RTA data packet shunting method and device, computer equipment and storage medium

Abstract

The invention relates to an RTA data packet distribution method and device, computer equipment and a storage medium, and belongs to the technical field of software engineering. According to the method, the RTA data packets are pre-classified and stored, an advertiser performs shunting configuration to form a plurality of shunting configuration strategies, the screening rules of the shunting configuration strategies are set, the appropriate shunting configuration strategies are screened, and the commodity parameters are received and then added into the shunting configuration strategies to form the shunting scheme. According to the method, the generated shunting scheme and the classified and stored RTA data packets are subjected to filter hit and library collision, the shunting result is returned, accurate delivery can be performed according to the demand of an advertiser, refined shunting of crowd flow is performed based on commodity parameters, and the shunting configuration strategy of the advertiser can be stored and managed.

Description

Technical Field

[0001] The present application relates to the field of software engineering technology, and in particular to an RTA data packet diversion method, device, computer equipment and storage medium. Background Art

[0002] With the rapid development of the Internet, online advertising has become one of the important means for companies to promote their products and services. The main ways to deliver online advertising include CPC (Cost per click), CPM (Cost per mille) and CPA (Cost per action). Among them, CPM is the most common delivery method, which is charged according to the number of times the advertisement is displayed. However, the effect of the CPM delivery method is not ideal, because the number of times the advertisement is displayed cannot guarantee the effective display of the advertisement, and it cannot guarantee the audience group of the advertisement.

[0003] With the development of RTA technology, the delivery method has undergone a revolutionary change. RTA is a real-time bidding technology, that is, the publisher can bid on the delivery platform to obtain the opportunity to display the advertisement. RTA technology can not only improve the effect of delivery display, but also help the publisher to deliver advertisements more accurately.

[0004] At present, there are some advertising delivery systems based on the RTA delivery model on the market, but these systems have some defects. First, there is a separation between the application of RTA data packets and the diversion of the delivery party, and they do not support mutual references. They lack the ability to divert traffic to specific groups of people and cannot deliver ads accurately according to the needs of advertisers. Secondly, the group division is relatively rough, and it is simply judged as eligible for bidding or not eligible for bidding. There is a lack of refined diversion configuration strategies for group traffic, and it cannot meet the delivery party's needs for data storage and management. Summary of the invention

[0005] Based on this, an RTA data packet diversion method, device, computer equipment and storage medium are provided to solve the technical problems that the current advertising delivery system lacks the ability to divert traffic to specific groups of people, cannot deliver accurately according to the needs of advertisers, divides the population in a rough manner, and cannot meet the data storage and management needs of the publisher.

[0006] On the one hand, a method for distributing RTA data packets is provided, the method comprising:

[0007] Generate corresponding RTA data packets according to various usage scenarios, and classify and store the RTA data packets;

[0008] Performing diversion configuration to form multiple diversion configuration strategies, and setting screening rules for the diversion configuration strategies;

[0009] In response to receiving the data of the filled-in screening rule, calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request;

[0010] In response to receiving a diversion recommendation request, identifying a diversion configuration strategy to be adopted, obtaining commodity parameters and adding them to the diversion configuration strategy to form a diversion solution;

[0011] The RTA data packet stored in the classification is called to adopt the diversion scheme to perform real-time recommendation and diversion for users. According to the configured business dimensions, the user is subjected to filter hit collision and the diversion result is returned.

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

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

[0014] Constructing corresponding file storage directories in the cloud server according to various usage scenarios, and uploading the RTA data packet to the corresponding file storage directory 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 result of the RTA data packet is stored in a Bloom filter, the RTA data packet is classified and stored according to preset screening conditions, and the classified RTA data packet generates a corresponding Bloom filter key value.

[0017] In one embodiment, storing the parsing result of the RTA data packet in a Bloom filter, classifying and storing the RTA data packet according to preset screening conditions, and generating corresponding Bloom filter key values ​​for the classified RTA data packet includes:

[0018] The Bloom filter is set to be 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, and 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 mapped k points are set to 1;

[0019] The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter determines whether the user identifier in the parsing result of the RTA data packet is hit as 1 at k points through k hash functions;

[0020] If yes, it is determined that the RTA data packet meets the preset screening condition, the RTA data packet is saved in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition;

[0021] If not, it is determined that the RTA data packet does not meet the target screening condition, and the RTA data packet is stored in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition.

[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, a reading record of the RTA data packet is saved, and a Bloom filter key value of the RTA data packet is counted;

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

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

[0026] If there is no read record for the current RTA data packet, it is determined that the current RTA data packet is not 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 classification storage;

[0027] In response to the RTA data packet being updated, the updated RTA data packet is re-read and parsed, the parsing result of the updated RTA data packet is stored in a Bloom filter for classification storage, and the statistical Bloom filter key value of the RTA data packet is updated.

[0028] In one embodiment, the steps of performing diversion configuration to form multiple diversion configuration strategies and setting screening rules for the diversion configuration strategies include:

[0029] Setting the diversion configuration includes fixed configuration content, and the fixed configuration content is filled in by selection;

[0030] Setting the diversion configuration includes adding new branch configuration content, and the new branch configuration content is added in a new way;

[0031] By filling in a plurality of the diversion configurations according to various business scenarios, a diversion configuration strategy is formed for each of the diversion configurations;

[0032] The screening rules for setting the diversion configuration strategy include usage scenarios, strategy types, recommended strategies, and backup configuration strategies, wherein the recommended strategies include influencing factors and weights corresponding to the influencing factors;

[0033] In response to the diversion configuration strategy not meeting the usage requirements, the diversion configuration strategy is modified, removed from the shelves or deleted.

[0034] In one embodiment, in response to receiving the data of the filled-in screening rule, the step of calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request includes:

[0035] Obtain the fixed configuration content and the newly added branch configuration content in the data of the filled-in screening rule;

[0036] Determine whether there is a diversion configuration strategy that meets the screening rule;

[0037] If so, the diversion configuration strategy that meets the screening rule is used to perform user diversion to generate a diversion recommendation request;

[0038] If it does not exist, the bottom-line configuration strategy is used to perform user diversion to generate a diversion recommendation request.

[0039] In one embodiment, the calling of the classified stored RTA data packet adopts the diversion scheme to perform real-time recommendation diversion for users, and performs filter hit collision on users according to the configured business dimensions, and the steps of returning the diversion result include:

[0040] Determine whether to adopt the diversion scheme to perform real-time recommendation diversion for users;

[0041] If yes, query and obtain the classified stored RTA data packets through the RTA customer group interface;

[0042] Setting a filter according to the configured service dimension, and determining whether the user identification information of the RTA data packet hits the filter;

[0043] If there is a hit, it is determined that the RTA data packet meets the RTA customer group of the diversion solution, and after extracting the user information data of the RTA data packet, a recommendation sub-process is used to return the diversion result;

[0044] If no hit is found, it is determined that the RTA data packet does not meet the RTA customer group of the diversion scheme, and after extracting the user information data of the RTA data packet, a 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, a RTA data packet splitting device is provided, the device comprising:

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

[0047] A traffic diversion configuration module, used for performing traffic diversion configuration to form multiple traffic diversion configuration strategies, and setting screening rules for the traffic diversion configuration strategies;

[0048] A push configuration center module is used to, in response to receiving the data of the filled-in screening rule, call the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request;

[0049] A diversion result query module is used to identify the diversion configuration strategy adopted in response to receiving a diversion recommendation request, obtain commodity parameters and add them to the diversion configuration strategy to form a diversion plan;

[0050] The diversion processing module is used to call the RTA data packets stored in the classification to adopt the diversion scheme to perform real-time recommendation and diversion for users, perform filter hit collision on users according to the configured business dimensions, and return the diversion results.

[0051] In another aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0052] Generate corresponding RTA data packets according to various usage scenarios, and classify and store the RTA data packets;

[0053] Performing diversion configuration to form multiple diversion configuration strategies, and setting screening rules for the diversion configuration strategies;

[0054] In response to receiving the data of the filled-in screening rule, calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request;

[0055] In response to receiving a diversion recommendation request, identifying a diversion configuration strategy to be adopted, obtaining commodity parameters and adding them to the diversion configuration strategy to form a diversion solution;

[0056] The RTA data packet stored in the classification is called to adopt the diversion scheme to perform real-time recommendation and diversion for users. According to the configured business dimensions, the user is subjected to filter hit collision and the diversion result is returned.

[0057] In another aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0058] Generate corresponding RTA data packets according to various usage scenarios, and classify and store the RTA data packets;

[0059] Performing diversion configuration to form multiple diversion configuration strategies, and setting screening rules for the diversion configuration strategies;

[0060] In response to receiving the data of the filled-in screening rule, calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request;

[0061] In response to receiving a diversion recommendation request, identifying a diversion configuration strategy to be adopted, obtaining commodity parameters and adding them to the diversion configuration strategy to form a diversion solution;

[0062] The RTA data packet stored in the classification is called to adopt the diversion scheme to perform real-time recommendation and diversion for users. According to the configured business dimensions, the user is subjected to filter hit collision and the diversion result is returned.

[0063] The above-mentioned RTA data packet diversion method, device, computer equipment and storage medium pre-classify and store the RTA data packets, and the advertiser performs diversion configuration to form multiple diversion configuration strategies, and sets the screening rules of the diversion configuration strategy to screen the appropriate diversion configuration strategy, and after receiving the product parameters, add them to the diversion configuration strategy to form a diversion plan, and perform filter hit collision on the generated diversion plan and the classified stored RTA data packets, and return the diversion result. It can perform precise delivery according to the needs of the advertiser, and can perform refined diversion of crowd traffic based on product parameters, and can store and manage the advertiser's diversion configuration strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0065] Figure 1 This is an application environment diagram of the RTA data packet diversion method in one embodiment of the present application;

[0066] Figure 2 A schematic diagram of a flow chart of an RTA data packet diversion method in one embodiment of the present application;

[0067] Figure 3 A panoramic flow chart of the RTA data packet diversion method in one embodiment of the present application;

[0068] Figure 4A flowchart of steps for generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets in one embodiment of the present application;

[0069] Figure 5 A schematic diagram of the steps of storing the parsing result of the RTA data packet in a Bloom filter, classifying and storing the RTA data packet according to preset screening conditions, and generating corresponding Bloom filter key values ​​from the classified RTA data packet in one embodiment of the present application;

[0070] Figure 6 A schematic flow chart of forming multiple diversion configuration strategies and setting screening rule steps for the diversion configuration strategies in an embodiment of the present application;

[0071] Figure 7 A flowchart of the steps of generating a diversion recommendation request for user diversion by calling a corresponding diversion configuration strategy according to the screening rule in response to receiving the data of the filled-in screening rule in one embodiment of the present application;

[0072] Figure 8 A flowchart of setting a filter according to a configured service dimension and determining whether the user identification information of the RTA data packet hits the filter in one embodiment of the present application;

[0073] Fig. 9 This is a structural block diagram of an RTA data packet splitting device in one embodiment of the present application;

[0074] Fig.10 This is a diagram of the internal structure of a computer device in one embodiment of the present application. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0076] The RTA data packet diversion method provided in this application can be applied to Figure 1In the application environment shown. Among them, the advertiser terminal 101 and the media advertising platform 102 communicate with the marketing system server 103 through the network. The advertiser terminal 101 and the media advertising platform 102 both upload the marketing user data to be identified to the marketing system server 103. The marketing system server 103 identifies and analyzes the uploaded marketing user data to be identified and feeds back the marketing user data. The advertiser terminal 101 and the media advertising platform 102 carry out promotion and marketing according to the marketing user data fed back by the marketing system server 103. The promotion and marketing methods include sending advertising information or pushing marketing content. Among them, the advertiser terminal 101 and the media advertising platform 102 can be but not limited to various personal computers, laptops, smart phones, tablet computers 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 with independent servers or server clusters composed of multiple servers.

[0077] In one embodiment, Figure 2 , Figure 3 As shown, a RTA data packet diversion method is provided, and the method is applied to Figure 1 The marketing system server 103 in the example is used as an example to illustrate, including the following steps:

[0078] Step S1, generating corresponding RTA data packets according to various usage scenarios, and classifying and storing the RTA data packets;

[0079] Step S2, performing diversion configuration to form multiple diversion configuration strategies, and setting screening rules for the diversion configuration strategies;

[0080] Step S3, in response to receiving the data of the filled-in screening rule, calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request;

[0081] Step S4, in response to receiving the diversion recommendation request, identifying the adopted diversion configuration strategy, obtaining commodity parameters and adding them to the diversion configuration strategy to form a diversion plan;

[0082] Step S5, calling the RTA data packet stored in the classification to adopt the diversion scheme to perform real-time recommendation and diversion for users, and performing filter hit collision on the users according to the configured business dimensions, and returning the diversion result.

[0083] Among them, by pre-classifying and storing the RTA data packets in the marketing system server 103, the advertiser performs diversion configuration to form multiple diversion configuration strategies, and sets the screening rules of the diversion configuration strategy to screen the appropriate diversion configuration strategy, and after receiving the product parameters, add them to the diversion configuration strategy to form a diversion plan, and perform filter hit collision on the generated diversion plan and the classified stored RTA data packets, and return the diversion result, which can be accurately delivered according to the needs of the advertiser, and can divert the crowd flow based on the product parameters in a refined manner, and can store and manage the advertiser's diversion configuration strategy.

[0084] This embodiment can be flexibly and simply configured according to the needs of the publisher, thereby achieving more efficient and accurate delivery. At the same time, it provides fast and low-cost data storage and management functions, which can help the publisher better manage delivery data.

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

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

[0087] Step S12, constructing a corresponding file storage directory in the cloud server according to various usage scenarios, and uploading the RTA data packet to the corresponding file storage directory in the cloud server;

[0088] Step S13, obtaining the RTA data packet uploaded to the cloud server, and reading and parsing the RTA data packet;

[0089] Step S14, storing the parsing result of the RTA data packet in a Bloom filter, classifying and storing the RTA data packet according to preset screening conditions, and generating corresponding Bloom filter key values ​​for the classified RTA data packets.

[0090] like Figure 3 As shown in the figure, it provides users with highly flexible configuration to achieve the desired advertising effect. Users can select appropriate RTA data packets, formulate rule strategies suitable for the characteristics of the current population, and achieve more refined traffic operations. While ensuring the real-time performance of the system, it maximizes the value of traffic, achieves the effect of advertising, recovers costs, and improves ROI.

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

[0092] It is understandable that the RTA data packets are regularly uploaded to the OSS file server by the RTA back-end management system, which then scans and parses them. According to the different types of RTA data packets, the system can generate corresponding data packet parsing records, enter them into the library, and generate corresponding Bloom filter key values. Each reading of the RTA data packet has a corresponding reading record, ensuring that each reading of the RTA data packet can be traced and smoothly transitioned.

[0093] like Figure 5 As shown, in this embodiment, the parsing result of the RTA data packet is stored in the Bloom filter, the RTA data packet is classified and stored according to the preset screening conditions, and the classified RTA data packet generates the corresponding Bloom filter key value, including:

[0094] The Bloom filter is set to be 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, and 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 mapped k points are set to 1;

[0095] The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter determines whether the user identifier in the parsing result of the RTA data packet is hit as 1 at k points through k hash functions;

[0096] If yes, it is determined that the RTA data packet meets the preset screening condition, the RTA data packet is saved in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition;

[0097] If not, it is determined that the RTA data packet does not meet the target screening condition, and the RTA data packet is stored in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition.

[0098] like Figure 5 As shown, the parsing result of the RTA data packet is hashed into the Bloom filter to reduce the storage space of the data. Among them, the advantage of the Bloom filter is a probabilistic data structure with high space utilization. Preferably, the database is Redis, which is a high-performance key-value database that supports a variety of data structures, including strings, linked lists, sets, etc., and is completely open source and free.

[0099] Among them, the advantages of using Bloom filters are: very small space occupancy, because bits are used to identify whether an element exists; there is no need to store the data itself, and it has good confidentiality; the complexity of inserting and querying elements is O(k), k also represents the number of times the hash function is executed, and k hash functions are executed once; high performance, the database operation atomicity and high performance greatly improve service performance.

[0100] When the RTA data packet queries whether it meets the preset screening conditions, k hash functions are also used to determine whether the requested user identification information (preferably the device number) hits 1 at all k points. If the conditions are not met, the requesting user does not meet the data packet screening conditions, which can be used as a basic condition for user traffic judgment. In addition, the user information data of the RTA data packet (if any) will be cached in the database to provide a user data basis 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, a reading record of the RTA data packet is saved, and a Bloom filter key value of the RTA data packet is counted;

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

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

[0105] If there is no read record for the current RTA data packet, it is determined that the current RTA data packet is not 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 classification storage;

[0106] In response to the RTA data packet being updated, the updated RTA data packet is re-read and parsed, the parsing result of the updated RTA data packet is stored in a Bloom filter for classification storage, and the statistical Bloom filter key value of the RTA data packet is updated.

[0107] Among them, configure custom tasks to perform scheduled scanning. When scanning RTA data packets, you can flexibly configure reading rules according to business requirements, and specify the Bloom filter error rate and data level of the data packet.

[0108] After the RTA data packet is parsed, the data is stored in the Bloom filter, and the data record is updated at the same time, refreshing the latest Bloom filter key value of the RTA data packet. The subsequent diversion configuration page can read the parsed data packet in real time, and perform designated proportion diversion according to business requirements or recommend according to user information.

[0109] like Figure 6 As shown, in this embodiment, the steps of performing diversion configuration to form multiple diversion configuration strategies and setting screening rules of the diversion configuration strategies include:

[0110] Step S21, setting the diversion configuration includes fixed configuration content, the fixed configuration content is filled in by selection, and the fixed configuration content includes distribution ID, distribution name, distribution product and distribution result type;

[0111] Step S22, setting the diversion configuration to include newly added branch configuration content, wherein the newly added branch configuration content is added in a new manner;

[0112] Step S23, by filling in a plurality of the diversion configurations according to various business scenarios, forming a diversion configuration strategy for each of the diversion configurations;

[0113] Step S24, setting the screening rules of the diversion configuration strategy to include usage scenarios, strategy types, recommended strategies, and fallback configuration strategies, wherein the recommended strategies include influencing factors and weights corresponding to the influencing factors;

[0114] Step S25, in response to the diversion configuration strategy not meeting the usage requirements, modifying, removing or deleting the diversion configuration strategy.

[0115] According to the demands of the business side, the corresponding rules can be configured on the diversion configuration page. You can create a custom distribution ID and use it to match the corresponding strategy in the subsequent delivery page. In addition, the business side can also specify the distribution dimension of the distribution strategy, and can flexibly expand the new branch configuration content as needed. When configuring the distribution strategy, the system also supports setting a fallback strategy to deal with the situation where the user does not exist in the data packet. The system will perform hash diversion according to the currently configured fallback strategy.

[0116] In addition, the diversion configuration strategy can also be identified by selecting the RTA data package. Once the RTA data package is selected, the business party can configure the corresponding distribution strategy (i.e. diversion configuration) according to the specific demands. Among them, the distribution strategy can be customized according to the parameter dimensions of different businesses. For example, the parameter dimensions include but are not limited to user purchase records, user browsing records, user basic attributes and other tags. The distribution strategy is submitted to the recommendation rule engine for further judgment to achieve more refined user diversion and recommendation.

[0117] After the diversion configuration strategy is configured and put on the shelf, flexible on-shelf and off-shelf operations can be performed. When the strategy is put on the shelf, the configuration information will be pushed to the configuration center, which can be zookeeper, neptune, nacos, etc., to complete the decoupling of the configuration background and the business system, and improve the efficiency of the business system in reading the configuration and service performance (including but not limited to interface timeliness, concurrency, service stability, etc.), and only the strategies in the on-shelf state will take effect. For policies that have expired or are no longer needed, they can be taken off the shelf in a timely manner. After the modification is completed, in order to avoid the impact of data misoperation on production business, an error-free check is required before it can be put on the shelf and take effect.

[0118] like Figure 7 As shown, in this embodiment, in response to receiving the data of the filled-in screening rule, the step of calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request includes:

[0119] Step S31, obtaining the fixed configuration content and the newly added branch configuration content in the data of the filled-in screening rule;

[0120] Step S32, determining whether there is a diversion configuration strategy that meets the screening rule;

[0121] Step S33: if it exists, the diversion configuration strategy that meets the screening rule is used to perform user diversion to generate a diversion recommendation request; if it does not exist, the bottom-line configuration strategy is used to perform user diversion to generate a diversion recommendation request.

[0122] See also Figure 3 In this embodiment, the RTA data packet stored in the classification is called and the diversion scheme is used to perform real-time recommendation and diversion for users. According to the configured business dimensions, the user is subjected to filter hit collision, and the diversion result is returned, and the steps include:

[0123] Determine whether to adopt the diversion scheme to perform real-time recommendation diversion for users;

[0124] If yes, query and obtain the classified stored RTA data packets through the RTA customer group interface;

[0125] Setting a filter according to the configured service dimension, and determining whether the user identification information of the RTA data packet hits the filter;

[0126] If there is a hit, it is determined that the RTA data packet meets the RTA customer group of the diversion solution, and after extracting the user information data of the RTA data packet, a recommendation sub-process is used to return the diversion result;

[0127] If no hit is found, it is determined that the RTA data packet does not meet the RTA customer group of the diversion scheme, and after extracting the user information data of the RTA data packet, a 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, wherein the filter is set according to the configured business dimension, and the method of determining whether the user identification information of the RTA data packet hits the filter is the same or similar to the aforementioned method of storing the parsing results of the RTA data packet in the Bloom filter, classifying and storing the RTA data packets according to preset screening conditions, and generating corresponding Bloom filter key values ​​for the classified RTA data packets, which will not be repeated here.

[0129] Preferably, the user identification information is a device number. Figure 8 As shown, according to the configured business dimension, a filter is set, and the method for determining whether the user identification information of the RTA data packet hits the filter is as follows: for the RTA customer group that meets the diversion scheme, the corresponding product can be recommended to the RTA user based on the recommendation sub-process, and for the RTA customer group that does not meet the diversion scheme, the device number can be used as the diversion identification based on the diversion sub-process, and the diversion result of the corresponding recommended product can be returned by hash diversion. The calculation method of hash diversion is Hash()%100.

[0130] Combined with the diversion results, all diversion effect statistics are displayed to the publisher in the data dashboard, including daily totals, diversion result distribution, diversion result ratio, etc.

[0131] In the above-mentioned RTA data packet diversion method, the RTA data packets are pre-classified and stored, and the advertiser performs diversion configuration to form multiple diversion configuration strategies, and sets the screening rules of the diversion configuration strategy to screen the appropriate diversion configuration strategy, and after receiving the product parameters, add them to the diversion configuration strategy to form a diversion plan, and perform filter hit collision on the generated diversion plan and the classified stored RTA data packets, and return the diversion result. It can be accurately delivered according to the needs of the advertiser, and the crowd traffic can be finely diverted based on the product parameters, and the advertiser's diversion configuration strategy can be stored and managed.

[0132] It should be understood that although Figure 2-Figure 8 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2-Figure 8At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed 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 part of the sub-steps or stages of other steps.

[0133] In one embodiment, Fig. 9 As shown, an RTA data packet diversion device 10 is provided, comprising: an RTA data packet management module 1, a diversion configuration module 2, a push configuration center module 3, a diversion result query module 4, and a diversion 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 classify and store the RTA data packets.

[0135] The diversion configuration module 2 is used to perform diversion configuration to form multiple diversion configuration strategies and set screening rules for the diversion configuration strategies.

[0136] The push configuration center module 3 is used for, in response to receiving the data of the filled-in screening rule, calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request.

[0137] The query diversion result module 4 is used to identify the adopted diversion configuration strategy in response to receiving the diversion recommendation request, and obtain commodity parameters to add to the diversion configuration strategy to form a diversion plan.

[0138] The diversion processing module 5 is used to call the RTA data packets stored in the classification to adopt the diversion scheme to perform real-time recommendation diversion for users, perform filter hit collision on users according to the configured business dimensions, and return the diversion result.

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

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

[0141] Constructing corresponding file storage directories in the cloud server according to various usage scenarios, and uploading the RTA data packet to the corresponding file storage directory 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 result of the RTA data packet is stored in a Bloom filter, the RTA data packet is classified and stored according to preset screening conditions, and the classified RTA data packet generates a corresponding Bloom filter key value.

[0144] In this embodiment, storing the parsing result of the RTA data packet in a Bloom filter, classifying and storing the RTA data packet according to preset screening conditions, and generating corresponding Bloom filter key values ​​for the classified RTA data packet include:

[0145] The Bloom filter is set to be 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, and 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 mapped k points are set to 1;

[0146] The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter determines whether the user identifier in the parsing result of the RTA data packet is hit as 1 at k points through k hash functions;

[0147] If yes, it is determined that the RTA data packet meets the preset screening condition, the RTA data packet is saved in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition;

[0148] If not, it is determined that the RTA data packet does not meet the target screening condition, and the RTA data packet is stored in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition.

[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, a reading record of the RTA data packet is saved, and a Bloom filter key value of the RTA data packet is counted;

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

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

[0153] If there is no read record for the current RTA data packet, it is determined that the current RTA data packet is not 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 classification storage;

[0154] In response to the RTA data packet being updated, the updated RTA data packet is re-read and parsed, the parsing result of the updated RTA data packet is stored in a Bloom filter for classification storage, and the statistical Bloom filter key value of the RTA data packet is updated.

[0155] In this embodiment, the steps of performing diversion configuration to form multiple diversion configuration strategies and setting screening rules for the diversion configuration strategies include:

[0156] Setting the diversion configuration includes fixed configuration content, which is filled in by selection, and the fixed configuration content includes distribution ID, distribution name, distribution product and distribution result type;

[0157] Setting the diversion configuration includes adding new branch configuration content, and the new branch configuration content is added in a new way;

[0158] By filling in a plurality of the diversion configurations according to various business scenarios, a diversion configuration strategy is formed for each of the diversion configurations;

[0159] The screening rules for setting the diversion configuration strategy include usage scenarios, strategy types, recommended strategies, and backup configuration strategies, wherein the recommended strategies include influencing factors and weights corresponding to the influencing factors;

[0160] In response to the diversion configuration strategy not meeting the usage requirements, the diversion configuration strategy is modified, removed from the shelves or deleted.

[0161] In this embodiment, in response to receiving the data of the filled-in screening rule, the step of calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request includes:

[0162] Obtain the fixed configuration content and the newly added branch configuration content in the data of the filled-in screening rule;

[0163] Determine whether there is a diversion configuration strategy that meets the screening rule;

[0164] If so, the diversion configuration strategy that meets the screening rule is used to perform user diversion to generate a diversion recommendation request;

[0165] If it does not exist, the bottom-line configuration strategy is used to perform user diversion to generate a diversion recommendation request.

[0166] In this embodiment, the RTA data packet stored in the classification is called and the diversion scheme is used to perform real-time recommendation and diversion for users. According to the configured business dimension, the user is subjected to filter hit collision, and the diversion result is returned. The steps include:

[0167] Determine whether to adopt the diversion scheme to perform real-time recommendation diversion for users;

[0168] If yes, query and obtain the classified stored RTA data packets through the RTA customer group interface;

[0169] Setting a filter according to the configured service dimension, and determining whether the user identification information of the RTA data packet hits the filter;

[0170] If there is a hit, it is determined that the RTA data packet meets the RTA customer group of the diversion solution, and after extracting the user information data of the RTA data packet, a recommendation sub-process is used to return the diversion result;

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

[0172] In the above-mentioned RTA data packet diversion device, by pre-classifying and storing the RTA data packets, the advertiser performs diversion configuration to form multiple diversion configuration strategies, and sets the screening rules of the diversion configuration strategy to screen the appropriate diversion configuration strategy, and after receiving the product parameters, add them to the diversion configuration strategy to form a diversion plan, and perform filter hit collision on the generated diversion plan and the classified stored RTA data packets, and return the diversion result. It can perform precise delivery according to the needs of the advertiser, and can perform refined diversion of crowd traffic based on product parameters, and can store and manage the advertiser's diversion configuration strategy.

[0173] The specific definition of the RTA data packet diversion device can be found in the definition of the RTA data packet diversion method above, which will not be repeated here. Each module in the above RTA data packet diversion device can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0174] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0175] Generate corresponding RTA data packets according to various usage scenarios, and classify and store the RTA data packets;

[0176] Performing diversion configuration to form multiple diversion configuration strategies, and setting screening rules for the diversion configuration strategies;

[0177] In response to receiving the data of the filled-in screening rule, calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request;

[0178] In response to receiving a diversion recommendation request, identifying a diversion configuration strategy to be adopted, obtaining commodity parameters and adding them to the diversion configuration strategy to form a diversion solution;

[0179] The RTA data packet stored in the classification is called to adopt the diversion scheme to perform real-time recommendation and diversion for users. According to the configured business dimensions, the user is subjected to filter hit collision and the diversion result is returned.

[0180] In one embodiment, the computer program further implements the following steps when executed by a processor:

[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] Constructing corresponding file storage directories in the cloud server according to various usage scenarios, and uploading the RTA data packet to the corresponding file storage directory in the cloud server;

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

[0185] The parsing result of the RTA data packet is stored in a Bloom filter, the RTA data packet is classified and stored according to preset screening conditions, and the classified RTA data packet generates a corresponding Bloom filter key value.

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

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

[0188] The Bloom filter is set to be 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, and 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 mapped k points are set to 1;

[0189] The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter determines whether the user identifier in the parsing result of the RTA data packet is hit as 1 at k points through k hash functions;

[0190] If yes, it is determined that the RTA data packet meets the preset screening condition, the RTA data packet is saved in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition;

[0191] If not, it is determined that the RTA data packet does not meet the target screening condition, and the RTA data packet is stored in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition.

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

[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, a reading record of the RTA data packet is saved, and a Bloom filter key value of the RTA data packet is counted;

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

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

[0197] If there is no read record for the current RTA data packet, it is determined that the current RTA data packet is not 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 classification storage;

[0198] In response to the RTA data packet being updated, the updated RTA data packet is re-read and parsed, the parsing result of the updated RTA data packet is stored in a Bloom filter for classification storage, and the statistical Bloom filter key value of the RTA data packet is updated.

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

[0200] The steps of performing diversion configuration to form multiple diversion configuration strategies and setting screening rules for the diversion configuration strategies include:

[0201] Setting the diversion configuration includes fixed configuration content, which is filled in by selection, and the fixed configuration content includes distribution ID, distribution name, distribution product and distribution result type;

[0202] Setting the diversion configuration includes adding new branch configuration content, and the new branch configuration content is added in a new way;

[0203] By filling in a plurality of the diversion configurations according to various business scenarios, a diversion configuration strategy is formed for each of the diversion configurations;

[0204] The screening rules for setting the diversion configuration strategy include usage scenarios, strategy types, recommended strategies, and backup configuration strategies, wherein the recommended strategies include influencing factors and weights corresponding to the influencing factors;

[0205] In response to the diversion configuration strategy not meeting the usage requirements, the diversion configuration strategy is modified, removed from the shelves or deleted.

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

[0207] In response to receiving the data of the filled-in screening rule, the step of calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request includes:

[0208] Obtain the fixed configuration content and the newly added branch configuration content in the data of the filled-in screening rule;

[0209] Determine whether there is a diversion configuration strategy that meets the screening rule;

[0210] If so, the diversion configuration strategy that meets the screening rule is used to perform user diversion to generate a diversion recommendation request;

[0211] If it does not exist, the bottom-line configuration strategy is used to perform user diversion to generate a diversion recommendation request.

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

[0213] The calling of the RTA data packet classified and stored adopts the diversion scheme to perform real-time recommendation and diversion for users, and performs filter hit collision on users according to the configured business dimensions, and the steps of returning the diversion result include:

[0214] Determine whether to adopt the diversion scheme to perform real-time recommendation diversion for users;

[0215] If yes, query and obtain the classified stored RTA data packets through the RTA customer group interface;

[0216] Setting a filter according to the configured service dimension, and determining whether the user identification information of the RTA data packet hits the filter;

[0217] If there is a hit, it is determined that the RTA data packet meets the RTA customer group of the diversion solution, and after extracting the user information data of the RTA data packet, a recommendation sub-process is used to return the diversion result;

[0218] If no hit is found, it is determined that the RTA data packet does not meet the RTA customer group of the diversion scheme, and after extracting the user information data of the RTA data packet, a 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 the computer program is executed by the processor, please refer to the above limitations on the method for RTA data packet diversion, which will not be repeated here.

[0220] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.10 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store RTA data packet diversion data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an RTA data packet diversion method is implemented.

[0221] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[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 implements the following steps when executing the computer program:

[0223] Generate corresponding RTA data packets according to various usage scenarios, and classify and store the RTA data packets;

[0224] Performing diversion configuration to form multiple diversion configuration strategies, and setting screening rules for the diversion configuration strategies;

[0225] In response to receiving the data of the filled-in screening rule, calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request;

[0226] In response to receiving a diversion recommendation request, identifying a diversion configuration strategy to be adopted, obtaining commodity parameters and adding them to the diversion configuration strategy to form a diversion solution;

[0227] The RTA data packet stored in the classification is called to adopt the diversion scheme to perform real-time recommendation and diversion for users. According to the configured business dimensions, the user is subjected to filter hit collision and the diversion result is returned.

[0228] In one embodiment, when the processor executes the computer program, the processor further implements 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] Constructing corresponding file storage directories in the cloud server according to various usage scenarios, and uploading the RTA data packet to the corresponding file storage directory in the cloud server;

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

[0233] The parsing result of the RTA data packet is stored in a Bloom filter, the RTA data packet is classified and stored according to preset screening conditions, and the classified RTA data packet generates a corresponding Bloom filter key value.

[0234] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

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

[0236] The Bloom filter is set to be 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, and 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 mapped k points are set to 1;

[0237] The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter determines whether the user identifier in the parsing result of the RTA data packet is hit as 1 at k points through k hash functions;

[0238] If yes, it is determined that the RTA data packet meets the preset screening condition, the RTA data packet is saved in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition;

[0239] If not, it is determined that the RTA data packet does not meet the target screening condition, and the RTA data packet is stored in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition.

[0240] In one embodiment, when the processor executes the computer program, the processor further implements 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, a reading record of the RTA data packet is saved, and a Bloom filter key value of the RTA data packet is counted;

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

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

[0245] If there is no read record for the current RTA data packet, it is determined that the current RTA data packet is not 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 classification storage;

[0246] In response to the RTA data packet being updated, the updated RTA data packet is re-read and parsed, the parsing result of the updated RTA data packet is stored in a Bloom filter for classification storage, and the statistical Bloom filter key value of the RTA data packet is updated.

[0247] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0248] The steps of performing diversion configuration to form multiple diversion configuration strategies and setting screening rules for the diversion configuration strategies include:

[0249] Setting the diversion configuration includes fixed configuration content, which is filled in by selection, and the fixed configuration content includes distribution ID, distribution name, distribution product and distribution result type;

[0250] Setting the diversion configuration includes adding new branch configuration content, and the new branch configuration content is added in a new way;

[0251] By filling in a plurality of the diversion configurations according to various business scenarios, a diversion configuration strategy is formed for each of the diversion configurations;

[0252] The screening rules for setting the diversion configuration strategy include usage scenarios, strategy types, recommended strategies, and backup configuration strategies, wherein the recommended strategies include influencing factors and weights corresponding to the influencing factors;

[0253] In response to the diversion configuration strategy not meeting the usage requirements, the diversion configuration strategy is modified, removed from the shelves or deleted.

[0254] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0255] In response to receiving the data of the filled-in screening rule, the step of calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request includes:

[0256] Obtain the fixed configuration content and the newly added branch configuration content in the data of the filled-in screening rule;

[0257] Determine whether there is a diversion configuration strategy that meets the screening rule;

[0258] If so, the diversion configuration strategy that meets the screening rule is used to perform user diversion to generate a diversion recommendation request;

[0259] If it does not exist, the bottom-line configuration strategy is used to perform user diversion to generate a diversion recommendation request.

[0260] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0261] The calling of the RTA data packet classified and stored adopts the diversion scheme to perform real-time recommendation and diversion for users, and performs filter hit collision on users according to the configured business dimensions, and the steps of returning the diversion result include:

[0262] Determine whether to adopt the diversion scheme to perform real-time recommendation diversion for users;

[0263] If yes, query and obtain the classified stored RTA data packets through the RTA customer group interface;

[0264] Setting a filter according to the configured service dimension, and determining whether the user identification information of the RTA data packet hits the filter;

[0265] If there is a hit, it is determined that the RTA data packet meets the RTA customer group of the diversion solution, and after extracting the user information data of the RTA data packet, a recommendation sub-process is used to return the diversion result;

[0266] If no hit is found, it is determined that the RTA data packet does not meet the RTA customer group of the diversion scheme, and after extracting the user information data of the RTA data packet, a 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 implemented when the processor executes the computer program, please refer to the above limitations on the method for RTA data packet diversion, which will not be repeated here.

[0268] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0269] Generate corresponding RTA data packets according to various usage scenarios, and classify and store the RTA data packets;

[0270] Performing diversion configuration to form multiple diversion configuration strategies, and setting screening rules for the diversion configuration strategies;

[0271] In response to receiving the data of the filled-in screening rule, calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request;

[0272] In response to receiving a diversion recommendation request, identifying a diversion configuration strategy to be adopted, obtaining commodity parameters and adding them to the diversion configuration strategy to form a diversion solution;

[0273] The RTA data packet stored in the classification is called to adopt the diversion scheme to perform real-time recommendation and diversion for users. According to the configured business dimensions, the user is subjected to filter hit collision and the diversion result is returned.

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

[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] Constructing corresponding file storage directories in the cloud server according to various usage scenarios, and uploading the RTA data packet to the corresponding file storage directory 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 result of the RTA data packet is stored in a Bloom filter, the RTA data packet is classified and stored according to preset screening conditions, and the classified RTA data packet generates a corresponding Bloom filter key value.

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

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

[0282] The Bloom filter is set to be 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, and 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 mapped k points are set to 1;

[0283] The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter determines whether the user identifier in the parsing result of the RTA data packet is hit as 1 at k points through k hash functions;

[0284] If yes, it is determined that the RTA data packet meets the preset screening condition, the RTA data packet is saved in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition;

[0285] If not, it is determined that the RTA data packet does not meet the target screening condition, and the RTA data packet is stored in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition.

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

[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, a reading record of the RTA data packet is saved, and a Bloom filter key value of the RTA data packet is counted;

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

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

[0291] If there is no read record for the current RTA data packet, it is determined that the current RTA data packet is not 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 classification storage;

[0292] In response to the RTA data packet being updated, the updated RTA data packet is re-read and parsed, the parsing result of the updated RTA data packet is stored in a Bloom filter for classification storage, and the statistical Bloom filter key value of the RTA data packet is updated.

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

[0294] The steps of performing diversion configuration to form multiple diversion configuration strategies and setting screening rules for the diversion configuration strategies include:

[0295] Setting the diversion configuration includes fixed configuration content, which is filled in by selection, and the fixed configuration content includes distribution ID, distribution name, distribution product and distribution result type;

[0296] Setting the diversion configuration includes adding new branch configuration content, and the new branch configuration content is added in a new way;

[0297] By filling in a plurality of the diversion configurations according to various business scenarios, a diversion configuration strategy is formed for each of the diversion configurations;

[0298] The screening rules for setting the diversion configuration strategy include usage scenarios, strategy types, recommended strategies, and backup configuration strategies, wherein the recommended strategies include influencing factors and weights corresponding to the influencing factors;

[0299] In response to the diversion configuration strategy not meeting the usage requirements, the diversion configuration strategy is modified, removed from the shelves or deleted.

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

[0301] In response to receiving the data of the filled-in screening rule, the step of calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request includes:

[0302] Obtain the fixed configuration content and the newly added branch configuration content in the data of the filled-in screening rule;

[0303] Determine whether there is a diversion configuration strategy that meets the screening rule;

[0304] If so, the diversion configuration strategy that meets the screening rule is used to perform user diversion to generate a diversion recommendation request;

[0305] If it does not exist, the bottom-line configuration strategy is used to perform user diversion to generate a diversion recommendation request.

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

[0307] The calling of the RTA data packet classified and stored adopts the diversion scheme to perform real-time recommendation and diversion for users, and performs filter hit collision on users according to the configured business dimensions, and the steps of returning the diversion result include:

[0308] Determine whether to adopt the diversion scheme to perform real-time recommendation diversion for users;

[0309] If yes, query and obtain the classified stored RTA data packets through the RTA customer group interface;

[0310] Setting a filter according to the configured service dimension, and determining whether the user identification information of the RTA data packet hits the filter;

[0311] If there is a hit, it is determined that the RTA data packet meets the RTA customer group of the diversion solution, and after extracting the user information data of the RTA data packet, a recommendation sub-process is used to return the diversion result;

[0312] If no hit is found, it is determined that the RTA data packet does not meet the RTA customer group of the diversion scheme, and after extracting the user information data of the RTA data packet, a 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 the computer program is executed by the processor, please refer to the above limitations on the method for RTA data packet diversion, which will not be repeated here.

[0314] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database 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. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0315] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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 above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A RTA data packet diversion method, characterized in that: include: Generate corresponding RTA data packets according to various usage scenarios, and classify and store the RTA data packets; Performing diversion configuration to form multiple diversion configuration strategies, and setting screening rules for the diversion configuration strategies; In response to receiving the data of the filled-in screening rule, calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request; In response to receiving a diversion recommendation request, identifying a diversion configuration strategy to be adopted, obtaining commodity parameters and adding them to the diversion configuration strategy to form a diversion solution; The RTA data packet stored in the classification is called to adopt the diversion scheme to perform real-time recommendation and diversion for users. According to the configured business dimensions, the user is subjected to filter hit collision and the diversion result is returned.

2. The RTA data packet diversion method according to claim 1, characterized in that: The steps of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets include: Acquire user information data corresponding to various usage scenarios, and form the user information data into corresponding RTA data packets; Constructing corresponding file storage directories in the cloud server according to various usage scenarios, and uploading the RTA data packet to the corresponding file storage directory in the cloud server; Obtain the RTA data packet uploaded to the cloud server, and read and parse the RTA data packet; The parsing result of the RTA data packet is stored in a Bloom filter, the RTA data packet is classified and stored according to preset screening conditions, and the classified RTA data packet generates a corresponding Bloom filter key value.

3. The RTA data packet diversion method according to claim 2, characterized in that: The step of storing the parsing result of the RTA data packet in a Bloom filter, classifying and storing the RTA data packet according to preset screening conditions, and generating corresponding Bloom filter key values ​​for the classified RTA data packet includes: The Bloom filter is set to be 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, and 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 mapped k points are set to 1; The parsing result of the RTA data packet is hashed into the Bloom filter, and the Bloom filter determines whether the user identifier in the parsing result of the RTA data packet is hit as 1 at k points through k hash functions; If yes, it is determined that the RTA data packet meets the preset screening condition, the RTA data packet is saved in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition; If not, it is determined that the RTA data packet does not meet the target screening condition, and the RTA data packet is stored in a database, and the classified RTA data packet generates a Bloom filter key value corresponding to the preset screening condition.

4. The RTA data packet diversion method according to claim 3, characterized in that: The step of generating corresponding RTA data packets according to various usage scenarios and classifying and storing the RTA data packets further includes: When the RTA data packet is read, a reading record of the RTA data packet is saved, and a Bloom filter key value of the RTA data packet is counted; Configure a customized task to periodically scan the RTA data packet stored in the cloud server; If there is a read record for the current RTA data packet, it is determined that the current RTA data packet has been processed, and the current RTA data packet is skipped to proceed to the next RTA data packet scan; If there is no read record for the current RTA data packet, it is determined that the current RTA data packet is not 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 classification storage; In response to the RTA data packet being updated, the updated RTA data packet is re-read and parsed, the parsing result of the updated RTA data packet is stored in a Bloom filter for classification storage, and the statistical Bloom filter key value of the RTA data packet is updated.

5. The RTA data packet diversion method according to claim 1, characterized in that: The steps of performing diversion configuration to form multiple diversion configuration strategies and setting screening rules for the diversion configuration strategies include: Setting the diversion configuration includes fixed configuration content, and the fixed configuration content is filled in by selection; Setting the diversion configuration includes adding new branch configuration content, and the new branch configuration content is added in a new way; By filling in a plurality of the diversion configurations according to various business scenarios, a diversion configuration strategy is formed for each of the diversion configurations; The screening rules for setting the diversion configuration strategy include usage scenarios, strategy types, recommended strategies, and backup configuration strategies, wherein the recommended strategies include influencing factors and weights corresponding to the influencing factors; In response to the diversion configuration strategy not meeting the usage requirements, the diversion configuration strategy is modified, removed from the shelves or deleted.

6. The RTA data packet diversion method according to claim 5, characterized in that: In response to receiving the data of the filled-in screening rule, the step of calling the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request includes: Obtain the fixed configuration content and the newly added branch configuration content in the data of the filled-in screening rule; Determine whether there is a diversion configuration strategy that meets the screening rule; If so, the diversion configuration strategy that meets the screening rule is used to perform user diversion to generate a diversion recommendation request; If it does not exist, the bottom-line configuration strategy is used to perform user diversion to generate a diversion recommendation request.

7. The RTA data packet diversion method according to claim 1, characterized in that: The calling of the RTA data packet classified and stored adopts the diversion scheme to perform real-time recommendation and diversion for users, and performs filter hit collision on users according to the configured business dimensions, and the steps of returning the diversion result include: Determine whether to adopt the diversion scheme to perform real-time recommendation diversion for users; If yes, query and obtain the classified stored RTA data packets through the RTA customer group interface; Setting a filter according to the configured service dimension, and determining whether the user identification information of the RTA data packet hits the filter; If there is a hit, it is determined that the RTA data packet meets the RTA customer group of the diversion solution, and after extracting the user information data of the RTA data packet, a recommendation sub-process is used to return the diversion result; If no hit is found, it is determined that the RTA data packet does not meet the RTA customer group of the diversion scheme, and after extracting the user information data of the RTA data packet, a diversion sub-process is used to perform hash diversion based on the user identification information and return the diversion result.

8. An RTA data packet splitting device, characterized in that: The device comprises: An RTA data packet management module is used to generate corresponding RTA data packets according to various usage scenarios, and classify and store the RTA data packets; A traffic diversion configuration module, used to perform traffic diversion configuration to form multiple traffic diversion configuration strategies, and set screening rules for the traffic diversion configuration strategies; A push configuration center module is used to, in response to receiving the data of the filled-in screening rule, call the corresponding diversion configuration strategy according to the screening rule to perform user diversion and generate a diversion recommendation request; A query diversion result module is used to identify the adopted diversion configuration strategy in response to receiving a diversion recommendation request, obtain commodity parameters and add them to the diversion configuration strategy to form a diversion plan; The diversion processing module is used to call the RTA data packets stored in the classification to adopt the diversion scheme to perform real-time recommendation and diversion for users, perform filter hit collision on users according to the configured business dimensions, and return the diversion results.

9. 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, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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