Information processing method and device applied to advertisement engine

Abstract

The invention discloses an information processing method and device applied to an advertisement engine. A specific embodiment of the method comprises the following steps: acquiring an advertisement appeal of a user; an executable strategy set meeting the advertisement appeal is determined through a multi-level agent, the agent of each level in the multi-level agent can determine an executable strategy set corresponding to the advertisement appeal, and the executable strategy set comprises executable strategies corresponding to a plurality of sub-processes in the whole advertisement process; and executing the executable strategies corresponding to the plurality of sub-processes in the executable strategy set through the service interfaces corresponding to the plurality of sub-processes. The multi-level intelligent agent in the advertisement engine can automatically process the whole advertisement process based on the advertisement appeal of the user, the participation threshold of the advertisement service is reduced, and the experience degree and the convenience of the user in the advertisement processing process are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the fields of computer technology, specifically to the fields of advertising and artificial intelligence technology, and in particular to an information processing method, device, computer-readable medium, and electronic device applied to an advertising engine. Background Art

[0002] As a traditional business, advertising has reached a bottleneck after a long period of development. With the help of information technology, Internet advertising has achieved explosive growth in monetization efficiency. However, the extremely high monetization efficiency of Internet advertising has promoted the continuous improvement of the advertising business. From production to delivery, to broadcasting, to effect verification, the links of an advertisement are getting longer and longer, with more and more branches and more and more sophisticated operations. The extremely large advertising industry has spawned a variety of advertising engines such as full-featured engines, simplified engines, and automated engines for advertising. However, the advertising field still has problems such as low advertiser delivery experience and high barriers to participation in the advertising business. Summary of the invention

[0003] The embodiments of the present application provide an information processing method, device, computer-readable medium, and electronic device applied to an advertising engine.

[0004] In a first aspect, an embodiment of the present application provides an information processing method applied to an advertising engine, including: obtaining a user's advertising demands; determining, through a multi-level intelligent agent, a set of executable policies that satisfy the advertising demands, wherein each level of the multi-level intelligent agent can determine an executable policy set corresponding to the advertising demands, and the executable policy set includes executable policies corresponding to each of multiple sub-processes in the entire advertising process; executing the executable policies corresponding to each of the multiple sub-processes in the executable policy set through the business interfaces corresponding to each of the multiple sub-processes.

[0005] In some examples, each level of agents in the multi-level intelligent agents includes multiple agents, and the above-mentioned determination of an executable strategy set that satisfies the advertising demand through the multi-level intelligent agents includes: processing multiple sub-demands in the advertising demand according to the execution order of the agents at each level in the multi-level intelligent agents, and in response to the presence of a target sub-demand among the multiple sub-demands for which the agents of the current level have not determined an executable strategy based on a preset voting strategy, processing the target sub-demand through agents of subsequent levels until the executable strategies corresponding to the multiple sub-demands are combined to obtain an executable strategy set.

[0006] In some examples, the first level of agents in the multi-level agents includes an agent group corresponding to each of the multiple sub-processes, the agent group includes multiple agents, and the multiple sub-demands in the advertising appeal are processed in the execution order of the agents at each level in the multi-level agents. In response to the presence of a target sub-demand among the multiple sub-demands for which the agents at the current level have not determined an executable strategy based on a preset voting strategy, the target sub-demand is processed by agents at subsequent levels until the executable strategies corresponding to the multiple sub-demands are combined to obtain an executable strategy set, including: decomposing user demands to determine the sub-demands corresponding to each of the multiple sub-processes; processing the sub-demands corresponding to each of the multiple sub-processes through the agent groups corresponding to each of the multiple sub-processes to obtain multiple candidate executable strategies corresponding to each of the multiple sub-processes; for the multiple candidate executable strategies output by each of the multiple agent groups, determining whether an executable strategy can be determined from the multiple candidate executable strategies based on the preset voting strategy; in response to the fact that the executable strategy corresponding to each of the multiple agent groups can be determined, the executable strategy set is obtained by combining the executable strategies corresponding to the multiple agent groups.

[0007] In some examples, the above-mentioned processing of multiple sub-appeals in the advertising appeal according to the execution order of the agents at each level in the multi-level agents, in response to the presence of a target sub-appeal among the multiple sub-appeals for which the agents at the current level have not determined an executable strategy based on a preset voting strategy, processing the target sub-appeal by agents at subsequent levels until an executable strategy set is obtained by combining the executable strategies corresponding to the multiple sub-appeals, further includes: in response to determining that there is a target agent group among the multiple agent groups for which an executable strategy has not been determined, processing the target sub-appeal by multiple agents at subsequent levels until an executable strategy set is obtained by determining an executable strategy from the candidate executable strategies output by each of the multiple agents at the current level based on the preset voting strategy, wherein the multiple agents at subsequent levels are determined according to the execution order; combining the executable strategies determined by other agent groups among the multiple agent groups except the target agent group and the executable strategies determined by the multiple agents at the current level.

[0008] In some examples, the above-mentioned intelligent agent groups corresponding to each of the multiple sub-processes process the sub-demands corresponding to each of the multiple sub-processes to obtain multiple candidate executable strategies corresponding to each of the multiple sub-processes, including: parsing the sub-demands corresponding to each of the multiple sub-processes through the intelligent agent groups corresponding to each of the multiple sub-processes to obtain advertising terms corresponding to each of the multiple sub-demands; for each of the multiple intelligent agent groups, determining the candidate executable strategy corresponding to the sub-process corresponding to the intelligent agent group from a preset limited result set according to the advertising terms corresponding to the sub-demand to be processed by the intelligent agent group.

[0009] In some examples, the above-mentioned multi-level intelligent agents are obtained in the following manner: determining the initial models corresponding to each of the multi-level intelligent agents based on the accuracy and parameter scale of the models in the model set, wherein the accuracy and parameter scale of the initial models corresponding to the level to which the intelligent agent belongs are positively correlated; determining the first knowledge base corresponding to each of the multi-level intelligent agents, wherein the first knowledge bases corresponding to agents of different levels include advertising knowledge of different preset dimensions; training the initial models corresponding to each of the multi-level intelligent agents through the first knowledge bases corresponding to each of the multi-level intelligent agents to obtain the multi-level intelligent agents.

[0010] In some examples, multiple sub-processes include an advertising creative sub-process, an advertising delivery sub-process, an advertising operation sub-process, and an advertising playback sub-process, and the above-mentioned business interfaces corresponding to each of the multiple sub-processes are used to execute the executable policies corresponding to each of the multiple sub-processes in the executable policy set, including: generating advertising creatives through the business interface corresponding to the advertising creative sub-process according to the executable policy corresponding to the advertising creative sub-process in the executable policy set; executing the delivery process of the advertising creatives through the business interface corresponding to the advertising delivery sub-process according to the executable policy corresponding to the advertising delivery sub-process in the executable policy set; executing the operation process of the advertising creatives through the business interface corresponding to the advertising operation sub-process according to the executable policy corresponding to the advertising operation sub-process in the executable policy set; executing the playback process of the advertising creatives through the business interface corresponding to the advertising playback sub-process according to the executable policy corresponding to the advertising playback sub-process in the executable policy set.

[0011] In some examples, the above-mentioned executable strategy corresponding to the advertising delivery sub-process in the executable strategy set executes the advertising creative delivery process, including: determining the target nodes existing in the advertising delivery graph from the advertising appeal, wherein the advertising delivery graph represents all advertising links in the advertising delivery process; determining the advertising delivery links between the target nodes from the advertising delivery graph; and using the executable strategy corresponding to the advertising delivery sub-process to execute the advertising creative delivery process according to the advertising delivery links.

[0012] In some examples, the above-mentioned determining the advertisement delivery link between the target nodes from the advertisement delivery graph includes: using a depth-first traversal algorithm to determine the advertisement delivery link between the target nodes from the advertisement delivery graph.

[0013] In some examples, the above-mentioned advertising delivery graph is created by: extracting key points related to the delivery business in the advertising delivery sub-process; merging the same key points to obtain an advertising delivery graph, wherein the nodes in the advertising delivery graph include node identifiers and node values ​​representing the advertising revenue of the nodes.

[0014] In some examples, the above-mentioned advertisement delivery links include multiple links, and the above-mentioned method also includes: using a preset compression method to compress each of the multiple advertisement delivery links, and displaying the compressed multiple advertisement delivery links.

[0015] In some examples, the above method further includes: determining an advertisement delivery link to be executed from a plurality of advertisement delivery links according to the received link selection operation.

[0016] In some examples, the method further includes: determining a supplementary appeal based on a received supplementary appeal operation; determining an updated target node from an advertising delivery graph based on the advertising appeal and the supplementary appeal; and determining an advertising delivery link between the updated target nodes from the advertising delivery graph.

[0017] In some examples, the above method also includes: determining creative materials in the advertising creative submitted by the user; determining similar materials of the creative materials from the second knowledge base through the advertising review intelligent agent; generating new materials based on the similar materials through the advertising review intelligent agent; and determining the advertising review results based on the similarity between the new materials and the advertising creative of the advertising users in the advertising user set.

[0018] In some examples, determining similar materials of the creative material from the second knowledge base includes: determining a first material feature of the creative material; and determining the similar material based on a similarity between the first material feature and a second material feature of a material in the second knowledge base.

[0019] In some examples, the second knowledge base includes an advertising knowledge base and a brand knowledge base, and the similar materials are determined based on the similarity between the first material feature and the second material feature of the material in the second knowledge base, including: determining a plurality of similar materials ranked at the top in similarity based on the similarity between the first material feature and the second material feature of the material in the advertising knowledge base; determining a plurality of similar materials ranked at the top in similarity and having a similarity exceeding a preset similarity threshold based on the similarity between the first material feature and the second material feature of the material in the brand knowledge base.

[0020] In some examples, the above method also includes: determining the third material feature of the creative material in the historical advertising creative submitted by the user; fusing the first material feature and the third material feature to obtain a fused feature; and the above-mentioned generating new materials based on similar materials includes: generating new materials based on the second material feature and the fused feature corresponding to the similar materials.

[0021] In some examples, the above method also includes: adjusting a preset similarity threshold according to the operation of the advertising engine.

[0022] In some examples, the obtaining of the user's advertising demands includes: obtaining the user's advertising demands represented based on natural language.

[0023] In some examples, the above method also includes: determining the agent at each level of the multi-level agent according to the received selection operation.

[0024] In the second aspect, an embodiment of the present application provides an information processing device applied to an advertising engine, comprising: an acquisition unit, configured to acquire the user's advertising demands; a determination unit, configured to determine an executable policy set that satisfies the advertising demands through multi-level intelligent agents, wherein each level of the multi-level intelligent agents can determine the executable policy set corresponding to the advertising demands, and the executable policy set includes executable policies corresponding to each of the multiple sub-processes in the entire advertising process; an execution unit, configured to execute the executable policies corresponding to each of the multiple sub-processes in the executable policy set through the business interfaces corresponding to each of the multiple sub-processes.

[0025] In some examples, the agents at each level in the multi-level agents include multiple agents, and the above-mentioned determination unit is further configured to: process multiple sub-demands in the advertising appeal in the order of execution of the agents at each level in the multi-level agents, and in response to the presence of a target sub-appeal among the multiple sub-appeals for which the agents at the current level have not determined an executable strategy based on a preset voting strategy, process the target sub-appeal by agents at subsequent levels until the executable strategies corresponding to the multiple sub-appeals are combined to obtain an executable strategy set.

[0026] In some examples, the first-level agents in the multi-level agents include agent groups corresponding to multiple sub-processes, each agent group includes multiple agents, and the above-mentioned determination unit is further configured to: decompose user demands and determine the sub-demands corresponding to each of the multiple sub-processes; process the sub-demands corresponding to each of the multiple sub-processes through the agent groups corresponding to each of the multiple sub-processes to obtain multiple candidate executable strategies corresponding to each of the multiple sub-processes; for the multiple candidate executable strategies output by each of the multiple agent groups, determine whether an executable strategy can be determined from the multiple candidate executable strategies based on a preset voting strategy; in response to the fact that for each of the multiple agent groups, the executable strategy corresponding to the agent group can be determined, and the executable strategy set is obtained by combining the executable strategies corresponding to each of the multiple agent groups.

[0027] In some examples, the above-mentioned determination unit is further configured to: in response to determining that there is a target intelligent agent group among multiple intelligent agent groups for which an executable strategy has not been determined, process the target sub-demand through multiple intelligent agents at subsequent levels until an executable strategy is determined from the candidate executable strategies output by each of the multiple intelligent agents at the current level based on a preset voting strategy, wherein the multiple intelligent agents at subsequent levels are determined according to an execution order; and obtain an executable strategy set by combining the executable strategies determined by other intelligent agent groups except the target intelligent agent group among the multiple intelligent agent groups and the executable strategies determined by the multiple intelligent agents at the current level.

[0028] In some examples, the above-mentioned determination unit is further configured to: parse the sub-demands corresponding to each of the multiple sub-processes through the intelligent agent groups corresponding to each of the multiple sub-processes, and obtain the advertising terms corresponding to each of the multiple sub-demands; for each of the multiple intelligent agent groups, determine the candidate executable strategy corresponding to the sub-process corresponding to the intelligent agent group from a preset limited result set according to the advertising terms corresponding to the sub-demand to be processed by the intelligent agent group.

[0029] In some examples, the above-mentioned multi-level intelligent agents are obtained in the following manner: determining the initial models corresponding to each of the multi-level intelligent agents based on the accuracy and parameter scale of the models in the model set, wherein the accuracy and parameter scale of the initial models corresponding to the level to which the intelligent agent belongs are positively correlated; determining the first knowledge base corresponding to each of the multi-level intelligent agents, wherein the first knowledge bases corresponding to agents of different levels include advertising knowledge of different preset dimensions; training the initial models corresponding to each of the multi-level intelligent agents through the first knowledge bases corresponding to each of the multi-level intelligent agents to obtain the multi-level intelligent agents.

[0030] In some examples, multiple sub-processes include an advertising creative sub-process, an advertising delivery sub-process, an advertising operation sub-process and an advertising playback sub-process, and the above-mentioned execution unit is further configured to: generate advertising creativity through the business interface corresponding to the advertising creative sub-process and according to the executable policy corresponding to the advertising creative sub-process in the executable policy set; execute the advertising creative delivery process through the business interface corresponding to the advertising delivery sub-process and according to the executable policy corresponding to the advertising delivery sub-process in the executable policy set; execute the algorithms involved in the advertising process through the business interface corresponding to the advertising operation sub-process and according to the executable policy corresponding to the advertising operation sub-process in the executable policy set; execute the advertising creative playback process through the business interface corresponding to the advertising playback sub-process and according to the executable policy corresponding to the advertising playback sub-process in the executable policy set.

[0031] In some examples, the above-mentioned execution unit is further configured to: determine the target nodes existing in the advertising delivery graph from the advertising appeal, wherein the advertising delivery graph represents all advertising links in the advertising delivery process; determine the advertising delivery links between the target nodes from the advertising delivery graph; and use the executable strategy corresponding to the advertising delivery sub-process to execute the advertising creative delivery process according to the advertising delivery link.

[0032] In some examples, the execution unit is further configured to: use a depth-first traversal algorithm to determine the advertisement delivery links between target nodes from the advertisement delivery graph.

[0033] In some examples, the above-mentioned advertising delivery graph is created by: extracting key points related to the delivery business in the advertising delivery sub-process; merging the same key points to obtain an advertising delivery graph, wherein the nodes in the advertising delivery graph include node identifiers and node values ​​representing the advertising revenue of the nodes.

[0034] In some examples, the above-mentioned advertising delivery links include multiple links, and the above-mentioned device also includes: a compression display unit, which is configured to use a preset compression method to compress each of the multiple advertising delivery links and display the compressed multiple advertising delivery links.

[0035] In some examples, the above-mentioned apparatus further includes: a selection unit configured to determine an advertisement delivery link to be executed from a plurality of advertisement delivery links according to a received link selection operation.

[0036] In some examples, the above-mentioned device also includes: a supplementing unit, configured to determine a supplementary appeal based on a received supplementary appeal operation; determine an updated target node from an advertising delivery graph based on the advertising appeal and the supplementary appeal; and determine an advertising delivery link between the updated target nodes from the advertising delivery graph.

[0037] In some examples, the above-mentioned device also includes: a review unit, configured to determine the creative materials in the advertising creativity submitted by the user; determine similar materials of the creative materials from the second knowledge base through the advertising review intelligent agent; generate new materials based on the similar materials through the advertising review intelligent agent; determine the advertising review result based on the similarity between the new materials and the advertising creativity of the advertising users in the advertising user set.

[0038] In some examples, the review unit is further configured to: determine a first material feature of the creative material; and determine similar materials based on a similarity between the first material feature and a second material feature of a material in the second knowledge base.

[0039] In some examples, the second knowledge base includes an advertising knowledge base and a brand knowledge base, and the review unit is further configured to: determine a plurality of similar materials ranked at the top in similarity based on the similarity between the first material feature and the second material feature of the material in the advertising knowledge base; determine a plurality of similar materials ranked at the top in similarity and whose similarity exceeds a preset similarity threshold based on the similarity between the first material feature and the second material feature of the material in the brand knowledge base.

[0040] In some examples, the above-mentioned device also includes: a fusion unit, configured to determine the third material feature of the creative material in the historical advertising creative submitted by the user; fuse the first material feature and the third material feature to obtain a fusion feature; and the above-mentioned review unit is further configured to: generate a new material based on the second material feature and the fusion feature corresponding to similar materials.

[0041] In some examples, the apparatus further includes: an adjustment unit configured to adjust a preset similarity threshold according to an operation condition of the advertising engine.

[0042] In some examples, the acquisition unit is further configured to: acquire the user's advertising demands represented by natural language.

[0043] In some examples, the above-mentioned device also includes: an agent determination unit, which is configured to determine the agent of each level in the multi-level agent according to the received selection operation.

[0044] In a third aspect, an embodiment of the present application provides a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the first aspect is implemented.

[0045] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation manner of the first aspect.

[0046] The information processing method and device applied to the advertising engine provided in the embodiment of the present application obtain the advertising demands of users; determine the executable policy set that meets the advertising demands through multi-level intelligent agents, wherein the intelligent agents at each level in the multi-level intelligent agents can determine the executable policy set corresponding to the advertising demands, and the executable policy set includes executable policies corresponding to each of the multiple sub-processes in the entire advertising process; execute the executable policies corresponding to each of the multiple sub-processes in the executable policy set through the business interfaces corresponding to each of the multiple sub-processes, thereby providing an information processing method applied to the advertising engine, wherein the multi-level intelligent agents in the advertising engine can automatically process the entire advertising process based on the advertising demands of users, thereby lowering the participation threshold of the advertising business and improving the user experience and convenience in the advertising processing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0048] Figure 1 is an exemplary system architecture diagram in which an embodiment of the present application may be applied;

[0049] Figure 2 is a flow chart of an embodiment of an information processing method applied to an advertising engine according to the present application;

[0050] Figure 3 is a schematic diagram of the structure of a multi-level intelligent agent according to this embodiment;

[0051] Figure 4 is a schematic diagram of an application scenario of the information processing method applied to an advertisement engine according to the present embodiment;

[0052] Figure 5 is a flow chart of another embodiment of an information processing method applied to an advertisement engine according to the present application;

[0053] Figure 6 is a schematic diagram of an advertisement placement diagram according to this embodiment;

[0054] Figure 7 is a schematic diagram of a user interface of multiple compressed advertisement delivery links according to this embodiment;

[0055] Figure 8 is a flow chart of another embodiment of an information processing method applied to an advertisement engine according to the present application;

[0056] Fig. 9 is a structural diagram of an embodiment of an information processing device applied to an advertisement engine according to the present application;

[0057] Fig.10 It is a structural diagram of a computer system suitable for implementing the embodiments of the present application. DETAILED DESCRIPTION

[0058] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It should also be noted that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0059] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0060] It should be noted that the collection, collection, updating, analysis, processing, use, transmission, storage and other aspects of user personal information involved in the technical solution of this disclosure are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security and national security.

[0061] Figure 1 An exemplary architecture 100 to which the information processing method and apparatus for an advertisement engine of the present application can be applied is shown.

[0062] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The communication connection between the terminal devices 101, 102, 103 constitutes a topological network, and the network 104 is used to provide a medium for the communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0063] The terminal devices 101, 102, 103 can interact with the server 105 through the network 104 to receive or send data, etc. The terminal devices 101, 102, 103 can be hardware devices or software that support network connection for data interaction and data processing. When the terminal devices 101, 102, 103 are hardware, they can be various electronic devices that support network connection, information acquisition, interaction, display, processing and other functions, including but not limited to smart phones, car computers, tablet computers, e-book readers, laptop portable computers and desktop computers, etc. When the terminal devices 101, 102, 103 are software, they can be installed in the electronic devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0064] Server 105 may be a server that provides various services, for example, a background processing server that obtains the user's advertising demands through terminal devices 101, 102, 103, and generates and executes executable policy sets that meet the advertising demands through multi-level agents. As an example, server 105 may be a cloud server.

[0065] It should be noted that the server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules (for example, software or software modules used to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0066] It should also be noted that the information processing method applied to the advertising engine provided in the embodiments of the present application can be executed by a server, or by a terminal device, or by the server and the terminal device in cooperation with each other. Accordingly, the various parts (such as various units) included in the information processing device applied to the advertising engine can be all set in the server, or all set in the terminal device, or can be set in the server and the terminal device respectively.

[0067] It should be understood that Figure 1 The number of terminal devices, networks and servers in the system is only illustrative. Any number of terminal devices, networks and servers may be provided as required. When the electronic device on which the information processing method applied to the advertising engine is running does not need to transmit data with other electronic devices, the system architecture may only include the electronic device (e.g., server or terminal device) on which the information processing method applied to the advertising engine is running.

[0068] Continue to refer Figure 2, shows a process 200 of an embodiment of an information processing method applied to an advertisement engine, comprising the following steps:

[0069] Step 201, obtaining the user's advertising demands.

[0070] In this embodiment, the execution subject (eg Figure 1 The terminal device or server in the network can obtain the user's advertising demands remotely or locally through a wired network connection or a wireless network connection.

[0071] Users can input advertising appeals to the advertising engine based on advertising terms and / or non-advertising terms. For example, the advertising engine uses public web services to provide front-end pages and back-end servers, and provides login and advertising delivery functions; users (e.g., merchants) use advertising platform accounts to log in to the advertising engine and enter the advertising delivery function page. The front-end page supports the input function of advertising appeals to support users to input advertising appeals.

[0072] Advertisement appeals can be represented in the form of text, voice, picture, video, etc. For advertisement appeals, the advertisement engine supports the above-mentioned multiple input forms.

[0073] The advertising engine also supports multiple inputs of advertising demands. The advertising engine provides a separate supplementary entry, where users can modify or supplement the initial advertising demands that have been entered; the advertising engine modifies the version number and input time of the initial advertising demands to determine and process the complete advertising demands. It should be noted that the supplementary demands obtained by the supplementary entry have a higher priority than the initial advertising demands, and the priority can be distinguished by serial number or weight level.

[0074] In some optional implementations of this embodiment, the execution subject may perform step 201 in the following manner: obtaining the user's advertising demands represented by natural language.

[0075] In real life, advertising business is extremely complicated and many users do not understand advertising business. It is more in line with users' usage needs for users to express their advertising demands based on natural language. In actual application scenarios, users can input advertising demands based on multi-round dialogues. The above-mentioned execution subject can maintain context information by introducing memory networks or long short-term memory networks (LSTM) to ensure a global understanding of the questions or demands raised by users.

[0076] As an example, the advertising appeal entered by the user is: Use a budget of 5,000 yuan to promote the product of sku=123, and I hope to have 300 exposures.

[0077] At this point, the record of the above advertising appeal in the database is: id=1, lv=1, v1=Use a budget of 5,000 yuan to promote the product sku=123, and I hope to have 300 exposures.

[0078] The user can make modifications: Using a budget of 5,000 yuan to promote product sku=123, I hope to have 600 exposures.

[0079] At this time, the record in the database is: id=1, lv=1, v2=using a budget of 5,000 yuan to promote the product sku=123, and I hope to have 300 exposures.

[0080] The user can add an additional entry: 20 more conversions are desired.

[0081] At this time, the record in the database is: id = 1, lv = 1, v2 = Use a budget of 5,000 yuan to promote the product sku = 123, and I hope to have 300 exposures;

[0082] id=1, lv=2, v1=20 more conversions are desired.

[0083] As in the above example, in order to ensure the feasibility of user demands, user demands generally include at least two types of demand information: advertising budget and advertising delivery effect. Delivery target refers to the revenue that the advertiser hopes to obtain through this advertisement.

[0084] The delivery target can be a classification of effects, such as: get 1,000 exposures, get 500 clicks, get 20 conversions; it can also be a classification of business, such as: promote stores, promote products, promote activity pages; it can also be a vague description, such as: get good off-site effects, mainly new customers on the site. The delivery target can be one of the above, or a combination of multiple. Budget refers to the cost that the advertiser can provide, mainly including the amount and period.

[0085] Step 202, through multi-level intelligent agents, determine an executable strategy set that meets the advertising demands.

[0086] In this embodiment, the execution subject can determine an executable strategy set that meets the advertising demands through a multi-level intelligent agent. Each level of the multi-level intelligent agent can determine an executable strategy set corresponding to the advertising demands, and the executable strategy set includes executable strategies corresponding to each of the multiple sub-processes in the entire advertising process.

[0087] Each level of the multi-level agent includes at least one agent, such as a neural network model such as a recurrent neural network model and a residual network model, or a large language model such as GPT (Generative Pre-Trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers). Agents at different levels can use the same model structure or different model structures.

[0088] In this embodiment, the generation process of the multi-level agent is as follows:

[0089] First, define the specific problems that need to be solved by each of the multiple sub-processes in the entire advertising process, such as advertising text generation, advertising creative production, advertising effect prediction, user behavior analysis, etc. Then, collect advertising data related to the defined problems, including advertising copy, user comments, social media content and other data under various advertising types and themes. Then, preprocess and clean the collected advertising data, including text segmentation, removal of stop words, processing of missing values ​​and outliers, etc., to ensure the quality of the data. Then, build a knowledge graph based on the advertising knowledge obtained after cleaning, including advertising industry terms, key concepts, brand information, etc., which helps the model better understand the advertising content. Then, select large language models suitable for natural language processing tasks, such as GPT-3, BERT, etc. These models perform well in understanding and generating data. Finally, use data from the advertising field to fine-tune the selected large language model to better adapt it to the specific needs of each advertising task in multiple sub-processes.

[0090] In this embodiment, the execution subject can determine the executable strategy set that meets the advertising demand through any layer of agents in the multi-level agents. For example, an agent at one layer can be randomly selected from the multi-level agents to process the advertising demand, or a load balancing strategy can be used to select an agent at one layer from the multi-level agents to process the advertising demand.

[0091] As an example, the multiple sub-processes include an advertising creative sub-process, an advertising delivery sub-process, an advertising operation sub-process, and an advertising playback sub-process. Among them, the advertising creative sub-process is used to generate advertising creatives that meet advertising demands, the advertising delivery sub-process is used to guide the delivery process of advertising creatives, the advertising operation sub-process is used to determine the data processing algorithm involved in the entire advertising process, and the advertising playback sub-process is used to guide the playback process of advertising creatives.

[0092] The executable strategies of the advertising delivery sub-process mainly include the strategies concerned by the advertising delivery business, including advertising delivery, financial settlement, report viewing, etc.

[0093] The executable strategies of the advertising creative sub-process mainly include the strategies concerned with advertising creative production, including advertising plan / unit / creative generation, live broadcast / short video / graphics and other content processing, SKU (Stock Keeping Unit, minimum inventory unit) and other product processing, URL (uniform resource locator) and other landing page processing, etc.

[0094] The executable strategies of the advertising operation sub-process mainly include the strategies focused on by advertising data and algorithms, including advertising models, logs, follow-up, anti-fraud, etc.

[0095] The executable strategies of the ad playback sub-process mainly include the strategies concerned with ad playback, including user portrait, adserver, userserver, ad recall, gateway, etc.

[0096] As an example, the user's advertising demand is: use a budget of 5,000 yuan to promote product sku=123. I hope to have 300 exposures and 20 conversions. Among them, I require the promotion to be targeted at plus members and do not want it to appear outside the platform. The total run time is 3 days.

[0097] The executable strategies corresponding to multiple sub-processes are:

[0098] Advertising placement sub-process: merchant pin, budget: 5,000 yuan, promotion plan: 3 days, promotion type: product promotion; automatically complete financial settlement requirements and report requirements.

[0099] Ad creative sub-process: SKU: 123, automatically complete the landing page URL, product main image, etc.

[0100] Advertising operation sub-process: Exposure requirement: 300, Conversion requirement: 20

[0101] Advertisement playback sub-process: Targeting requirements: plus membership, Ad space requirements: within the platform

[0102] In some optional implementations of this embodiment, the agents at each level of the multi-level agent include multiple agents. The multiple agents may be agents with the same model structure or agents with different model structures.

[0103] The above-mentioned execution entity can execute the above-mentioned step 202 in the following manner: according to the execution order of the intelligent agents at each level in the multi-level intelligent agents, multiple sub-demands in the advertising appeal are processed, and in response to the presence of a target sub-demand among the multiple sub-demands for which the intelligent agents at the current level have not determined an executable strategy based on the preset voting strategy, the target sub-demand is processed by the intelligent agents at the subsequent levels until the executable strategies corresponding to the multiple sub-demands are combined to obtain an executable strategy set.

[0104] As an example, the multi-level agent includes a first level, a second level and a third level for providing three levels of agents, and the execution order is the first level, the second level and the third level.

[0105] Continue to refer Figure 3 , shows a schematic diagram of the structure of a multi-level intelligent agent, including multiple intelligent agent groups 301 at the first level, multiple intelligent agents 302 at the second level and multiple intelligent agents 303 at the third level.

[0106] For each sub-appeal in the advertising appeal, it is necessary to determine the executable strategy corresponding to the sub-appeal, so as to combine the executable strategies corresponding to multiple sub-appeals to obtain an executable strategy set.

[0107] The preset voting strategy is used to determine the executable strategy in the voting result set for each sub-demand from the candidate executable strategies output by multiple intelligent agents at one level, such as the unanimous recognition strategy, the majority strategy, etc.

[0108] The multiple intelligent agents at the first level generate candidate executable strategies according to each sub-demand in the advertisement appeal respectively, and vote on the multiple candidate executable strategies; for each sub-appeal, an executable strategy is determined from the multiple candidate executable strategies corresponding to the sub-appeal, and the subsequent executable strategy processing process is terminated.

[0109] In response to the fact that there is a target sub-demand among multiple sub-demands for which the multiple intelligent agents at the first level are unable to determine an executable strategy based on a preset voting strategy, multiple intelligent agents at the second level output multiple candidate executable strategies for the target sub-demand, and vote on the multiple candidate executable strategies. In response to an executable strategy being determined therefrom, the subsequent executable strategy processing process is terminated.

[0110] In response to the inability of multiple agents at the second level to determine an executable strategy for the target sub-demand based on the preset voting strategy, multiple agents at the third level output multiple candidate executable strategies for the target sub-demand, and determine an executable strategy from them. Since the third level is the bottom level, an executable strategy for the target sub-demand must be determined.

[0111] In this implementation, the multi-level intelligent agents have an execution order. Based on the execution order, the executable strategy set of the current level is determined by a preset voting strategy. The multi-level intelligent agents cooperate with the voting mechanism to help alleviate the model hallucination problem and improve the response speed of the intelligent agents, as well as the orderliness and efficiency of the intelligent agents in the data processing process.

[0112] In some optional implementations of this embodiment, continue to refer to Figure 3 The first-level agent in the multi-level agent includes agent groups corresponding to multiple sub-processes, and the agent group includes multiple agents. As an example, the advertising creative sub-process, the advertising delivery sub-process, the advertising operation sub-process, and the advertising playback sub-process each correspond to an agent group.

[0113] In this implementation, the execution subject may perform the process of determining the executable policy set in the following manner:

[0114] First, break down user demands and determine the sub-demands corresponding to multiple sub-processes.

[0115] The user demands entered by the user are disassembled to identify the demand segments (sub-demands) belonging to the advertising creative sub-process, advertising delivery sub-process, advertising operation sub-process and advertising playback sub-process. For the sub-demands corresponding to different sub-processes, they are distributed to the agent groups under the corresponding sub-processes for processing.

[0116] Second, through the agent groups corresponding to the multiple sub-processes, the sub-demands corresponding to the multiple sub-processes are processed to obtain multiple candidate executable strategies corresponding to the multiple sub-processes.

[0117] For each sub-process in the multiple sub-processes, the sub-demands corresponding to the sub-process are processed respectively by multiple agents in the agent group corresponding to the sub-process to obtain multiple candidate executable strategies corresponding to the sub-process.

[0118] Third, for the multiple candidate executable strategies output by the multiple agent groups respectively, determine whether an executable strategy can be determined from the multiple candidate executable strategies based on a preset voting strategy.

[0119] For each of the multiple agent groups, based on a preset voting strategy, vote on the candidate executable strategies output by each of the multiple agents in the current agent group to determine whether an executable strategy can be determined from the multiple candidate executable strategies.

[0120] Fourth, in response to each of the multiple agent groups, an executable strategy corresponding to the agent group can be determined, and the executable strategies corresponding to the multiple agent groups are combined to obtain an executable strategy set.

[0121] When multiple agent groups can determine corresponding executable strategies, the executable strategies corresponding to the multiple agent groups can be combined to obtain an executable strategy set.

[0122] In this implementation, a method for determining an executable strategy set based on a first-level intelligent agent is provided, and multiple sub-processes are assigned to corresponding intelligent agent groups, each performing their respective duties, further improving the efficiency and accuracy of determining the executable strategy set.

[0123] In some optional implementations of this embodiment, the execution subject may perform the process of determining the executable policy set in the following manner:

[0124] First, in response to determining that there is a target agent group among multiple agent groups for which an executable strategy has not been determined, multiple agents at subsequent levels process the target sub-demands until an executable strategy is determined from the candidate executable strategies output by each of the multiple agents at the current level based on a preset voting strategy.

[0125] Among them, multiple agents in subsequent layers are determined according to the execution order.

[0126] In this implementation, the agents in the subsequent layers can realize the functions of the agents in the previous layers. Continuing with the three-layer example, the agents in the second layer and the agents in the third layer both have the function of processing sub-demands and determining executable strategies by the agent groups corresponding to the multiple sub-processes in the first layer.

[0127] As an example, the target agent group for which no executable strategy has been determined among the multiple agent groups is the agent group corresponding to the advertising creative sub-process. At this time, the sub-demand corresponding to the advertising creative sub-process is input into the multiple agents at the second level, and the multiple candidate executable strategies output are voted to determine the executable strategy; otherwise, the sub-demand corresponding to the advertising creative sub-process is input into the multiple agents at the third level to determine the executable strategy.

[0128] Then, the executable strategies determined by other agent groups except the target agent group among the multiple agent groups and the executable strategies determined by multiple agents at the current level are combined to obtain an executable strategy set.

[0129] Continuing with the above example, the executable strategies corresponding to the advertising placement sub-process, advertising operation sub-process and advertising playback sub-process in the first level are combined with the executable strategies corresponding to the advertising creative sub-process determined by the intelligent agent at the second level to obtain an executable strategy set.

[0130] In this implementation, a method for determining an executable strategy set in combination with multi-level intelligent agents is provided, which further improves the efficiency and accuracy of determining the executable strategy set.

[0131] In some optional implementations of this embodiment, the execution subject may perform the second step in the following manner:

[0132] Firstly, through the agent groups corresponding to the multiple sub-processes, the sub-demands corresponding to the multiple sub-processes are analyzed to obtain the advertising terms corresponding to the multiple sub-demands.

[0133] In this implementation, the above-mentioned execution entity determines the advertising terms corresponding to each of the multiple sub-appeals based on the following process: (1) Using natural language processing technology to understand the advertising appeals put forward by the user. Natural language understanding includes steps such as lexical analysis, grammatical analysis, and semantic analysis to extract key information from the advertising appeals. (2) Identify entities related to the advertising field from the advertising appeals, such as brand names, product types, advertising goals, etc. (3) Perform sentiment analysis on the user's language to understand the user's emotional tendencies in order to better understand their needs and expectations. (4) Based on the results of natural language understanding, entity recognition, and sentiment analysis, parse the advertising appeals into advertising terms.

[0134] There may be parts in the advertising appeal that are not involved in the above multiple sub-processes. In this case, the automatic engine can process the parts that are not involved in the above multiple sub-processes to fully and comprehensively analyze the user's advertising appeal. The automatic engine can provide intelligent services for advertising. After the user sets the necessary logic on the advertising link, the algorithm decision mechanism helps the user complete the advertising behavior.

[0135] Continuing with the above example, the user's advertising appeal is:

[0136] With a budget of 5,000 yuan, I want to promote product sku=123 with 300 impressions and 20 conversions. I want the promotion to be targeted at plus members and I don’t want it to appear outside the platform. The promotion will be run for a total of 3 days.

[0137] The parsed advertising terms are:

[0138] Merchant pin, budget: 5,000 yuan, sku: 123, promotion type: product promotion, exposure requirement: 300, conversion requirement: 20, targeting requirement: plus member, advertising position requirement: within the platform, promotion plan: 3 days.

[0139] Then, for each of the multiple agent groups, based on the advertising terms corresponding to the sub-appeal to be processed by the agent group, a candidate executable strategy corresponding to the sub-process of the agent group is determined from a preset limited result set.

[0140] The limited result set includes all executable policies involved in the advertising process. Different sub-processes can have different limited result sets. The advertising engine has a management function for the limited result set, and relevant technicians can add, delete, modify, query, and other operations on the results in the limited result set.

[0141] Continuing with the above example, based on the advertising terms corresponding to the multiple sub-appeals, the executable strategies corresponding to the multiple sub-processes can be determined as follows:

[0142] Advertising placement sub-process: merchant pin, budget: 5,000 yuan, promotion plan: 3 days, promotion type: product promotion; automatically complete financial settlement requirements and report requirements.

[0143] Advertising creative sub-process: sku: 123, automatically complete the landing page URL, product main picture, etc.

[0144] Advertising operation sub-process: Exposure requirement: 300, Conversion requirement: 20

[0145] Advertisement playback sub-process: Targeting requirements: plus membership, Ad space requirements: within the platform

[0146] In this implementation, the advertising terms of the sub-appeal are first determined and then the executable strategy is determined based on the advertising terms. The output executable strategy is limited by limiting the result set, which further improves the accuracy of the executable strategy and the adaptability of the executable strategy to the sub-process.

[0147] In some optional implementations of this embodiment, the above-mentioned execution subject can obtain a multi-level intelligent agent in the following manner:

[0148] First, according to the accuracy and parameter scale of the models in the model set, the initial models corresponding to the multi-level intelligent agents are determined.

[0149] Among them, there is a positive correlation between the level to which the intelligent agent belongs and the accuracy and parameter scale of the initial model corresponding to the level.

[0150] The model collection includes a variety of models suitable for processing advertising tasks. According to the accuracy of the model, the model can be divided into low-precision model, half-precision model and full-precision model; according to the parameter scale of the model, the model can be divided into low-parameter scale model, medium-parameter scale model and high-parameter scale model.

[0151] Continuing with the example of three-level agents, the first-level agents tend to choose half-precision, low-parameter-scale models; the second-level agents tend to choose half / full-precision, medium-parameter-scale models; and the third-level agents tend to choose full-precision, high-parameter-scale models.

[0152] Then, the first knowledge base corresponding to each of the multi-level intelligent agents is determined.

[0153] Among them, the first knowledge base corresponding to intelligent agents at different levels includes advertising knowledge of different preset dimensions.

[0154] As an example, the preset dimensions include the advertising industry, the advertising enterprise, and the advertising business line in the enterprise. The advertising industry, the advertising enterprise, and the advertising business line correspond to the first-level intelligent agent, the second-level intelligent agent, and the third-level intelligent agent in sequence.

[0155] The advertising knowledge corresponding to the advertising industry is the general knowledge of the advertising industry. For example, billing models include CPM (Cost Per Mille), CPC (Cost Per Click), CPT (Cost Per Time), CPS (Cost Per Sale), etc.; the advertising knowledge of advertising companies is the general knowledge of the advertising business within the advertising companies. For example, the advertising business of an advertising company includes search advertising express, recommended advertising shopping touchpoints, etc.; the advertising knowledge of advertising business lines is the knowledge related to specific advertising business lines in advertising companies, such as the method of producing express advertising plans, etc.

[0156] The execution subject can provide a hierarchical storage function of the first knowledge base, which can be stored in various public databases (e.g., relational databases, non-relational databases, object-oriented databases). For each type of knowledge, the storage in the database must include two fields: questions and answers, preferably including the contribution channel of the stored knowledge, the writing time, etc.

[0157] The first knowledge base sets up a management backend, and the corresponding knowledge base administrator enters the corresponding knowledge and stores it in the database corresponding to the first knowledge base, and supports the administrator to manage the existing knowledge. For example, add a knowledge expiration management function, set an effective period for each stored knowledge, and ensure that the first knowledge base only provides valid knowledge.

[0158] The first knowledge base has a data synchronization function, which can synchronize data from other database systems to the database of the first knowledge base to enrich the first knowledge base. In addition, in order to further improve the richness of knowledge in the first knowledge base, the advertising-related knowledge data generated by the multi-level intelligent agent can also be transmitted back to the first knowledge base and stored.

[0159] Finally, the initial models corresponding to the multi-level intelligent agents are trained through the first knowledge bases corresponding to the multi-level intelligent agents to obtain the multi-level intelligent agents.

[0160] In this implementation, a specific method for obtaining a multi-level intelligent agent is provided, which improves the accuracy of the multi-level intelligent agent.

[0161] In some optional implementations of this embodiment, the above-mentioned execution subject may also perform the following operations: determining a multi-level intelligent agent for processing the advertising appeal according to the received selection operation.

[0162] As an example, for each of the multiple levels, the user can determine at least one agent in the level from the agent set by selecting. The agent set includes multiple agents, each of which can independently process the advertising business of the entire advertising process, or the agents can cooperate with each other to process the advertising business of the entire advertising process.

[0163] In this implementation, users can select agents at each level based on their own needs, which helps to further improve the user experience and the matching degree between the multi-level agents and the users.

[0164] Step 203 , executing the executable policies corresponding to the multiple sub-processes in the executable policy set through the business interfaces corresponding to the multiple sub-processes.

[0165] In this embodiment, the execution subject may execute the executable policies corresponding to the multiple sub-processes in the executable policy set through the business interfaces corresponding to the multiple sub-processes.

[0166] As an example, for each of the multiple sub-processes, the execution subject can transmit the executable strategy corresponding to the sub-process to the business interface corresponding to the sub-process. The business interface processes according to the execution plan represented by the executable strategy; the business interfaces corresponding to the multiple sub-processes cooperate with each other to process the complete advertising process.

[0167] In some optional implementations of this embodiment, the execution subject may perform step 203 in the following manner:

[0168] Through the business interface corresponding to the advertising creative sub-process, the advertising creative is generated according to the executable strategy corresponding to the advertising creative sub-process in the executable strategy set.

[0169] Delivery is the most complex business in the advertising process. The business interface corresponding to the creative advertising sub-process must include at least three types of interfaces: advertising delivery interface, report viewing interface, and financial interface. The interface can be either a remote procedure call (rpc) interface or a browser call (http) interface. If it is a remote procedure call interface, a server needs to be provided; if it is a browser call interface, a server needs to be provided.

[0170] The advertising delivery interface should provide the following functions: plan making, business selection, and association designation with other businesses; the report viewing interface should provide the following functions: report display; the financial interface should provide the following functions: deductions, account and financial information maintenance.

[0171] Through the business interface corresponding to the advertisement delivery sub-process, the advertisement creative delivery process is executed according to the executable strategy corresponding to the advertisement delivery sub-process in the executable strategy set.

[0172] Advertising creativity mainly adopts the generative approach, that is, providing a creative generation interface, receiving user-specified creative generation demands, such as types (pictures, videos, etc.), and using executable strategies corresponding to the creative generation demands to generate advertising creativity.

[0173] Through the business interface corresponding to the advertising operation sub-process, the algorithms involved in the advertising process are executed according to the executable strategy corresponding to the advertising operation sub-process in the executable strategy set.

[0174] The business interface corresponding to the advertising operation sub-process mainly provides the specification of models and algorithms. For example, users can choose exposure priority, add-to-cart priority, conversion priority, etc.; they can also choose different types of DMP (Data Management Platform) audience targeting.

[0175] Through the business interface corresponding to the advertisement playing sub-process, the advertisement creative playing process is executed according to the executable strategy corresponding to the advertisement playing sub-process in the executable strategy set.

[0176] The business interface corresponding to the ad playback sub-process mainly provides the specification of the playback link, such as portrait selection, recall sorting logic, etc.

[0177] In this embodiment, each sub-process may correspond to multiple business interfaces, and there is a corresponding relationship between the executable strategy and the business interface. The above-mentioned execution subject or the electronic device connected to the above-mentioned execution subject is provided with a policy interface dictionary for determining the business interface matched by each executable strategy. Thus, the executable strategy is mapped to an interface call through the policy interface dictionary. For all executable strategies matching the business interface, it is necessary to formulate the execution order and priority that conforms to the delivery business.

[0178] For policies without matching interfaces, they are specially marked so that the business interface that can execute the policy can be completed through the automatic engine.

[0179] In this implementation, a specific implementation method of an executable policy set is provided, which improves the orderliness and efficiency of the executable policies during execution.

[0180] Continue to see Figure 4 , Figure 4FIG. 4 is a schematic diagram 400 of an application scenario of the data processing method according to this embodiment. Figure 4 In the application scenario, user 401 sends an advertising demand to server 403 through terminal device 402. Server 403 first obtains the user's advertising demand; then, through the multi-level intelligent agent, it determines the executable strategy set that meets the advertising demand, wherein each level of the intelligent agent in the multi-level intelligent agent can determine the executable strategy set corresponding to the advertising demand, and the executable strategy set includes executable strategies corresponding to multiple sub-processes in the entire advertising process; finally, through the business interfaces corresponding to the multiple sub-processes, the executable strategies corresponding to the multiple sub-processes in the executable strategy set are executed.

[0181] The method provided by the above-mentioned embodiments of the present application obtains the user's advertising demands; determines an executable policy set that meets the advertising demands through a multi-level intelligent agent, wherein each level of the multi-level intelligent agent can determine the executable policy set corresponding to the advertising demands, and the executable policy set includes executable policies corresponding to each of the multiple sub-processes in the entire advertising process; executes the executable policies corresponding to each of the multiple sub-processes in the executable policy set through the business interfaces corresponding to each of the multiple sub-processes, thereby providing an information processing method applied to an advertising engine, wherein the multi-level intelligent agent in the advertising engine can automatically process the entire advertising process based on the user's advertising demands, thereby lowering the participation threshold of the advertising business and improving the user experience and convenience in the advertising processing process.

[0182] Continue to refer Figure 5 , shows a schematic process 500 of another embodiment (advertising creative delivery embodiment) of the information processing method applied to the advertising engine according to the present application, including the following steps:

[0183] Step 501: determine the target node existing in the advertisement delivery graph from the advertisement appeal.

[0184] In this embodiment, the execution subject can determine the target node existing in the advertisement delivery graph from the advertisement appeal, wherein the advertisement delivery graph represents all advertisement links in the advertisement delivery process.

[0185] Continue to refer Figure 6 , showing a schematic diagram of an advertisement delivery graph 600. The advertisement link includes a plurality of nodes, and there is a connection relationship between the nodes that have a sequential relationship.

[0186] As an example, the execution subject first determines the nodes included in the advertisement appeal, then matches each node included in the advertisement appeal with each node in the advertisement delivery graph, and determines the matched nodes as target nodes. Generally, the number of the determined target nodes is multiple.

[0187] Step 502: determine the advertisement delivery links between target nodes from the advertisement delivery graph.

[0188] In this embodiment, the execution entity may determine the advertisement delivery link between the target nodes from the advertisement delivery graph.

[0189] In this embodiment, the above-mentioned execution subject can determine the feasible connection between the target nodes as the advertising delivery link between the target nodes. Among them, the feasible connection between the target nodes means: if the target nodes are not directly connected, then the connection that can connect all the target nodes is a feasible connection; if the target nodes are directly connected, then the connection that is extended so that a closed loop can be formed between the target nodes is a feasible connection.

[0190] In some optional implementations of this embodiment, the execution subject may perform step 402 in the following manner: using a depth-first traversal algorithm to determine the advertisement delivery links between target nodes from the advertisement delivery graph.

[0191] The depth-first search (DFS) algorithm is a traversal method for graphs or trees. It starts from a node and recursively traverses the depth of the graph by exploring its adjacent nodes. Since the advertising delivery link between target nodes is specifically manifested as the connection relationship between the target nodes in time sequence, the depth-first traversal algorithm is more suitable for the determination process of the advertising delivery link.

[0192] In this implementation, a depth-first traversal algorithm is used to determine the advertisement delivery link, thereby improving the efficiency and accuracy of determining the advertisement delivery link.

[0193] In some optional implementations of this embodiment, the execution subject may perform the process of determining the advertisement delivery link in the following manner:

[0194] First, taking the starting point of the advertising delivery business as the starting node, recursively perform the following search operations on the nodes in the advertising delivery graph:

[0195] Mark the status of the current node as visited, and add the current node to the current path; in response to determining that the current node is a target node, add the current path to the advertising delivery link list; in response to determining that there are unvisited nodes among the adjacent nodes of the current node, determine the current node corresponding to the next search operation from the unvisited nodes, and perform the next search operation; in response to determining that all adjacent nodes of the current node have been visited, backtrack to the previous node of the current node, mark the access status of the current node as unvisited, and remove the current node from the current path.

[0196] As an example, the determination process includes the following steps:

[0197] 1. Select the starting point of the advertising service as the starting node and mark it as visited.

[0198] 2. At the same time, initialize an empty advertising link list to store all feasible paths found.

[0199] 3. Recursive search: Starting from the starting node, perform the following steps for each adjacent unvisited node:

[0200] 3.1. Mark the current node as visited.

[0201] 3.2. Add the current node to the current path.

[0202] 3.3. Recursively perform the above steps for unvisited adjacent nodes.

[0203] 3.4. If the current node is the target node, add the current path to the feasible path list.

[0204] 4. Backtracking: After all the adjacent nodes of the current node have been visited, backtrack to the previous node, unmark the current node, and remove the current node from the path. Continue to perform recursive search on other unvisited nodes.

[0205] Repeat steps 3 and 4 until all feasible paths are found. This set is the feasible link set.

[0206] Second, the links in the advertisement delivery link list are determined as advertisement delivery links between target nodes in the advertisement delivery graph.

[0207] For example, if a merchant enters "spend 5,000 yuan to place a certain sku", the calculated advertising links include:

[0208] 1. Use 5,000 yuan to place CPC product ads and add X crowd packages to obtain click effects from targeted groups.

[0209] 2. Use 5,000 yuan to place CPS ads, set X commission, and use a certain expert to get conversion and investment-output ratio.

[0210] 3. Using 5,000 yuan to place contact ads can get better exposure.

[0211] It should be noted that the above example is only a schematic description of the advertisement delivery link. In practice, there are advertisement delivery businesses involving hundreds or thousands of business units, and there will be many advertisement delivery links determined, and the structure will be more complex.

[0212] In this implementation, a specific implementation method of using a depth-first traversal algorithm to determine an advertisement delivery link is provided, which further improves the efficiency and accuracy of determining the advertisement delivery link.

[0213] Step 503: adopt the executable strategy corresponding to the advertisement delivery sub-process and execute the advertisement creative delivery process according to the advertisement delivery link.

[0214] In this embodiment, the above-mentioned execution subject can adopt the executable strategy corresponding to the advertisement delivery sub-process and execute the delivery process of the advertisement creative according to the advertisement delivery link.

[0215] The advertisement delivery link is used to represent the delivery process of advertisement creativity. In the advertisement delivery process that is executed step by step according to the advertisement delivery link, each link in the delivery process is executed according to the executable strategy.

[0216] The method provided by the above-mentioned embodiment of the present application provides a new advertising delivery method that can meet the user's advertising delivery needs by determining the target nodes existing in the advertising delivery graph from the advertising appeal, wherein the advertising delivery graph represents all advertising links in the advertising delivery process; determining the advertising delivery links between the target nodes from the advertising delivery graph; and using the executable strategy corresponding to the advertising delivery sub-process to execute the advertising creative delivery process according to the advertising delivery link.

[0217] In some optional implementations of this embodiment, the advertisement placement graph is created in the following manner:

[0218] First, extract the key points related to the advertising business in the advertising delivery sub-process; then, merge the same key points to obtain an advertising delivery graph. The nodes in the advertising delivery graph include node identifiers and node values ​​representing the advertising revenue of the nodes.

[0219] As an example, firstly, various existing advertising delivery processes are sorted out. Then, the following operations are performed on various advertising delivery processes: each business-related key point in the delivery process is extracted; and nodes are merged for the same key points.

[0220] After all the advertising delivery processes are disassembled and the above operations are performed, a complete advertising delivery graph knowledge base can be formed.

[0221] Each node in the advertising graph contains two pieces of information: node identification and node value. The node identification is used to represent the advertising action of this node, and the node value is the advertising revenue of this node. For example, the advertising revenue of the CPS node includes the highest investment-output ratio and high conversion rate.

[0222] In this implementation, a method for generating an advertisement placement map is provided, which improves the comprehensiveness and accuracy of the information in the advertisement placement map.

[0223] In some optional implementations of this embodiment, the advertisement delivery links between the target nodes include multiple links. In this implementation, the execution subject may also perform the following operations:

[0224] A preset compression method is used to compress each of the multiple advertisement delivery links, and the compressed multiple advertisement delivery links are displayed. The preset compression method can be specifically set according to actual conditions and is not limited here.

[0225] In this implementation, the execution subject may first construct a data structure of an advertisement delivery link, and the data structure mainly includes two parts: feasible delivery behavior and expected revenue.

[0226] Specifically, the execution subject above takes the input information (node ​​identifier and node value) of the nodes in the advertising delivery graph as the basis to form a two-dimensional data structure: {feasible delivery behavior: expected return}; and then, the feasible link is expressed by the above structure. The following is an example of a set of feasible links:

[0227] {Advertising appeal}-{Feasible delivery behavior 1: Expected return 1}-{Feasible delivery behavior 2: Expected return 2}

[0228] {Advertising appeal}-{Feasible delivery behavior 1: Expected return 1}-{Feasible delivery behavior 3: Expected return 3}

[0229] {Advertising appeal}-{Feasible delivery behavior 2: Expected return 2}-{Feasible delivery behavior 3: Expected return 3}

[0230] The preset compression method is, for example, to select only nodes directly connected to the target node to represent a feasible link, and to deduplicate repeated expected benefits, and to combine the deduplicated results together.

[0231] Continuing with the above advertising claim of "spending 5,000 yuan to place a certain sku" as an example, refer to the attached Figure 7 , showing a user interface 700 of compressed multiple advertisement delivery links. The compressed multiple advertisement delivery links are:

[0232] Feasible link 1: CPC, expected effect XXX

[0233] Feasible link 2: CPS, expected effect XXX

[0234] Viable link 3: Touchpoint advertising, expected effect XXX

[0235] The advertising delivery agent uses the above compression method to output and display the first N feasible links on the front end. Users can view the links and can choose to expand them to view the details of each feasible link.

[0236] In this implementation, multiple compressed advertising delivery links are displayed to the user, which improves the user experience and information acquisition efficiency.

[0237] In some optional implementations of this embodiment, the above-mentioned execution subject may also perform the following operation: determine an advertisement delivery link to be executed from multiple advertisement delivery links according to the received link selection operation.

[0238] Users can use voice commands, click, touch and other action commands to issue link selection operations. Figure 7 The above-mentioned execution subject can directly click the display area corresponding to the advertisement delivery link to issue a link selection operation.

[0239] In this implementation, users can flexibly select the advertising delivery link to be executed based on their own demands, which helps to further improve the user experience and meet the user's advertising demands.

[0240] In some optional implementations of this embodiment, the above execution subject may also perform the following operations:

[0241] First, the supplementary demands are determined according to the received supplementary demands operation.

[0242] Continue to refer to the above Figure 7 , users can click the "Supplementary Demand" button to enter additional demands.

[0243] Then, according to the advertisement appeal and the supplementary appeal, the updated target node is determined from the advertisement delivery graph.

[0244] As an example, the execution subject first determines the nodes included in the supplementary appeal, then matches each node included in the advertising appeal and each node included in the supplementary appeal with each node in the advertising delivery graph, and determines the matched nodes as updated target nodes. Generally, there are multiple target nodes.

[0245] Finally, the advertising delivery links between the updated target nodes are determined from the advertising delivery graph.

[0246] In this implementation, the execution subject may refer to the above process of determining the advertisement delivery link based on the target node to determine the advertisement delivery link between the updated target nodes, which will not be described in detail here.

[0247] In this implementation, the user can perform multiple appeal supplement operations based on the advertising appeal to express the appeal information as perfectly as possible, so that the final advertising delivery link is more adapted to the user's expectations.

[0248] Continue to refer Figure 8, shows a schematic process 800 of another embodiment (advertising creative review embodiment) of the information processing method applied to the advertising engine according to the present application, including the following steps:

[0249] Step 801, determining the creative material in the advertisement creative submitted by the user.

[0250] In this embodiment, the execution entity may determine the creative material in the advertisement creative submitted by the user.

[0251] Advertising creativity refers to the advertising data submitted by users, that is, advertising in the general sense. Creative materials refer to the various materials and materials used to produce, display and disseminate in advertising activities, including copywriting, pictures, audio, video and other content. Creative materials are the specific manifestations of advertising creativity. Through different forms of advertising materials, users can show products, services or brand images to the target audience and convey the information and emotions that advertisers want to express.

[0252] The advertising creativity in this embodiment is provided by the user and is not automatically generated by a multi-level intelligent agent based on the user's advertising demands.

[0253] As an example, the execution entity may analyze and process the advertisement creative submitted by the user to determine the various creative materials included therein.

[0254] Step 802: determine similar materials of the creative material from the second knowledge base through the advertisement review agent.

[0255] In this embodiment, the above-mentioned execution entity can determine similar materials of the creative material from the second knowledge base through the advertisement review intelligent agent.

[0256] The advertising review agent can automatically review advertising creativity, using neural network models such as recurrent neural network models and residual network models, and large language models such as GPT (Generative Pre-Trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers).

[0257] The second knowledge base includes various types of rich materials. As an example, the execution subject can perform material analysis on the advertising creativity of various well-known brands known so far, and obtain the second knowledge base through statistics.

[0258] In this embodiment, the execution subject can determine the similarity between each material in the second knowledge base and the creative material through the advertisement review agent, and then determine similar materials of the creative material from the second knowledge base according to the similarity. The similarity can be determined by calculating cosine similarity, Euclidean distance, etc.

[0259] In some optional implementations of this embodiment, the execution subject may perform step 802 in the following manner:

[0260] First, determine the first material characteristics of the creative material.

[0261] As an example, the execution subject may extract features of the creative material through a feature extraction network to generate a first material feature of the creative material, wherein the first material feature may be specifically represented by a feature vector.

[0262] Then, similar materials are determined according to the similarity between the first material feature and the second material feature of the material in the second knowledge base.

[0263] In this implementation, the execution subject may use the same feature extraction network to extract the second material feature of the material in the second knowledge base, and then determine the similarity between the first material feature and the second material feature of the material in the second knowledge base, and determine the material in the second knowledge base whose similarity exceeds a preset threshold as a similar material. The preset threshold can be specifically set according to actual conditions and is not limited here.

[0264] In this implementation, a specific implementation method for determining similar materials based on material characteristics is provided, which improves the accuracy of determining similar materials.

[0265] In some optional implementations of this embodiment, the second knowledge base includes an advertising knowledge base and a brand knowledge base. The advertising knowledge base refers to advertising knowledge in the advertising field, such as advertising knowledge related to the advertising industry and advertising companies. The brand knowledge base includes various types of knowledge derived from various types of advertising users (such as various types of advertising brands), such as brand trademarks, names, product names, creative images, creative videos, etc. For the two knowledge bases, the advertising knowledge base and the brand knowledge base, the advertising engine supports manual entry or import to write data to the two types of knowledge bases respectively.

[0266] In this implementation, the execution subject may perform the similar material determination process in the following manner:

[0267] First, according to the similarity between the first material feature and the second material feature of the material in the advertising knowledge base, a plurality of similar materials ranked top in similarity are determined.

[0268] In this implementation, the execution entity may sort the materials in the advertising knowledge base in descending order of similarity, and then determine the multiple materials ranked at the top in similarity as similar materials.

[0269] Then, according to the similarity between the first material feature and the second material feature of the material in the brand knowledge base, a plurality of similar materials with a top similarity ranking and a similarity exceeding a preset similarity threshold are determined.

[0270] In this implementation, the execution entity may sort the materials in the brand knowledge base in descending order of similarity, and then determine multiple materials with the highest similarity ranking and a similarity exceeding a preset similarity threshold as similar materials.

[0271] In this implementation, the second knowledge base includes an advertising knowledge base and a brand knowledge base, and similar materials are recalled for the two types of knowledge bases respectively, thereby improving the richness and accuracy of similar materials.

[0272] In some optional implementations of this embodiment, the above-mentioned execution subject may also perform the following operations: adjusting the preset similarity threshold according to the operation status of the advertisement engine.

[0273] The operation status is, for example, whether the user has subscribed to the adjustment service for the preset similarity threshold, wherein the adjustment degree of the preset similarity threshold corresponds to different fees.

[0274] It can be understood that after an advertising user pays, the preset similarity threshold corresponding to the advertising user can be increased, so that the similarity between multiple similar materials returned based on the brand knowledge base and the creative materials is greater, which is more helpful to make the generated new materials similar to the advertising creativity of the advertising user, thereby improving the advertising review of the advertising creativity submitted by the user for the advertising user.

[0275] In this implementation, the preset similarity threshold can be flexibly adjusted based on the operating conditions to adjust the advertising creative submitted by the user to improve the advertising review of the advertising user, which helps to improve the flexibility of the review process and user experience.

[0276] Step 803, through the advertisement review agent, new materials are generated based on similar materials.

[0277] In this embodiment, the above-mentioned execution entity can generate new materials based on similar materials through the advertisement review intelligent agent.

[0278] As an example, the advertising review intelligence has the AIGC (Artificial Intelligence Generated Content) function, which can generate new materials based on similar materials. Through the advertising review intelligent body, similar materials are feature extracted to obtain similar material features, and then new materials are generated based on similar material features.

[0279] In some optional implementations of this embodiment, the above-mentioned execution subject may also perform the following operations: determining the third material feature of the creative material in the historical advertising creative submitted by the user; and fusing the first material feature and the third material feature.

[0280] As an example, the above-mentioned execution entity can extract features of creative materials in historical advertising creatives submitted by users and up to the current preset time period through the same feature extraction network to obtain the third material features of the creative materials in the historical advertising creatives.

[0281] In this implementation, the execution subject may perform step 803 in the following manner: generating a new material according to the second material feature and the fusion feature corresponding to the similar material.

[0282] As an example, through the advertisement review agent, features of similar materials are extracted to obtain similar material features, and then new materials are generated based on similar material features and fusion features.

[0283] In this implementation, historical advertising ideas submitted by users are taken into consideration during the process of producing new materials, and new materials are generated for advertising review by combining historical advertising ideas submitted by users with currently submitted advertising ideas, which helps to further improve the accuracy of the review results.

[0284] Step 804: Determine the advertisement review result according to the similarity between the new material and the advertisement creativity of the advertisement users in the advertisement user set.

[0285] In this embodiment, the execution entity may determine the advertisement review result according to the similarity between the new material and the advertisement creative ideas of the advertisement users in the advertisement user set.

[0286] The advertisement user set includes all advertisement users up to now, and records the advertisement creative data of each advertisement user.

[0287] In this embodiment, the above-mentioned execution subject can determine the similarity between the new material and the advertising creativity of each advertising user in the advertising user set; then, determine the advertising review result based on the similarity. For example, when the similarity between the new material and the advertising creativity of a certain advertising user exceeds a preset threshold, it is determined that the advertising creativity submitted by the user has the behavior of "freeloading" the advertising user. Among them, the similarity can be determined by calculating cosine similarity, Euclidean distance, etc.

[0288] In this embodiment, creative materials in advertising ideas submitted by users are determined; similar materials of the creative materials are determined from a second knowledge base through an advertising review intelligent agent; new materials are generated based on the similar materials through the advertising review intelligent agent; and the advertising review results are determined based on the similarity between the new materials and the advertising ideas of advertising users in the advertising user set, thereby providing a new advertising review method and improving the accuracy of advertising review.

[0289] Continue to refer Fig. 9 As an implementation of the methods shown in the above figures, the present application provides an embodiment of an information processing device applied to an advertising engine. The device embodiment corresponds to the method embodiments shown in Figures 200, 500, and 800, and the device can be specifically applied to various electronic devices.

[0290] like Fig. 9 As shown, the information processing device 900 applied to the advertising engine includes: an acquisition unit 901, configured to acquire the user's advertising demands; a determination unit 902, configured to determine an executable policy set that meets the advertising demands through multi-level intelligent agents, wherein each level of the multi-level intelligent agents can determine the executable policy set corresponding to the advertising demands, and the executable policy set includes executable policies corresponding to each of the multiple sub-processes in the entire advertising process; an execution unit 903, configured to execute the executable policies corresponding to each of the multiple sub-processes in the executable policy set through the business interfaces corresponding to each of the multiple sub-processes.

[0291] In some optional implementations of the present embodiment, the agents at each level in the multi-level agents include multiple agents, and the above-mentioned determination unit 902 is further configured to: process multiple sub-appeals in the advertising appeal in the order of execution of the agents at each level in the multi-level agents, and in response to the presence of a target sub-appeal among the multiple sub-appeals for which the agents at the current level have not determined an executable strategy based on the preset voting strategy, process the target sub-appeal by the agents at subsequent levels until the executable strategies corresponding to the multiple sub-appeals are combined to obtain an executable strategy set.

[0292] In some optional implementations of the present embodiment, the first-level agents in the multi-level agent include agent groups corresponding to multiple sub-processes, each agent group includes multiple agents, and the above-mentioned determination unit 902 is further configured to: decompose user demands and determine the sub-demands corresponding to each of the multiple sub-processes; process the sub-demands corresponding to each of the multiple sub-processes through the agent groups corresponding to each of the multiple sub-processes to obtain multiple candidate executable strategies corresponding to each of the multiple sub-processes; for the multiple candidate executable strategies output by each of the multiple agent groups, determine whether an executable strategy can be determined from the multiple candidate executable strategies based on a preset voting strategy; in response to the fact that for each of the multiple agent groups, the executable strategy corresponding to the agent group can be determined, and the executable strategy set is obtained by combining the executable strategies corresponding to each of the multiple agent groups.

[0293] In some optional implementations of the present embodiment, the above-mentioned determination unit 902 is further configured to: in response to determining that there is a target intelligent agent group among multiple intelligent agent groups for which an executable strategy has not been determined, process the target sub-demand through multiple intelligent agents at subsequent levels until an executable strategy is determined from the candidate executable strategies output by each of the multiple intelligent agents at the current level based on a preset voting strategy, wherein the multiple intelligent agents at subsequent levels are determined according to an execution order; and obtain an executable strategy set by combining the executable strategies determined by other intelligent agent groups except the target intelligent agent group among the multiple intelligent agent groups and the executable strategies determined by the multiple intelligent agents at the current level.

[0294] In some optional implementations of the present embodiment, the above-mentioned determination unit 902 is further configured to: parse the sub-demands corresponding to the multiple sub-processes through the intelligent agent groups corresponding to the multiple sub-processes, and obtain the advertising terms corresponding to the multiple sub-demands; for each of the multiple intelligent agent groups, determine the candidate executable strategy corresponding to the sub-process corresponding to the intelligent agent group from the preset limited result set according to the advertising terms corresponding to the sub-demand to be processed by the intelligent agent group.

[0295] In some optional implementations of the present embodiment, the above-mentioned multi-level intelligent agent is obtained in the following manner: determining the initial model corresponding to each of the multi-level intelligent agents based on the accuracy and parameter scale of the models in the model set, wherein the accuracy and parameter scale of the initial model corresponding to the level to which the intelligent agent belongs are positively correlated; determining the first knowledge base corresponding to each of the multi-level intelligent agents, wherein the first knowledge base corresponding to the intelligent agents of different levels includes advertising knowledge of different preset dimensions; training the initial model corresponding to each of the multi-level intelligent agents through the first knowledge base corresponding to each of the multi-level intelligent agents to obtain the multi-level intelligent agent.

[0296] In some optional implementations of this embodiment, multiple sub-processes include an advertising creative sub-process, an advertising delivery sub-process, an advertising operation sub-process and an advertising playback sub-process, and the above-mentioned execution unit 903 is further configured to: generate advertising creativity through the business interface corresponding to the advertising creative sub-process and according to the executable policy corresponding to the advertising creative sub-process in the executable policy set; execute the delivery process of the advertising creativity through the business interface corresponding to the advertising delivery sub-process and according to the executable policy corresponding to the advertising delivery sub-process in the executable policy set; execute the algorithm involved in the advertising process through the business interface corresponding to the advertising operation sub-process and according to the executable policy corresponding to the advertising operation sub-process in the executable policy set; execute the playback process of the advertising creativity through the business interface corresponding to the advertising playback sub-process and according to the executable policy corresponding to the advertising playback sub-process in the executable policy set.

[0297] In some optional implementations of this embodiment, the above-mentioned execution unit 903 is further configured to: determine the target nodes existing in the advertising delivery graph from the advertising appeal, wherein the advertising delivery graph represents all advertising links in the advertising delivery process; determine the advertising delivery links between the target nodes from the advertising delivery graph; adopt the executable strategy corresponding to the advertising delivery sub-process, and execute the advertising creative delivery process according to the advertising delivery link.

[0298] In some optional implementations of this embodiment, the execution unit 903 is further configured to: use a depth-first traversal algorithm to determine the advertisement delivery links between target nodes from the advertisement delivery graph.

[0299] In some optional implementations of the present embodiment, the above-mentioned advertising delivery graph is created in the following manner: extracting key points related to the delivery business in the advertising delivery sub-process; merging the same key points to obtain an advertising delivery graph, wherein the nodes in the advertising delivery graph include node identifiers and node values ​​representing the advertising revenue of the nodes.

[0300] In some optional implementations of this embodiment, the above-mentioned advertising delivery links include multiple links, and the above-mentioned device also includes: a compression display unit (not shown in the figure), which is configured to use a preset compression method to compress each of the multiple advertising delivery links and display the compressed multiple advertising delivery links.

[0301] In some optional implementations of this embodiment, the above-mentioned device also includes: a selection unit (not shown in the figure), which is configured to determine an advertisement delivery link to be executed from multiple advertisement delivery links according to the received link selection operation.

[0302] In some optional implementations of this embodiment, the above-mentioned device also includes: a supplementary unit (not shown in the figure), which is configured to determine the supplementary appeal based on the received supplementary appeal operation; determine the updated target node from the advertising delivery graph based on the advertising appeal and the supplementary appeal; and determine the advertising delivery link between the updated target nodes from the advertising delivery graph.

[0303] In some optional implementations of this embodiment, the above-mentioned device also includes: a review unit (not shown in the figure), which is configured to determine the creative materials in the advertising creativity submitted by the user; determine similar materials of the creative materials from the second knowledge base through the advertising review intelligent agent; generate new materials based on the similar materials through the advertising review intelligent agent; determine the advertising review result based on the similarity between the new materials and the advertising creativity of the advertising users in the advertising user set.

[0304] In some optional implementations of this embodiment, the above-mentioned review unit (not shown in the figure) is further configured to: determine the first material feature of the creative material; and determine similar materials based on the similarity between the first material feature and the second material feature of the material in the second knowledge base.

[0305] In some optional implementations of the present embodiment, the second knowledge base includes an advertising knowledge base and a brand knowledge base, and the review unit (not shown in the figure) is further configured to: determine a plurality of similar materials ranked at the top in similarity based on the similarity between the first material feature and the second material feature of the material in the advertising knowledge base; determine a plurality of similar materials ranked at the top in similarity and whose similarity exceeds a preset similarity threshold based on the similarity between the first material feature and the second material feature of the material in the brand knowledge base.

[0306] In some optional implementations of the present embodiment, the above-mentioned device also includes: a fusion unit (not shown in the figure), configured to determine the third material feature of the creative material in the historical advertising creativity submitted by the user; fuse the first material feature and the third material feature to obtain a fusion feature; and the above-mentioned review unit is further configured to: generate new materials based on the second material feature and the fusion feature corresponding to similar materials.

[0307] In some optional implementations of this embodiment, the above-mentioned device also includes: an adjustment unit (not shown in the figure), which is configured to adjust the preset similarity threshold according to the operation status of the advertising engine.

[0308] In some optional implementations of this embodiment, the acquisition unit 901 is further configured to: acquire the user's advertising demands represented by natural language.

[0309] In some examples, the above-mentioned device also includes: an agent determination unit (not shown in the figure), which is configured to determine the agent of each level in the multi-level agent according to the received selection operation.

[0310] In the present embodiment, an acquisition unit in an information processing device applied to an advertising engine acquires a user's advertising demands; a determination unit determines an executable policy set that satisfies the advertising demands through a multi-level intelligent agent, wherein each level of the multi-level intelligent agent can determine an executable policy set corresponding to the advertising demands, and the executable policy set includes executable policies corresponding to each of multiple sub-processes in the entire advertising process; an execution unit executes executable policies corresponding to each of the multiple sub-processes in the executable policy set through business interfaces corresponding to each of the multiple sub-processes, thereby providing an information processing device applied to an advertising engine, wherein the multi-level intelligent agent in the advertising engine can automatically process the entire advertising process based on the user's advertising demands, thereby lowering the threshold for participation in the advertising business and improving the user experience and convenience in the advertising processing process.

[0311] Reference below Fig.10 , which shows a device suitable for implementing the embodiments of the present application (eg Figure 1 Schematic diagram of the structure of a computer system 1000 having devices 101, 102, 103, 105) shown. Fig.10 The device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0312] like Fig.10 As shown, the computer system 1000 includes a processor (e.g., CPU, central processing unit) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage part 1008 into a random access memory (RAM) 1003. Various programs and data required for the operation of the system 1000 are also stored in the RAM 1003. The processor 1001, ROM 1002, and RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0313] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage section 1008 as needed.

[0314] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 1009, and / or installed from a removable medium 1011. When the computer program is executed by the processor 1001, the above-mentioned functions defined in the method of the present application are executed.

[0315] It should be noted that the computer-readable medium of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0316] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the client computer, partially on the client computer, as a separate software package, partially on the client computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the client computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0317] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the device, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0318] The units involved in the embodiments described in the present application may be implemented by software or by hardware. The described units may also be arranged in a processor, for example, may be described as: a processor comprising an acquisition unit, a determination unit and an execution unit. Among them, the names of these units do not constitute a limitation on the unit itself under certain circumstances. For example, the determination unit may also be described as "determining an executable strategy set that meets the advertising demands through a multi-level intelligent agent, wherein each level of the intelligent agent in the multi-level intelligent agent can determine the executable strategy set corresponding to the advertising demands, and the executable strategy set includes units of executable strategies corresponding to each of the multiple sub-processes in the entire advertising process".

[0319] As another aspect, the present application also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the device, the computer device: obtains the user's advertising demands; determines an executable strategy set that satisfies the advertising demands through a multi-level intelligent agent, wherein each level of the multi-level intelligent agent can determine the executable strategy set corresponding to the advertising demands, and the executable strategy set includes executable strategies corresponding to each of the multiple sub-processes in the entire advertising process; and executes the executable strategies corresponding to each of the multiple sub-processes in the executable strategy set through the business interfaces corresponding to each of the multiple sub-processes.

[0320] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. An information processing method applied to an advertising engine, comprising: Obtain users’ advertising demands; Determine an executable strategy set that meets the advertising demand through multi-level intelligent agents, wherein each level of intelligent agents in the multi-level intelligent agents can determine an executable strategy set corresponding to the advertising demand, and the executable strategy set includes executable strategies corresponding to each of multiple sub-processes in the entire advertising process; The executable policies corresponding to the multiple sub-processes in the executable policy set are executed through the business interfaces corresponding to the multiple sub-processes.

2. The method according to claim 1, wherein: Each level of the multi-level agent includes a plurality of agents, and The step of determining an executable strategy set that satisfies the advertising demands through a multi-level intelligent agent includes: According to the execution order of the agents at each level in the multi-level agents, multiple sub-demands in the advertising appeal are processed, and in response to the presence of a target sub-appeal among the multiple sub-appeals for which the agents at the current level have not determined an executable strategy based on the preset voting strategy, the target sub-appeal is processed by the agents at the subsequent levels until the executable strategies corresponding to the multiple sub-appeals are combined to obtain the executable strategy set.

3. The method according to claim 2, wherein: The first level of agents in the multi-level agent includes an agent group corresponding to each of the plurality of sub-processes, wherein the agent group includes a plurality of agents, and The method of processing the multiple sub-demands in the advertisement demand in the order of execution of the agents at each level in the multi-level agents, and in response to the existence of a target sub-demand among the multiple sub-demands for which the agents at the current level have not determined an executable strategy based on a preset voting strategy, processing the target sub-demand by agents at subsequent levels until the executable strategies corresponding to the multiple sub-demands are combined to obtain the executable strategy set includes: Decomposing the user demands and determining the sub-demands corresponding to the multiple sub-processes; Processing the sub-demands corresponding to the multiple sub-processes respectively through the agent groups corresponding to the multiple sub-processes respectively, and obtaining the multiple candidate executable strategies corresponding to the multiple sub-processes respectively; For multiple candidate executable strategies output by each of the multiple agent groups, determining whether an executable strategy can be determined from the multiple candidate executable strategies based on the preset voting strategy; In response to the fact that for each of the multiple agent groups, an executable strategy corresponding to the agent group can be determined, the executable strategy set is obtained by combining the executable strategies corresponding to each of the multiple agent groups.

4. The method according to claim 2 or 3, wherein: The method further comprises: processing the multiple sub-demands in the advertisement demand in the order of execution of the agents at each level in the multi-level agents; in response to the existence of a target sub-demand among the multiple sub-demands for which the agents at the current level have not determined an executable strategy based on a preset voting strategy, processing the target sub-demand by agents at subsequent levels until the executable strategies corresponding to the multiple sub-demands are combined to obtain the executable strategy set; In response to determining that there is a target agent group among the multiple agent groups for which an executable strategy has not been determined, processing the target sub-demand through multiple agents at subsequent levels until an executable strategy is determined from candidate executable strategies output by each of the multiple agents at the current level based on the preset voting strategy; The executable strategy set is obtained by combining the executable strategies determined by other agent groups among the multiple agent groups except the target agent group and the executable strategies determined by multiple agents at the current level.

5. The method according to claim 3, wherein: The step of processing the sub-demands corresponding to the multiple sub-processes by the agent groups corresponding to the multiple sub-processes to obtain the multiple candidate executable strategies corresponding to the multiple sub-processes includes: By using the agent groups corresponding to the multiple sub-processes, the sub-demands corresponding to the multiple sub-processes are analyzed to obtain the advertising terms corresponding to the multiple sub-demands; For each of the multiple agent groups, a candidate executable strategy corresponding to the sub-process of the agent group is determined from a preset limited result set according to the advertising terms corresponding to the sub-appeal to be processed by the agent group.

6. The method according to any one of claims 1 to 5, wherein: The multi-level agent is obtained in the following way: Determine the initial models corresponding to the multi-level agents according to the accuracy and parameter scale of the models in the model set, wherein the accuracy and parameter scale of the initial models corresponding to the level to which the agent belongs are positively correlated; Determining first knowledge bases corresponding to the multiple levels of intelligent agents, wherein the first knowledge bases corresponding to the intelligent agents at different levels include advertising knowledge of different preset dimensions; The initial models corresponding to the multi-level intelligent agents are trained by using the first knowledge bases corresponding to the multi-level intelligent agents to obtain the multi-level intelligent agents.

7. The method according to claim 1, wherein: The multiple sub-processes include an advertising creative sub-process, an advertising delivery sub-process, an advertising operation sub-process and an advertising playback sub-process, and The executing, through the business interfaces corresponding to the multiple sub-processes, the executable policies corresponding to the multiple sub-processes in the executable policy set respectively, comprises: Generate an advertisement creative through the business interface corresponding to the advertisement creative sub-process and according to the executable strategy corresponding to the advertisement creative sub-process in the executable strategy set; Through the business interface corresponding to the advertisement delivery sub-process, and according to the executable strategy corresponding to the advertisement delivery sub-process in the executable strategy set, the delivery process of the advertisement creative is executed; Execute the operation process of the advertising creative through the business interface corresponding to the advertising operation sub-process and according to the executable strategy corresponding to the advertising operation sub-process in the executable strategy set; The advertisement creative play process is executed through the business interface corresponding to the advertisement play sub-process and according to the executable strategy corresponding to the advertisement play sub-process in the executable strategy set.

8. The method according to claim 7, wherein: The process of executing the advertisement creative delivery process according to the executable strategy corresponding to the advertisement delivery sub-process in the executable strategy set includes: Determine a target node existing in an advertisement delivery graph from the advertisement appeal, wherein the advertisement delivery graph represents all advertisement links in an advertisement delivery process; Determining the advertisement delivery links between the target nodes from the advertisement delivery graph; The executable strategy corresponding to the advertisement delivery sub-process is adopted, and the delivery process of the advertisement creative is executed according to the advertisement delivery link.

9. The method according to claim 8, wherein: The step of determining the advertisement delivery link between the target nodes from the advertisement delivery graph includes: A depth-first traversal algorithm is used to determine the advertisement delivery links between the target nodes from the advertisement delivery graph.

10. The method according to claim 8, wherein: The advertising placement graph is created in the following way: Extracting key points related to the advertising delivery business in the advertising delivery sub-process; The same key points are merged to obtain the advertisement placement graph, wherein the nodes in the advertisement placement graph include node identifiers and node values ​​representing the advertisement revenue of the nodes.

11. The method according to claim 8, wherein: The advertisement delivery link includes multiple links, and The method further comprises: A preset compression method is adopted to compress each of the multiple advertisement delivery links, and the compressed multiple advertisement delivery links are displayed.

12. The method according to claim 11, wherein: Also includes: According to the received link selection operation, an advertisement delivery link to be executed is determined from the multiple advertisement delivery links.

13. The method according to any one of claims 8 to 12, wherein: Also includes: Determine the supplementary demands according to the received supplementary demands operation; According to the advertisement appeal and the supplementary appeal, determining an updated target node from the advertisement delivery graph; The advertisement delivery links between the updated target nodes are determined from the advertisement delivery graph.

14. The method according to claim 1, wherein: Also includes: Determining the creative material in the advertising creative submitted by the user; Determine similar materials of the creative material from a second knowledge base through an advertisement review agent; Generate new materials based on the similar materials through the advertisement review agent; The advertisement review result is determined according to the similarity between the new material and the advertisement creativity of the advertisement users in the advertisement user set.

15. The method according to claim 14, wherein: Determining similar materials of the creative material from the second knowledge base includes: Determining a first material characteristic of the creative material; The similar material is determined according to the similarity between the first material feature and the second material feature of the material in the second knowledge base.

16. The method according to claim 15, wherein: The second knowledge base includes an advertising knowledge base and a brand knowledge base, and The determining the similar material according to the similarity between the first material feature and the second material feature of the material in the second knowledge base includes: According to the similarity between the first material feature and the second material feature of the material in the advertising knowledge base, a plurality of similar materials ranked top in similarity are determined; According to the similarity between the first material feature and the second material feature of the material in the brand knowledge base, a plurality of similar materials with a top similarity ranking and a similarity exceeding a preset similarity threshold are determined.

17. The method according to claim 15 or 16, wherein: Also includes: Determining a third material feature of a creative material in the historical advertising creative submitted by the user; fusing the first material feature and the third material feature to obtain a fused feature; as well as The generating new material according to the similar material comprises: A new material is generated according to the second material feature corresponding to the similar material and the fusion feature.

18. The method according to claim 16, wherein: Also includes: The preset similarity threshold is adjusted according to the operation status of the advertisement engine.

19. The method according to claim 1, wherein: The obtaining of the user's advertising demands includes: Acquire the advertisement demands of the user represented by natural language.

20. The method according to claim 1, wherein: Also includes: According to the received selection operation, the agent of each level in the multi-level agent is determined.

21. An information processing device applied to an advertisement engine, comprising: An acquisition unit, configured to acquire the user's advertising demands; A determination unit is configured to determine an executable strategy set that satisfies the advertising demand through a multi-level intelligent agent, wherein each level of the intelligent agent in the multi-level intelligent agent can determine an executable strategy set corresponding to the advertising demand, and the executable strategy set includes executable strategies corresponding to each of a plurality of sub-processes in the entire advertising process; The execution unit is configured to execute the executable policies corresponding to each of the multiple sub-processes in the executable policy set through the business interfaces corresponding to each of the multiple sub-processes.

22. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 20 is implemented.

23. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 20.

24. A computer program product comprising: A computer program which, when executed by a processor, implements the method according to any one of claims 1 to 20.

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