting configuration strategies include:

[0249] Setting up the traffic splitting configuration includes fixed configuration content, which is filled in by selection. The fixed configuration content includes distribution ID, distribution name, distribution product, and distribution result type.

[0250] The branching configuration settings include adding new branch configuration content, which is added via the add method.

[0251] By filling in various traffic splitting configurations according to different business scenarios, a traffic splitting configuration strategy is formed for each type of traffic splitting configuration.

[0252] The filtering rules for setting the traffic splitting configuration strategy include usage scenarios, strategy types, recommended strategies, and fallback configuration strategies, wherein the recommended strategies include influence factors and the weights corresponding to the influence factors;

[0253] When the traffic splitting configuration strategy fails to meet usage requirements, the traffic splitting configuration strategy may be modified, removed, or deleted.

[0254] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0255] The step of responding to the data of the filter rule being filled in, and then invoking the corresponding traffic splitting configuration strategy to generate a traffic splitting recommendation request according to the filter rule, includes:

[0256] Retrieve the fixed configuration content and newly added branch configuration content from the data of the filter rules filled in;

[0257] Determine whether there is a traffic splitting configuration strategy that satisfies the filtering rules;

[0258] If it exists, the user traffic will be diverted and a traffic recommendation request will be generated according to the traffic diversion configuration strategy that meets the filtering rules.

[0259] If it does not exist, the fallback configuration strategy will be used to generate a user traffic splitting recommendation request.

[0260] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0261] The steps of calling the RTA data packets stored in the categorized database and using the aforementioned traffic splitting scheme for real-time user recommendation, including filtering users based on the configured business dimensions and returning the traffic splitting results, include:

[0262] Determine whether to adopt the aforementioned traffic splitting scheme for real-time user recommendation traffic splitting;

[0263] If so, the RTA data packets stored in the category are retrieved through the RTA customer group interface;

[0264] Based on the configured business dimensions, set filters to determine whether the user identification information of the RTA data packet matches the filters;

[0265] If a match is found, the RTA data packet is determined to be an RTA customer group that meets the requirements of the traffic splitting scheme. After extracting the user information data of the RTA data packet, the traffic splitting result is returned using a recommendation sub-process.

[0266] If no match is found, the RTA data packet is determined not to meet the RTA customer group of the diversion scheme. After extracting the user information data of the RTA data packet, the diversion sub-process is used to perform hash diversion based on the user identification information and return the diversion result.

[0267] For specific limitations on the steps a processor takes when executing a computer program, please refer to the limitations on the RTA packet splitting method mentioned above, which will not be repeated here.

[0268] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0269] Based on various usage scenarios, corresponding RTA data packets are generated, and the RTA data packets are classified and stored.

[0270] Multiple traffic splitting configuration strategies are generated by configuring traffic splitting, and filtering rules for the traffic splitting configuration strategies are set.

[0271] Upon receiving the data filled in with the filtering rules, the system calls the corresponding traffic splitting configuration strategy according to the filtering rules to generate traffic splitting recommendation requests for users.

[0272] Upon receiving a traffic splitting recommendation request, the traffic splitting configuration strategy is identified, and product parameters are obtained and added to the traffic splitting configuration strategy to form a traffic splitting scheme.

[0273] The RTA data packets stored in the category are called and the traffic splitting scheme is used to perform real-time user recommendation traffic splitting. According to the configured business dimensions, the user is subjected to filter hit and credential stuffing, and the traffic splitting result is returned.

[0274] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0275] The steps of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets include:

[0276] Acquire user information data corresponding to various usage scenarios and form the user information data into corresponding RTA data packets;

[0277] According to various usage scenarios, corresponding file storage directories are built in the cloud server, and the RTA data packets are uploaded to the corresponding file storage directories in the cloud server;

[0278] Obtain the RTA data packet uploaded to the cloud server, and read and parse the RTA data packet;

[0279] The parsing results of the RTA data packets are stored in a Bloom filter. The RTA data packets are classified and stored according to preset filtering conditions, and corresponding Bloom filter key values ​​are generated from the classified RTA data packets.

[0280] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0281] The step of storing the parsing results of the RTA data packets in a Bloom filter, classifying and storing the RTA data packets according to preset filtering conditions, and generating corresponding Bloom filter key values ​​for the classified RTA data packets includes:

[0282] The Bloom filter is configured as a probabilistic data structure consisting of a bit array and k hash functions. When the Bloom filter is initialized, all elements of the bit array are 0. When a target element is added to the bit array set, the target element is mapped to k points in the bit array through the corresponding hash function, and the elements corresponding to the k mapped points are set to 1.

[0283] The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter uses k hash functions to determine whether the user identifier in the parsing result of the RTA data packet is 1 at all k points;

[0284] If so, the RTA data packet is determined to meet the preset filtering conditions, the RTA data packet is saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​that meet the preset filtering conditions.

[0285] If not, the RTA data packet is determined not to meet the target filtering conditions. The RTA data packet is then saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​corresponding to those that do not meet the preset filtering conditions.

[0286] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0287] The step of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets further includes:

[0288] When the RTA data packet is read, save the reading record of the RTA data packet and count the Bloom filter key value of the RTA data packet;

[0289] Configure a custom task to periodically scan the RTA data packets stored in the cloud server;

[0290] If the current RTA packet has a read record, it is determined that the current RTA packet has been processed, and the current RTA packet is skipped to proceed to the next RTA packet scan;

[0291] If there is no read record for the current RTA data packet, it is determined that the current RTA data packet has not been processed. The current RTA data packet is read and parsed, and the parsing result of the current RTA data packet is stored in a Bloom filter for classified storage.

[0292] In response to an update of the RTA data packet, the updated RTA data packet is reread and parsed. The parsing result of the updated RTA data packet is stored in a Bloom filter for classification and storage, and the Bloom filter key value of the statistically analyzed RTA data packet is updated.

[0293] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0294] The steps of configuring traffic splitting to form multiple traffic splitting configuration strategies and setting the filtering rules for the traffic splitting configuration strategies include:

[0295] Setting up the traffic splitting configuration includes fixed configuration content, which is filled in by selection. The fixed configuration content includes distribution ID, distribution name, distribution product, and distribution result type.

[0296] The branching configuration settings include adding new branch configuration content, which is added via the add method.

[0297] By filling in various traffic splitting configurations according to different business scenarios, a traffic splitting configuration strategy is formed for each type of traffic splitting configuration.

[0298] The filtering rules for setting the traffic splitting configuration strategy include usage scenarios, strategy types, recommended strategies, and fallback configuration strategies, wherein the recommended strategies include influence factors and the weights corresponding to the influence factors;

[0299] When the traffic splitting configuration strategy fails to meet usage requirements, the traffic splitting configuration strategy may be modified, removed, or deleted.

[0300] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0301] The step of responding to the data of the filter rule being filled in, and then invoking the corresponding traffic splitting configuration strategy to generate a traffic splitting recommendation request according to the filter rule, includes:

[0302] Retrieve the fixed configuration content and newly added branch configuration content from the data of the filter rules filled in;

[0303] Determine whether there is a traffic splitting configuration strategy that satisfies the filtering rules;

[0304] If it exists, the user traffic will be diverted and a traffic recommendation request will be generated according to the traffic diversion configuration strategy that meets the filtering rules.

[0305] If it does not exist, the fallback configuration strategy will be used to generate a user traffic splitting recommendation request.

[0306] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0307] The steps of calling the RTA data packets stored in the categorized database and using the aforementioned traffic splitting scheme for real-time user recommendation, including filtering users based on the configured business dimensions and returning the traffic splitting results, include:

[0308] Determine whether to adopt the aforementioned traffic splitting scheme for real-time user recommendation traffic splitting;

[0309] If so, the RTA data packets stored in the category are retrieved through the RTA customer group interface;

[0310] Based on the configured business dimensions, set filters to determine whether the user identification information of the RTA data packet matches the filters;

[0311] If a match is found, the RTA data packet is determined to be an RTA customer group that meets the requirements of the traffic splitting scheme. After extracting the user information data of the RTA data packet, the traffic splitting result is returned using a recommendation sub-process.

[0312] If no match is found, the RTA data packet is determined not to meet the RTA customer group of the diversion scheme. After extracting the user information data of the RTA data packet, the diversion sub-process is used to perform hash diversion based on the user identification information and return the diversion result.

[0313] For specific limitations on the steps implemented when a computer program is executed by a processor, please refer to the limitations on the RTA packet splitting method mentioned above, which will not be repeated here.

[0314] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0315] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0316] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for splitting RTA data packets, characterized in that, include: Based on various usage scenarios, corresponding RTA data packets are generated, and the RTA data packets are classified and stored. Multiple traffic splitting configuration strategies are generated by configuring traffic splitting, and filtering rules for the traffic splitting configuration strategies are set. Upon receiving the data filled in with the filtering rules, the system calls the corresponding traffic splitting configuration strategy according to the filtering rules to generate traffic splitting recommendation requests for users. Upon receiving a traffic splitting recommendation request, the traffic splitting configuration strategy is identified, and product parameters are obtained and added to the traffic splitting configuration strategy to form a traffic splitting scheme. The RTA data packets stored in the category are called and the traffic splitting scheme is used to perform real-time user recommendation traffic splitting. According to the configured business dimension, the user is subjected to Bloom filter hit credential stuffing, and the traffic splitting result is returned. The step of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets includes: Acquire user information data corresponding to various usage scenarios and form the user information data into corresponding RTA data packets; According to various usage scenarios, corresponding file storage directories are built in the cloud server, and the RTA data packets are uploaded to the corresponding file storage directories in the cloud server; Obtain the RTA data packet uploaded to the cloud server, and read and parse the RTA data packet; The parsing results of the RTA data packets are stored in a Bloom filter. The RTA data packets are classified and stored according to preset filtering conditions. The classified RTA data packets are then used to generate corresponding Bloom filter key values. When the RTA data packet is read, save the reading record of the RTA data packet and count the Bloom filter key value of the RTA data packet; Configure a custom task to periodically scan the RTA data packets stored in the cloud server; If the current RTA packet has a read record, it is determined that the current RTA packet has been processed, and the current RTA packet is skipped to proceed to the next RTA packet scan; If there is no read record for the current RTA data packet, it is determined that the current RTA data packet has not been processed. The current RTA data packet is read and parsed, and the parsing result of the current RTA data packet is stored in a Bloom filter for classified storage. In response to an update of the RTA data packet, the updated RTA data packet is reread and parsed. The parsing result of the updated RTA data packet is stored in a Bloom filter for classification and storage, and the Bloom filter key value of the statistically analyzed RTA data packet is updated.

2. The RTA data packet splitting method according to claim 1, characterized in that, The step of storing the parsing results of the RTA data packets in a Bloom filter, classifying and storing the RTA data packets according to preset filtering conditions, and generating corresponding Bloom filter key values ​​for the classified RTA data packets includes: The Bloom filter is configured as a probabilistic data structure consisting of a bit array and k hash functions. When the Bloom filter is initialized, all elements of the bit array are 0. When a target element is added to the bit array set, the target element is mapped to k points in the bit array through the corresponding hash function, and the elements corresponding to the k mapped points are set to 1. The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter uses k hash functions to determine whether the user identifier in the parsing result of the RTA data packet is 1 at all k points; If so, the RTA data packet is determined to meet the preset filtering conditions, the RTA data packet is saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​that meet the preset filtering conditions. If not, the RTA data packet is determined not to meet the preset filtering conditions. The RTA data packet is then saved in the database, and the classified RTA data packets are used to generate Bloom filter key values ​​corresponding to those that do not meet the preset filtering conditions.

3. The RTA data packet splitting method according to claim 1, characterized in that, The steps of configuring traffic splitting to form multiple traffic splitting configuration strategies and setting the filtering rules for the traffic splitting configuration strategies include: Setting up the traffic splitting configuration includes fixed configuration content, which is filled in by selection. The branching configuration settings include adding new branch configuration content, which is added via the add method. By filling in various traffic splitting configurations according to different business scenarios, a traffic splitting configuration strategy is formed for each type of traffic splitting configuration. The filtering rules for setting the traffic splitting configuration strategy include usage scenarios, strategy types, recommended strategies, and fallback configuration strategies, wherein the recommended strategies include influence factors and the weights corresponding to the influence factors; When the traffic splitting configuration strategy fails to meet usage requirements, the traffic splitting configuration strategy may be modified, removed, or deleted.

4. The RTA data packet splitting method according to claim 3, characterized in that, The step of responding to the received data of the filter rules by calling the corresponding traffic splitting configuration strategy to generate traffic splitting recommendation requests according to the filter rules includes: Retrieve the fixed configuration content and newly added branch configuration content from the data of the filter rules filled in; Determine whether there is a traffic splitting configuration strategy that satisfies the filtering rules; If it exists, the user traffic will be diverted and a traffic recommendation request will be generated according to the traffic diversion configuration strategy that meets the filtering rules. If it does not exist, the fallback configuration strategy will be used to generate a user traffic splitting recommendation request.

5. The RTA data packet splitting method according to claim 1, characterized in that, The steps of calling the RTA data packets stored in the categorized database and using the aforementioned traffic splitting scheme for real-time user recommendation, including performing Bloom filter hits on users based on the configured business dimensions and returning the traffic splitting results, include: Determine whether to adopt the aforementioned traffic splitting scheme for real-time user recommendation traffic splitting; If so, the RTA data packets stored in the category are retrieved through the RTA customer group interface; Set a Bloom filter according to the configured business dimension, and determine whether the user identification information of the RTA data packet matches the Bloom filter. If a match is found, the RTA data packet is determined to be an RTA customer group that meets the requirements of the traffic splitting scheme. After extracting the user information data of the RTA data packet, the traffic splitting result is returned using a recommendation sub-process. If no match is found, the RTA data packet is determined not to meet the RTA customer group of the diversion scheme. After extracting the user information data of the RTA data packet, the diversion sub-process is used to perform hash diversion based on the user identification information and return the diversion result.

6. An RTA data packet splitting device, characterized in that, The apparatus for implementing the RTA packet splitting method according to any one of claims 1 to 5, the apparatus comprising: The RTA data packet management module is used to generate corresponding RTA data packets according to various usage scenarios and to classify and store the RTA data packets. The traffic splitting configuration module is used to perform traffic splitting configuration to form multiple traffic splitting configuration strategies, and to set the filtering rules for the traffic splitting configuration strategies; The push configuration center module is used to respond to the data of the filter rules filled in, and to call the corresponding traffic splitting configuration strategy to split users and generate traffic splitting recommendation requests according to the filter rules. The query flow distribution result module is used to respond to a received flow distribution recommendation request, identify the flow distribution configuration strategy adopted, obtain product parameters and add them to the flow distribution configuration strategy to form a flow distribution scheme; The traffic splitting module is used to call the RTA data packets stored in the classification and apply the traffic splitting scheme to perform real-time user recommendation traffic splitting. Based on the configured business dimensions, it performs Bloom filter hits on users and returns the traffic splitting results.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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