Interaction component layout configuration method and device, electronic equipment and storage medium

Abstract

Embodiments of the invention provide an interaction component layout configuration method and apparatus, an electronic device and a storage medium. The method comprises the steps of receiving a starting request for a target agent; in response to the starting request, obtaining a target entity model corresponding to the target agent, the target entity model including point location rule information for a version contrast experiment task, and the point location rule information being used for representing a point location layout of a skill item of the target agent in the version contrast experiment task; and according to the target entity model corresponding to the target agent, generating component configuration information corresponding to the target agent, the component configuration information being used for enabling the target client to display skill items of the target agent based on the point location layout corresponding to the point location rule information. The rapid configuration and flexible modification of the interaction component layout are realized, and the execution efficiency and the experiment effect of the execution version contrast experiment task are improved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, device, electronic device and storage medium for configuring the layout of interactive components. Background Art

[0002] Currently, AI Agent-based conversational interaction interfaces have gradually become a new entry point for human-computer interaction. Users can issue instructions or requests to AI agents based on natural language to call upon the AI ​​agents' various skills to complete the corresponding tasks. At the same time, in order to better complete the corresponding tasks, the platform will provide AI agents in various subdivided fields for users to choose from.

[0003] For intelligent entities in different segments, the platform usually configures the dialogue interaction interface based on a fixed interaction component layout, but different interaction component layouts will affect the user's convenience of use, and then affect user retention. Therefore, in the prior art, the interaction component layout in the dialogue interaction interface is optimized by executing version control experimental tasks.

[0004] However, in the solutions of the prior art, in the process of executing the version control experiment task, there are problems such as low efficiency of interactive component layout configuration and high modification cost. Summary of the invention

[0005] The embodiments of the present disclosure provide a method, device, electronic device and storage medium for configuring the layout of an interactive component, so as to overcome the problems of low efficiency and high modification cost of configuring the layout of an interactive component.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for configuring an interactive component layout, including:

[0007] Receive a start request for a target intelligent agent; in response to the start request, obtain a target entity model corresponding to the target intelligent agent, wherein the target entity model includes point rule information for a version control experiment task, and the point rule information is used to characterize the point layout of the skill items of the target intelligent agent in the version control experiment task; generate component configuration information corresponding to the target intelligent agent according to the target entity model corresponding to the target intelligent agent, and the component configuration information is used to enable the target client to display the skill items of the target intelligent agent based on the point layout corresponding to the point rule information.

[0008] In a second aspect, an embodiment of the present disclosure provides a device for configuring a layout of interactive components, including:

[0009] A receiving module, used for receiving a start request for a target intelligent agent;

[0010] A processing module, configured to obtain, in response to the start request, a target entity model corresponding to the target intelligent agent, wherein the target entity model includes point rule information for the version control experiment task, and the point rule information is used to characterize the point layout of the skill items of the target intelligent agent in the version control experiment task;

[0011] A generation module is used to generate component configuration information corresponding to the target intelligent agent based on the target entity model corresponding to the target intelligent agent, and the component configuration information is used to enable the target client to display the skill items of the target intelligent agent based on the point layout corresponding to the point rule information.

[0012] In a third aspect, an embodiment of the present disclosure provides an electronic device, including: a processor and a memory;

[0013] The memory stores computer-executable instructions;

[0014] The processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the interactive component layout configuration method described in the first aspect and various possible designs of the first aspect.

[0015] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, in which computer execution instructions are stored. When a processor executes the computer execution instructions, the interactive component layout configuration method described in the first aspect and various possible designs of the first aspect is implemented.

[0016] In a fifth aspect, an embodiment of the present disclosure provides a computer program product, including a computer program, which, when executed by a processor, implements the interactive component layout configuration method as described in the first aspect and various possible designs of the first aspect.

[0017] The interactive component layout configuration method, device, electronic device and storage medium provided in this embodiment receive a start request for a target intelligent agent; in response to the start request, obtain a target entity model corresponding to the target intelligent agent, wherein the target entity model contains point rule information for a version control experiment task, and the point rule information is used to characterize the point layout of the skill items of the target intelligent agent in the version control experiment task; according to the target entity model corresponding to the target intelligent agent, generate component configuration information corresponding to the target intelligent agent, and the component configuration information is used to enable the target client to display the skill items of the target intelligent agent based on the point layout corresponding to the point rule information. After receiving the start request, the target entity model corresponding to the target intelligent agent indicated by the start request is obtained, and the point layout of the skill items of the target intelligent agent is determined by using the point rule information in the target entity model for performing the version control experiment task, and then the corresponding component configuration information is generated and sent to the target client, so that the client can display the skill items of the target intelligent agent in a targeted manner based on the point rule information. Rapid configuration and flexible modification of the interactive component layout can be achieved, and the execution efficiency and experimental effect of the version control experiment task can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 An application scenario diagram of the interactive component layout configuration method provided in an embodiment of the present disclosure;

[0020] Figure 2 A schematic diagram of a method for configuring an interactive component layout according to an embodiment of the present disclosure Figure 1 ;

[0021] Figure 3 A schematic diagram of an interactive operation project provided by an embodiment of the present disclosure;

[0022] Figure 4 Schematic diagram of the interactive component layout configuration method provided in the embodiment of the present disclosure Figure 2 ;

[0023] Figure 5 for Figure 4 A flowchart of a specific implementation method of step S203 in the illustrated embodiment;

[0024] Figure 6 for Figure 4 A flowchart of another specific implementation of step S203 in the illustrated embodiment;

[0025] Figure 7 for Figure 4 A flowchart of another specific implementation of step S203 in the illustrated embodiment;

[0026] Figure 8 for Figure 4 A flowchart of another specific implementation of step S203 in the illustrated embodiment;

[0027] Fig. 9 for Figure 4 A flowchart of a specific implementation method of step S204 in the illustrated embodiment;

[0028] Fig.10 for Figure 4 A flowchart of a specific implementation method of step S205 in the illustrated embodiment;

[0029] Fig.11 A structural block diagram of an interactive component layout configuration device provided in an embodiment of the present disclosure;

[0030] Fig.12 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure;

[0031] Fig.13 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0033] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0034] The application scenarios of the embodiments of the present disclosure are explained below:

[0035] The interactive component layout configuration method provided by the embodiment of the present disclosure can be applied to an application (APP, Application) with intelligent body functions or the server corresponding to the application. More specifically, it can be applied to the application scenario of conducting version control experiments (i.e., AB experiments) for interactive components in the dialogue interaction interface of the intelligent body. The execution subject of this embodiment can be a terminal device running the above-mentioned application, or a server deploying the server corresponding to the above-mentioned application, or other electronic devices with similar functions. The server of the above-mentioned application with intelligent body functions can be partially or completely run on the server, and the method provided by this embodiment is executed on the server side, while the terminal device runs the client of the application. The communication between the server and the terminal device is based on the server-client, for example, so that the terminal device can obtain the execution result of the method provided in this embodiment and display it as needed.

[0036] Among them, in some embodiments, the above-mentioned terminal device or server can implement the interactive component layout configuration method provided by the embodiment of the present disclosure by running various computer executable instructions or computer programs. For example, computer executable instructions can be program-level commands, machine instructions or software instructions. The computer program can be a native program or software module in the operating system; it can be a local application, that is, a program that needs to be installed in the operating system to run, or it can be a small program embedded in any APP, that is, a program that runs based on a browser environment. In summary, the above-mentioned computer executable instructions can be instructions in any form, and the above-mentioned computer program can be an application, module or plug-in in any form, and the specific implementation form can be configured as needed. Furthermore, in the process of implementing the interactive component layout configuration method provided by the embodiment of the present disclosure, the server can execute the method by running a computer executable instruction or computer program set locally, or it can execute the method by calling a computer executable instruction or computer program set in an external server. In some embodiments, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud storage, cloud communications, cloud databases, cloud computing, cloud functions, network services, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), as well as big data and artificial intelligence platforms. Among them, cloud services can be interactive processing services for terminal devices to call.

[0037] Figure 1 An application scenario diagram of the interactive component layout configuration method provided in the embodiment of the present disclosure, referring to Figure 1As shown in the figure, a target application with an agent function is running in the terminal device, and a plurality of preset agent programs are provided in the target application, such as the agent Agent_1, agent Agent_2, agent Agent_3, etc. shown in the figure. After the user starts a corresponding target agent (such as agent Agent_1) by operating the target application, the terminal device sends a start request to the server, and then the server executes the interactive component layout configuration method provided in this embodiment, generates component configuration information for the target agent, and sends it back to the client on the terminal device side. The client initializes based on the component configuration information, including determining the layout of each skill item of the target agent and displaying it in the dialogue interaction interface, and displaying relevant prompt information in the dialogue interaction interface. For example, the prompt information shown in the figure is: "Hey, hello, I am your new friend Agent_1, I can answer your questions at any time and help you handle various tasks. Ask me questions now!". Afterwards, the user can further click on the skill items of the target agent in the dialogue interaction interface and enter the corresponding demand text to realize the corresponding function. For example, as shown in the figure, the skill items of the target agent include: "article writing", "dialogue reply", "image generation", etc. Further, for example, after the user selects "Article Writing" and enters the writing requirement "Write a space science fiction short story" in the dialog box, and selects the parameters of the skill item "Article Writing" (such as "Tone: Formal", "Length: Medium"), the target agent generates the corresponding text by calling the corresponding model. In another possible implementation, the above process can also be completed entirely by the terminal device, that is, the terminal device simultaneously provides the functions provided by the terminal device and the server in the above example, and realizes the process of the target agent calling the model and generating the corresponding text, which will not be repeated here.

[0038] For agents in different segments, the platform usually configures the dialogue interaction interface based on a fixed interactive component layout, but the display area of ​​the dialogue interaction interface is limited. Different interactive component layouts will affect the user's convenience of use, and then affect user retention. Therefore, in the prior art, the interactive component layout in the dialogue interaction interface is usually optimized by executing version control experiments (i.e., A / B experiments). In the related art, for version control experiments, the experimental configuration of the version control experiment is usually maintained in an array structure, and the array structure is used to control whether the skill items (interactive components) are issued and the order of issuance (i.e., determine the component layout). However, this solution requires the configuration of corresponding experimental configuration parameters for each agent, and the experimental configuration parameters between different agents cannot be reused or decoupled. When the experimental configuration parameters need to be modified or adjusted, each agent needs to be manually modified in turn, resulting in low efficiency in interactive component layout configuration and high modification costs.

[0039] The embodiment of the present disclosure provides a method for configuring the layout of interactive components to solve the above-mentioned problem.

[0040] refer to Figure 2 , Figure 2 A schematic diagram of a method for configuring an interactive component layout according to an embodiment of the present disclosure Figure 1 The method of this embodiment can be applied in a server, and the interactive component layout configuration method includes:

[0041] Step S101: Receive a start request for a target agent.

[0042] Step S102: In response to the start request, a target entity model corresponding to the target agent is obtained, wherein the target entity model contains point rule information for the version control experiment task, and the point rule information is used to characterize the point layout of the agent's skill items in the version control experiment task.

[0043] Step S103: Generate component configuration information corresponding to the target agent according to the target entity model corresponding to the target agent, and the component configuration information is used to enable the target client to display the skill items of the target agent based on the point layout corresponding to the point rule information.

[0044] For example, reference Figure 1 The application scenario schematic diagram shown takes the case where the server is the execution subject of the method provided in this embodiment as an example, receives a start request for the target intelligent agent, that is, the server receives the start request sent by the target client on the terminal device side, and then, based on the target intelligent agent indicated by the start request, the server obtains the corresponding target entity model. Among them, the target entity model can be a data structure for recording relevant data and information of the intelligent agent. In a possible implementation, the start request includes a first identifier representing the target intelligent agent, and the first identifier is, for example, the intelligent agent unique identification code of the target intelligent agent. After that, the name or position of the target intelligent agent is determined based on the first identifier, and then the target entity model of the target intelligent agent is obtained. In another possible implementation, the start request includes a first identifier representing the target intelligent agent, and the target entity model includes an intelligent agent identification field representing the corresponding intelligent agent. According to the first identifier in the start request, the pre-configured target entity model in the database is retrieved to obtain the target entity model with the intelligent agent identification field as the first identifier, and the target entity model is the target entity model of the target intelligent agent, wherein the pre-configured target entity model can be designed and generated in advance based on the experimental requirements and conditions, and the content and generation process of the experimental model are not specifically restricted this time.

[0045] Furthermore, the target entity model includes point rule information for the version control experiment task, and the point rule information is used to characterize the point layout of the intelligent agent's skill items in the version control experiment task. Exemplarily, the point rule information includes a global layout rule, and the global layout rule refers to all display points of the skill items corresponding to the target intelligent agent, that is, the point layout of the entire skill bar. In a possible implementation method, the global layout rule is represented by a point sequence. For example, [a, b, c] is a global layout rule, which identifies the arrangement order of skill item "a", skill item "b", and skill item "c". Accordingly, after generating component configuration information based on the global layout rule (point rule information) and sending it to the target client, the target client displays the target intelligent agent's skill items based on the point layout corresponding to the point rule information. Specifically, combined with the above example, on the client side, the target intelligent agent's dialogue interaction interface will arrange the above skill items in the order of skill item "a", skill item "b", and skill item "c". More specifically, for example, the interactive components of "article writing" (skill item "a"), "dialogue response" (skill item "b"), and "image generation" (skill item "c") are displayed in sequence in the dialogue interaction interface of the target intelligent agent, thereby realizing the execution of the specified version control experimental task.

[0046] In another possible implementation, the point rule information includes a single-point layout rule, which is used to indicate N target display points and the skill items of the target agent corresponding to each target display point, wherein the target client has M display points for displaying the skill items of the target agent, and N is an integer greater than 0 and less than M. For example, M is 3, that is, the target client has a total of 3 display points for displaying the target agent; and N is 1, that is, a separate layout rule is used to control the skill items displayed at one of the display points. More specifically, for example, a single-point layout rule is [skill id = a, point addr = 1], which indicates that skill item "a" is located first in the point arrangement. Correspondingly, after the component configuration information is generated based on the single-point layout rule (point rule information) and sent to the target client, on the client side, in the dialogue interaction interface of the target intelligent agent, skill item "a" will be ranked first, that is, skill item "a" will be ranked before other skill items, and the order of other skill items is not restricted by the single-point layout rule, such as randomly determined or determined based on preset rules. More specifically, for example, at the first display point in the dialogue interaction interface of the target intelligent agent, the interactive component of the skill item "article writing" (skill item "a") is displayed. At the second display point and the third display point, the interactive components of "image generation" (skill item "c") and "dialogue reply" (skill item "b") are displayed respectively, thereby realizing the execution of the specified version control experimental task.

[0047] Further, it can be understood that the target display point indicated by the single-point layout rule, in addition to indicating one display point (i.e., the case of [skill id = a, point addr = 1] in the above example), can also indicate more than two display points, for example, [skill id = a, point addr = 1; skill id = b, point addr = 3;], the single-point layout rule indicates that the skill item "a" is located in the first position in the point arrangement, and the skill item "b" is located in the third position in the point arrangement. In this embodiment, through the single-point layout rule, the layout rules for a single display point can be implemented, thereby refining the control granularity in the version control experiment. At the same time, multiple groups of experiments can be orthogonal, thereby improving the experimental group data.

[0048] Further, after obtaining the target entity model containing the point rule information for the version control experiment task, based on the target entity model, the component configuration information corresponding to the target intelligent agent is generated, and the component configuration information may include the interactive panel data corresponding to the skill item, and the layout rules (such as single-point layout rules, global layout rules, etc.) that characterize the point layout of the target skill item in the target client, wherein the interactive panel data is used to characterize the interactive operation items for implementing the corresponding skill item, and the layout order of the interactive operation items, specifically, the interactive operation items of the corresponding skill item, that is, the control for controlling the input parameters of the skill item during the execution of the skill item. The point layout of the target skill item in the target client is determined by the layout rules, and then the interactive operation items corresponding to each skill item are obtained by the interactive panel data. The above data is packaged according to the preset protocol to obtain the component configuration information. Finally, the component configuration information is sent to the target client on the terminal device side. After the target client parses the component configuration information, it generates and displays the skill items of the target intelligent agent in the dialogue interaction interface based on the layout rules and interactive panel data in the component configuration information. Finally, the server analyzes the effect of the point rule information by collecting the daily activity and retention of the target client, thereby completing the version control experiment task and obtaining relevant solutions for optimizing the point layout of the target intelligent agent.

[0049] Figure 3 A schematic diagram of an interactive operation project provided by an embodiment of the present disclosure, such as Figure 3As shown, in the dialogue interaction interface in the target client, after the target agent is started, the skill items of the target agent are first displayed, such as "article writing", "dialogue reply", "image generation", etc. shown in the figure. After that, after the target client responds to the trigger operation input by the user and triggers any of the skill items, the interactive operation items of the triggered skill item will be displayed. For example, after the skill item "article writing" is triggered, the interactive operation items displayed include type parameters such as "article", "email", "idea", and "tone", "length", "language", etc., as well as generation parameters such as "tone", "length", and "language". Among them, the generation parameters can be provided through an option bar, and the user can select through the option bar. For example, the option bar of the "tone" parameter includes parameter options such as "formal" and "colloquial"; the option bar of the "length" parameter includes parameter options such as "long", "medium", and "segment"; the option bar of the "language" parameter includes parameter options such as "Chinese" and "English". By clicking on the above interactive operation items, the user can further determine more detailed content generation parameters through simple interactive methods such as clicking operations, thereby achieving more precise control of the "skill items" of the target agent. The interactive panel data in the component configuration information not only records the above-mentioned interactive operation items by way of example, but also records the order of each interactive operation item. For example, "article" is displayed on the left side (before) of "email". Therefore, when the above-mentioned interactive operation items are displayed, the layout control of the interactive operation items is realized, thereby improving the control accuracy of the version control experiment task.

[0050] In this embodiment, a start request for a target intelligent agent is received; in response to the start request, a target entity model corresponding to the target intelligent agent is obtained, the target entity model contains point rule information for the version control experiment task, and the point rule information is used to characterize the point layout of the skill items of the target intelligent agent in the version control experiment task; according to the target entity model corresponding to the target intelligent agent, component configuration information corresponding to the target intelligent agent is generated, and the component configuration information is sent to the target client, so that the target client displays the skill items of the target intelligent agent based on the point layout corresponding to the point rule information. After receiving the start request, the target entity model corresponding to the target intelligent agent indicated by the start request is obtained, and the point layout of the skill items of the target intelligent agent is determined by using the point rule information in the target entity model for the version control experiment task, and then the corresponding component configuration information is generated and sent to the target client, so that the client can display the skill items of the target intelligent agent in a targeted manner based on the point rule information. Rapid configuration and flexible modification of the interactive component layout can be achieved, and the execution efficiency and experimental effect of the version control experiment task can be improved.

[0051] refer to Figure 4 , Figure 4 Schematic diagram of the interactive component layout configuration method provided in the embodiment of the present disclosure Figure 2 In this embodiment Figure 2 On the basis of the illustrated embodiment, step S103 is further refined, wherein the target entity model includes a skill item field, and the skill item field is used to represent the skill item configured by the agent. The interactive component layout configuration method includes:

[0052] Step S201: receiving a start request for a target agent sent by a target client.

[0053] Step S202: In response to the start request, a target entity model corresponding to the target agent is obtained, wherein the target entity model contains point rule information for the version control experiment task, and the point rule information is used to characterize the point layout of the agent's skill items in the version control experiment task.

[0054] Step S203: Determine the target skill item of the target agent according to the skill item field in the target entity model corresponding to the target agent.

[0055] Exemplarily, first, after obtaining the corresponding target entity model based on the start request, the server reads the skill item field in the target entity model for representing the skill item configured by the agent, and the corresponding field value of the skill item field is the unique identifier of the skill item. Among them, under the skill item field, there are usually multiple field values, that is, the target agent is configured with multiple target skill items that need to be displayed in the dialogue interaction interface.

[0056] In a possible implementation, the skill item field includes a first skill item field and a second skill item field, wherein the first skill item field is used to represent the acquisition method of the skill item corresponding to the agent, and the second skill item field is used to represent the acquisition parameters of the skill item, such as Figure 5 As shown, the specific implementation of step S203 includes:

[0057] Step S2031: Determine a target acquisition method for the skill item corresponding to the target agent according to the first skill item field in the target entity model corresponding to the target agent.

[0058] Step S2032: Determine the acquisition parameters of the skill item according to the second skill item field, and determine the target skill item of the target agent based on the target acquisition method and the acquisition parameters.

[0059] Exemplarily, the skill item field is composed of a first skill item field and a second skill item field, wherein the first skill item field is used to characterize the acquisition method of the skill item corresponding to the agent. For example, the field value of the first skill item field is 01, indicating that the skill item corresponding to the agent is configured by the recommendation system; the field value of the first skill item field is 02, indicating that the skill item corresponding to the agent is configured by the point experiment; the field value of the first skill item field is 03, indicating that the skill item corresponding to the agent is customized by the user. By acquiring the first skill item field in the target entity model corresponding to the target agent, the target acquisition method of the skill item corresponding to the target agent can be determined, such as one of the three acquisition methods mentioned above. The second skill item field is used to characterize the acquisition parameters of the skill item, and the acquisition parameters correspond to the acquisition method represented by the first skill item field, that is, there are matching acquisition parameters under different acquisition methods. Finally, by combining the target acquisition method and the acquisition parameters, the target skill item of the target agent can be determined.

[0060] Furthermore, in another possible implementation,

[0061] For example, Figure 6 As shown, the specific implementation of step S203 includes:

[0062] Step S2033: Obtain the preconfigured skill items corresponding to the target agent according to the skill item fields in the target entity model corresponding to the target agent.

[0063] Step S2034: Filter the preconfigured skill items according to the configuration validity of each preconfigured skill item to obtain the target skill item of the target agent, where the target skill item is the preconfigured skill item configured to be in an effective state among all the preconfigured skill items corresponding to the target agent.

[0064] Exemplarily, in a possible implementation, based on the skill item field in the target entity model corresponding to the target agent, the skill item corresponding to the target agent is determined to be a preconfigured skill item, that is, a skill item preconfigured for the target agent. After determining the preconfigured skill item, the configuration validity of each preconfigured skill item is further detected, wherein the configuration validity is whether the configuration item is configured to be effective (visible) to the user in the version control experiment task. Based on the configuration validity of each preconfigured skill item, the preconfigured skill item whose configuration validity is in an effective state (that is, visible to the user) is determined as the target skill item. Through the steps of this embodiment, the precision of the experimental control of the version control experiment task can be further improved, and the experimental effect can be improved.

[0065] Furthermore, in another possible implementation, the target entity model also includes a version field, which is used to represent the configuration version of the agent, such as Figure 7As shown, the specific implementation of step S203 includes:

[0066] Step S2030-1: Determine the target configuration version of the target agent based on the version field.

[0067] Step S2030-2: Determine the target skill item of the target agent according to the target configuration version and the skill item field in the target entity model corresponding to the target agent.

[0068] Exemplarily, further, in this embodiment, the target entity model also includes a version field, which is used to characterize the configuration version of the agent. The configuration version of the agent can be further determined through the version field, thereby realizing version control and enhancing the maintainability and isolation between different versions. Among them, in one possible implementation, the target agent corresponds to multiple entity models including the target entity model, and the configuration version corresponding to the target agent is recorded in the version field of the target entity model determined based on the startup request. In the steps of this embodiment, the target skill item of the target agent is jointly determined through the skill item field in the target configuration and the target entity model, thereby realizing isolation between agents of different versions and improving the maintainability of the version control experiment task.

[0069] Furthermore, in a possible implementation, Figure 5 , Figure 6 and Figure 7 Combining at least two of the above solutions to obtain another solution for determining the target skill item, specifically, combining Figure 5 , Figure 6 and Figure 7 Take the implementation of the solution as an example, Figure 8 As shown, the specific implementation of step S203 includes:

[0070] Step S203A: Determine the target configuration version of the target agent according to the version field.

[0071] Step S203B: Determine the target acquisition method of the skill item corresponding to the target agent of the target configuration version according to the first skill item field in the target entity model corresponding to the target agent of the target configuration version.

[0072] Step S203C: determining the acquisition parameters of the skill items according to the second skill item field in the target entity model corresponding to the target agent, and determining the optional skill items of the target agent based on the target acquisition method and the acquisition parameters.

[0073] Step S203D: Filter the optional skill items according to the configuration validity of each optional skill item to obtain the target skill item of the target agent, where the target skill item is the optional skill item configured to be in a valid state.

[0074] For example, in this embodiment, combined with Figures 5 to 7 The scheme in the embodiment first determines the target configuration version of the target intelligent agent, then determines the target acquisition method in combination with the first skill item field in the target entity model corresponding to the target intelligent agent of the target configuration version, and then determines the optional skill items of the target intelligent agent in combination with the acquisition parameters represented by the second skill item field, and finally screens based on the configuration validity of each optional skill item, and finally determines the optional skill items configured to be in an effective state as the target skill items of the target intelligent agent. The implementation methods of the above steps have been introduced one by one in the previous embodiment steps, and will not be repeated here.

[0075] Step S204: Determine the point layout of each target skill item according to the point rule information.

[0076] Exemplarily, after determining the target skill item, the point layout of each target skill item is further determined according to the point rule information. In a possible implementation, the target entity model includes a point rule field, and the point rule field records at least one point rule information and a task group identifier of the control experiment task group corresponding to the point rule information, such as Fig. 9 As shown, the specific implementation of step S204 includes:

[0077] Step S2041: Determine, according to the client identifier of the target client and the point rule field, the task group identifier of at least one target control experiment task group corresponding to the client identifier.

[0078] Step S2042: Determine at least one corresponding hit point rule information from the point rule field according to the task group identifier of at least one target control experiment task group.

[0079] Step S2043: determining target point rule information according to at least one hit point rule information, and determining the point layout of the target skill item based on the target point rule information.

[0080] Exemplarily, the target entity model includes skill item entity models corresponding to each target skill item. In the skill item entity model, the point rule field records the point rule adapted by the corresponding skill item, that is, the point rule information, and the task group identifier of the control experiment task group corresponding to the point rule information. After receiving the start request sent by the target client, the database is queried according to the client identifier carried in the start request, the experimental task group where the target client is located is hit, and the task group identifier of at least one target control experiment task group corresponding to the client identifier is determined. After that, combined with the content in the point rule field, according to the task group identifier of the target control experiment task group, at least one corresponding hit point rule information is determined from the point rule field. For example, according to the client identifier User_1 of the target client, the corresponding task group identifiers are determined to be Group_A_01 and Group_B_01, that is, the target client is in two versions of the control experiment tasks at the same time. Afterwards, according to the above-mentioned task group identifier, the corresponding point rule field is read to obtain two hit point rule information, such as Info_1 and Info_2, wherein the content of Info_1 is, for example, [skill id=a, point addr=1], indicating that the first skill item of the target agent is skill item "a". The content of Info_2 is, for example, [skill id=b, point addr=2], indicating that the second skill item of the target agent is skill item "b". Because the two rules are not in conflict, that is, the experiment based on the above rules is an orthogonal experiment, the two are located in different experimental traffic layers. Afterwards, according to the above-mentioned two hit point rule information, for example, the union of the two is determined as the target point rule information, that is, [skill id=a, point addr=1; skill id=b, point addr=2]. For other unrestricted display points, the corresponding skill items can be configured based on preset rules or randomly, so as to finally determine the point layout of each target skill item.

[0081] Furthermore, in a possible implementation, the target entity model also includes a traffic layer field and a traffic layer proportion field, wherein the traffic layer field is used to characterize the experimental traffic layer corresponding to the version control experiment task, wherein the version control experiment tasks corresponding to different traffic layer identifiers are orthogonal experiments; the traffic layer proportion field is used to characterize the proportion of the traffic of the target control experiment task group in the experimental traffic layer. Accordingly, the specific implementation method of step S2043 includes: according to the traffic layer field and the traffic layer proportion field, determine the target point rule information according to at least one hit point rule information.

[0082] Exemplarily, in this implementation step, after determining the two hit point rule information, the target point rule information is further determined according to the traffic layer field and the traffic layer proportion field. The target point rule information includes the traffic layer corresponding to the point rule and the traffic proportion, that is, how many processes are allocated to the point rule in the version control experiment task. For example, the content of the target point rule information determined based on the above steps is: [skill id = a, point addr = 1; skill id = b, point addr = 2, layer = 3, 20%], which means that the first skill item of the target intelligent agent is skill item "a", the second skill item of the target intelligent agent is skill item "b", and it is located in the third traffic layer. The proportion of this rule in the third traffic layer is 20%. Through the steps of this embodiment, the experimental parameter control in the version control experiment task process can be further improved, and the implementation effect can be improved.

[0083] Step S205: Obtain the skill item data of each target skill item, and generate component configuration information corresponding to the target intelligent agent according to the point layout and the skill item data of each target skill item, wherein the skill item data is used to implement the skill item function of the corresponding target skill item.

[0084] Exemplarily, skill item data is data stored on the server side and used to implement the skill item function of the corresponding target skill item. The skill item data stores the specific implementation method of the corresponding skill item, including the implementation logic, interaction logic and other information of the skill item. After determining the target skill item, based on the skill item identifier of the target skill item, the corresponding skill item data is obtained, and based on the point layout determined in the above steps and the skill item data of each target skill item, the component configuration information corresponding to the target intelligent entity is generated. Specifically, for example, it includes: obtaining the control template pack, converting uri to url, business processing, text filling, monitoring and dotting, and finally executing pack to generate the component configuration information corresponding to the target intelligent entity.

[0085] Furthermore, in a possible implementation, the skill item data includes interaction panel data corresponding to the skill item, and the interaction panel data is used to represent the arrangement order of the interaction operation items for implementing the corresponding skill item. Fig.10 As shown, the specific implementation of step S205 includes:

[0086] Step S2051: Generate first component configuration information according to the point layout, where the first component configuration information is used to characterize the point layout of each target skill item in the target client.

[0087] Step S2052: Generate second component configuration information according to the interaction panel data, where the second component configuration information is used to characterize the placement order of the interactive operation items after each target skill item is triggered.

[0088] Step S2053: Generate component configuration information corresponding to the target intelligent agent based on the first component configuration information and the second component configuration information.

[0089] Exemplarily, after obtaining the point layout, firstly, based on the point layout, firstly, generate the first component configuration information representing the point layout of each target skill item in the target client, such as a sequence of skill item identifiers, or the coordinate positions corresponding to the skill item identifiers; then, based on the interaction panel data, generate the arrangement order of the interactive operation items representing each target skill item after being triggered, wherein the interactive operation items are, for example, components for further interaction in the submenus and subpages after the skill item is triggered, for details, please refer to Figure 3 Relevant examples in the illustrated embodiments. When generating the first component configuration information and the second component configuration information, the first component configuration information and the second component configuration information are packaged to generate the component configuration information corresponding to the target intelligent agent, so that the component configuration information corresponding to the target intelligent agent can realize the layout control of the skill items and the interactive operation items corresponding to the skill items, thereby increasing the comparison dimension of the version control experiment task and improving the experimental effect of the version control experiment task.

[0090] Step S206: Send the component configuration information to the target client, so that the target client displays the skill items of the target agent based on the point layout corresponding to the point rule information.

[0091] In this embodiment, the implementation of steps S201-S202 and step S206 is disclosed in the present disclosure. Figure 2 The implementation methods of steps S101 to 102 and the second half of step S103 in the illustrated embodiment are the same and will not be described in detail here.

[0092] Corresponding to the interactive component layout configuration method of the above embodiment, Fig.11 A structural block diagram of an interactive component layout configuration device provided in an embodiment of the present disclosure. The method described in the above embodiment can be executed by the interactive component layout configuration device, which can be implemented by software and / or hardware, and can be integrated in an electronic device with certain data processing functions. The electronic device may include but is not limited to a mobile terminal with big data processing capabilities, and a fixed terminal with big data processing capabilities such as a desktop computer and a supercomputer.

[0093] For the sake of convenience, only the parts related to the embodiments of the present disclosure are shown. Fig.11 , the interactive component layout configuration device 3 includes:

[0094] A receiving module 31 is used to receive a start request for a target agent;

[0095] The processing module 32 is used to obtain a target entity model corresponding to the target agent in response to the start request, wherein the target entity model contains point rule information for the version control experiment task, and the point rule information is used to characterize the point layout of the skill items of the agent in the version control experiment task;

[0096] The generation module 33 is used to generate component configuration information corresponding to the target intelligent agent according to the target entity model corresponding to the target intelligent agent, and the component configuration information is used to enable the target client to display the skill items of the target intelligent agent based on the point layout corresponding to the point rule information.

[0097] According to one or more embodiments of the present disclosure, the start request includes a first identifier representing the target intelligent agent, and the target entity model includes an intelligent agent identification field representing the corresponding intelligent agent; the processing module 32 is specifically used to: retrieve the pre-configured target entity model in the database according to the first identifier, and obtain the target entity model whose intelligent agent identification field is the first identifier; determine the target entity model whose intelligent agent identification field is the first identifier as the target entity model of the target intelligent agent.

[0098] According to one or more embodiments of the present disclosure, the point rule information includes a single-point layout rule, which is used to indicate N target display points and the skill items of the target intelligent agent corresponding to each target display point, wherein the target client has M display points for displaying the skill items of the target intelligent agent, and N is an integer greater than 0 and less than M.

[0099] According to one or more embodiments of the present disclosure, the target entity model includes a skill item field, which is used to represent the skill items configured by the intelligent agent. The generation module 33 is specifically used to: determine the target skill items of the target intelligent agent according to the skill item field in the target entity model corresponding to the target intelligent agent; determine the point layout of each target skill item according to the point rule information; obtain the skill item data of each target skill item, and generate component configuration information corresponding to the target intelligent agent according to the point layout and the skill item data of each target skill item, wherein the skill item data is used to implement the skill item function of the corresponding target skill item.

[0100] According to one or more embodiments of the present disclosure, the skill item field includes a first skill item field and a second skill item field, wherein the first skill item field is used to characterize the acquisition method of the skill item corresponding to the intelligent agent, and the second skill item field is used to characterize the acquisition parameters of the skill item; when the generation module 33 determines the target skill item of the target intelligent agent according to the skill item field in the target entity model corresponding to the target intelligent agent, it is specifically used to: determine the target acquisition method of the skill item corresponding to the target intelligent agent according to the first skill item field in the target entity model corresponding to the target intelligent agent; determine the acquisition parameters of the skill item according to the second skill item field, and determine the target skill item of the target intelligent agent based on the target acquisition method and the acquisition parameters; wherein the field value of the first skill item field includes one of the following: recommendation system, point experiment configuration, user customization.

[0101] According to one or more embodiments of the present disclosure, when the generation module 33 determines the target skill items of the target intelligent agent according to the skill item fields in the target entity model corresponding to the target intelligent agent, it is specifically used to: obtain the preconfigured skill items corresponding to the target intelligent agent according to the skill item fields in the target entity model corresponding to the target intelligent agent; filter the preconfigured skill items according to the configuration validity of each preconfigured skill item to obtain the target skill items of the target intelligent agent, and the target skill items are the preconfigured skill items that are configured to be in an effective state among all the preconfigured skill items corresponding to the target intelligent agent.

[0102] According to one or more embodiments of the present disclosure, the target entity model includes a point rule field, in which at least one point rule information and a task group identifier of the control experiment task group corresponding to the point rule information are recorded; when the generation module 33 determines the point layout of each target skill item according to the point rule information, it is specifically used to: determine the task group identifier of at least one target control experiment task group corresponding to the client identifier according to the client identifier and the point rule field of the target client; determine at least one corresponding hit point rule information from the point rule field according to the task group identifier of at least one target control experiment task group; determine the target point rule information according to the at least one hit point rule information, and determine the point layout of the target skill item based on the target point rule information.

[0103] According to one or more embodiments of the present disclosure, the target entity model also includes a traffic layer field and a traffic layer proportion field, wherein the traffic layer field is used to characterize the experimental traffic layer corresponding to the version control experiment task, wherein the version control experiment tasks corresponding to different traffic layer identifiers are orthogonal experiments; the traffic layer proportion field is used to characterize the proportion of the traffic of the target control experiment task group in the experimental traffic layer; when the generation module 33 determines the target point rule information according to at least one hit point rule information, it is specifically used to: determine the target point rule information according to the traffic layer field and the traffic layer proportion field according to at least one hit point rule information.

[0104] According to one or more embodiments of the present disclosure, the skill item data includes interaction panel data corresponding to the skill item, and the interaction panel data is used to characterize the layout order of interactive operation items for realizing the corresponding skill item; when the generation module 33 generates component configuration information corresponding to the target intelligent entity according to the point layout and the skill item data of each target skill item, it is specifically used to: generate first component configuration information according to the point layout, and the first component configuration information is used to characterize the point layout of each target skill item in the target client; generate second component configuration information according to the interaction panel data, and the second component configuration information is used to characterize the layout order of interactive operation items of each target skill item after being triggered; generate component configuration information corresponding to the target intelligent entity according to the first component configuration information and the second component configuration information.

[0105] According to one or more embodiments of the present disclosure, the target entity model also includes a version field, which is used to characterize the configuration version of the agent; when the generation module 33 determines the target skill items of the target agent based on the skill item fields in the target entity model corresponding to the target agent, it is specifically used to: determine the target configuration version of the target agent based on the version field; determine the target skill items of the target agent based on the target configuration version and the skill item fields in the target entity model corresponding to the target agent.

[0106] The receiving module 31, the processing module 32 and the generating module 33 are connected in sequence. The interactive component layout configuration 3 provided in this embodiment can implement the technical solution of the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated in this embodiment.

[0107] Fig.12 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure is shown in FIG. Fig.12 As shown, the electronic device 4 includes:

[0108] A processor 41, and a memory 42 communicatively connected to the processor 41;

[0109] The memory 42 stores computer executable instructions;

[0110] The processor 41 executes the computer execution instructions stored in the memory 42 to implement the following Figure 2-Figure 10 The interactive component layout configuration method in the illustrated embodiment.

[0111] Optionally, the processor 41 and the memory 42 are connected via a bus 43 .

[0112] For related instructions, please refer to Figure 2-Figure 10 The relevant descriptions and effects corresponding to the steps in the corresponding embodiments can be understood, and no further elaboration is made here.

[0113] The present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, which are used to implement the present invention when executed by a processor. Figure 2-Figure 10 An interactive component layout configuration method is provided in any one of the corresponding embodiments.

[0114] The present invention provides a computer program product, including a computer program. When the computer program is executed by a processor, the present invention is realized. Figure 2-Figure 10 An interactive component layout configuration method is provided in any one of the corresponding embodiments.

[0115] In order to implement the above embodiment, the embodiment of the present disclosure also provides an electronic device.

[0116] refer to Fig.13 , which shows a schematic diagram of the structure of an electronic device 900 suitable for implementing the embodiment of the present disclosure, and the electronic device 900 may be a terminal device or a server. The terminal device may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, personal digital assistants (PDAs), tablet computers, portable multimedia players (PMPs), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig.13 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0117] like Fig.13As shown, the electronic device 900 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage device 908 to a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the electronic device 900 are also stored. The processing device 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0118] Typically, the following devices may be connected to the I / O interface 905: input devices 906 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 907 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 908 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 909. The communication device 909 may allow the electronic device 900 to communicate with other devices wirelessly or by wire to exchange data. Although Fig.13 The electronic device 900 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0119] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains 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 a communication device 909, or installed from a storage device 908, or installed from a ROM 902. When the computer program is executed by the processing device 901, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0120] It should be noted that the computer-readable medium disclosed above 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 disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, 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 suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0121] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0122] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0123] Computer program code for performing the operations of the present disclosure 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 user's computer, partially on the user's computer, as an independent software package, partially on the user's 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 user's computer via 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., via the Internet using an Internet service provider).

[0124] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square 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 implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square 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.

[0125] The units or modules involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit or module does not, in some cases, constitute a limitation on the unit itself.

[0126] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0127] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0128] In a first aspect, according to one or more embodiments of the present disclosure, a method for configuring an interactive component layout is provided, comprising:

[0129] Receive a start request for a target intelligent agent; in response to the start request, obtain a target entity model corresponding to the target intelligent agent, wherein the target entity model contains point rule information for a version control experiment task, and the point rule information is used to characterize the point layout of the agent's skill items in the version control experiment task; based on the target entity model corresponding to the target intelligent agent, generate component configuration information corresponding to the target intelligent agent, and the component configuration information is used to enable the target client to display the skill items of the target intelligent agent based on the point layout corresponding to the point rule information.

[0130] According to one or more embodiments of the present disclosure, the start request includes a first identifier representing the target intelligent agent, and the target entity model includes an intelligent agent identification field representing the corresponding intelligent agent; in response to the start request, the target entity model corresponding to the target intelligent agent is obtained, including: retrieving the pre-configured target entity model in the database according to the first identifier, and obtaining the target entity model whose intelligent agent identification field is the first identifier; and determining the target entity model whose intelligent agent identification field is the first identifier as the target entity model of the target intelligent agent.

[0131] According to one or more embodiments of the present disclosure, the point rule information includes a single-point layout rule, which is used to indicate N target display points and the skill items of the target intelligent agent corresponding to each of the target display points, wherein the target client has M display points for displaying the skill items of the target intelligent agent, and N is an integer greater than 0 and less than M.

[0132] According to one or more embodiments of the present disclosure, the target entity model includes a skill item field, and the skill item field is used to represent the skill items configured by the intelligent agent. The component configuration information corresponding to the target intelligent agent is generated according to the target entity model corresponding to the target intelligent agent, including: determining the target skill items of the target intelligent agent according to the skill item field in the target entity model corresponding to the target intelligent agent; determining the point layout of each of the target skill items according to the point rule information; acquiring the skill item data of each of the target skill items, and generating the component configuration information corresponding to the target intelligent agent according to the point layout and the skill item data of each of the target skill items, wherein the skill item data is used to implement the skill item function of the corresponding target skill item.

[0133] According to one or more embodiments of the present disclosure, the skill item field includes a first skill item field and a second skill item field, wherein the first skill item field is used to characterize the acquisition method of the skill item corresponding to the agent, and the second skill item field is used to characterize the acquisition parameters of the skill item; determining the target skill item of the target agent according to the skill item field in the target entity model corresponding to the target agent includes: determining the target acquisition method of the skill item corresponding to the target agent according to the first skill item field in the target entity model corresponding to the target agent; determining the acquisition parameters of the skill item according to the second skill item field, and determining the target skill item of the target agent based on the target acquisition method and the acquisition parameters; wherein the field value of the first skill item field includes one of the following: recommendation system, point experiment configuration, user customization.

[0134] According to one or more embodiments of the present disclosure, the target skill items of the target intelligent agent are determined according to the skill item fields in the target entity model corresponding to the target intelligent agent, including: obtaining the preconfigured skill items corresponding to the target intelligent agent according to the skill item fields in the target entity model corresponding to the target intelligent agent; filtering the preconfigured skill items according to the configuration validity of each of the preconfigured skill items to obtain the target skill items of the target intelligent agent, wherein the target skill items are the preconfigured skill items that are configured as effective among all the preconfigured skill items corresponding to the target intelligent agent.

[0135] According to one or more embodiments of the present disclosure, the target entity model includes a point rule field, in which at least one point rule information and a task group identifier of the control experiment task group corresponding to the point rule information are recorded; determining the point layout of each target skill item according to the point rule information includes: determining the task group identifier of at least one target control experiment task group corresponding to the client identifier according to the client identifier of the target client and the point rule field; determining at least one corresponding hit point rule information from the point rule field according to the task group identifier of the at least one target control experiment task group; determining the target point rule information according to the at least one hit point rule information, and determining the point layout of the target skill item based on the target point rule information.

[0136] According to one or more embodiments of the present disclosure, the target entity model also includes a traffic layer field and a traffic layer proportion field, wherein the traffic layer field is used to characterize the experimental traffic layer corresponding to the version control experiment task, wherein the version control experiment tasks corresponding to different traffic layer identifiers are orthogonal experiments; the traffic layer proportion field is used to characterize the proportion of the traffic of the target control experiment task group in the experimental traffic layer; determining the target point rule information according to the at least one hit point rule information includes: determining the target point rule information according to the traffic layer field and the traffic layer proportion field according to the at least one hit point rule information.

[0137] According to one or more embodiments of the present disclosure, the skill item data includes interaction panel data corresponding to the skill item, and the interaction panel data is used to characterize the layout order of interactive operation items for realizing the corresponding skill item; the component configuration information corresponding to the target intelligent agent is generated according to the point layout and the skill item data of each target skill item, including: generating first component configuration information according to the point layout, and the first component configuration information is used to characterize the point layout of each target skill item in the target client; generating second component configuration information according to the interaction panel data, and the second component configuration information is used to characterize the layout order of interactive operation items of each target skill item after being triggered; generating component configuration information corresponding to the target intelligent agent according to the first component configuration information and the second component configuration information.

[0138] According to one or more embodiments of the present disclosure, the target entity model also includes a version field, and the version field is used to characterize the configuration version of the agent; determining the target skill item of the target agent based on the skill item field in the target entity model corresponding to the target agent includes: determining the target configuration version of the target agent based on the version field; determining the target skill item of the target agent based on the target configuration version and the skill item field in the target entity model corresponding to the target agent.

[0139] In a second aspect, according to one or more embodiments of the present disclosure, a device for configuring an interactive component layout is provided, comprising:

[0140] A receiving module, used for receiving a start request for a target intelligent agent;

[0141] A processing module, configured to obtain, in response to the start request, a target entity model corresponding to the target intelligent agent, wherein the target entity model includes point rule information for the version control experiment task, and the point rule information is used to characterize the point layout of the skill items of the intelligent agent in the version control experiment task;

[0142] A generation module is used to generate component configuration information corresponding to the target intelligent agent based on the target entity model corresponding to the target intelligent agent, and the component configuration information is used to enable the target client to display the skill items of the target intelligent agent based on the point layout corresponding to the point rule information.

[0143] According to one or more embodiments of the present disclosure, the start request includes a first identifier representing the target intelligent agent, and the target entity model contains an intelligent agent identification field representing the corresponding intelligent agent; the processing module is specifically used to: retrieve the pre-configured target entity model in the database according to the first identifier, and obtain the target entity model whose intelligent agent identification field is the first identifier; determine the target entity model whose intelligent agent identification field is the first identifier as the target entity model of the target intelligent agent.

[0144] According to one or more embodiments of the present disclosure, the point rule information includes a single-point layout rule, which is used to indicate N target display points and the skill items of the target intelligent agent corresponding to each of the target display points, wherein the target client has M display points for displaying the skill items of the target intelligent agent, and N is an integer greater than 0 and less than M.

[0145] According to one or more embodiments of the present disclosure, the target entity model includes a skill item field, and the skill item field is used to represent the skill items configured by the intelligent agent. The generation module is specifically used to: determine the target skill items of the target intelligent agent according to the skill item field in the target entity model corresponding to the target intelligent agent; determine the point layout of each of the target skill items according to the point rule information; obtain the skill item data of each of the target skill items, and generate component configuration information corresponding to the target intelligent agent according to the point layout and the skill item data of each of the target skill items, wherein the skill item data is used to implement the skill item function of the corresponding target skill item.

[0146] According to one or more embodiments of the present disclosure, the skill item field includes a first skill item field and a second skill item field, wherein the first skill item field is used to characterize the acquisition method of the skill item corresponding to the agent, and the second skill item field is used to characterize the acquisition parameters of the skill item; when the generation module determines the target skill item of the target agent according to the skill item field in the target entity model corresponding to the target agent, it is specifically used to: determine the target acquisition method of the skill item corresponding to the target agent according to the first skill item field in the target entity model corresponding to the target agent; determine the acquisition parameters of the skill item according to the second skill item field, and determine the target skill item of the target agent based on the target acquisition method and the acquisition parameters; wherein the field value of the first skill item field includes one of the following: recommendation system, point experiment configuration, user customization.

[0147] According to one or more embodiments of the present disclosure, when the generation module determines the target skill items of the target intelligent agent according to the skill item fields in the target entity model corresponding to the target intelligent agent, it is specifically used to: obtain the preconfigured skill items corresponding to the target intelligent agent according to the skill item fields in the target entity model corresponding to the target intelligent agent; filter the preconfigured skill items according to the configuration validity of each of the preconfigured skill items to obtain the target skill items of the target intelligent agent, and the target skill items are the preconfigured skill items that are configured as effective among all the preconfigured skill items corresponding to the target intelligent agent.

[0148] According to one or more embodiments of the present disclosure, the target entity model includes a point rule field, in which at least one point rule information and a task group identifier of the control experiment task group corresponding to the point rule information are recorded; when the generation module determines the point layout of each target skill item according to the point rule information, it is specifically used to: determine the task group identifier of at least one target control experiment task group corresponding to the client identifier according to the client identifier of the target client and the point rule field; determine the corresponding at least one hit point rule information from the point rule field according to the task group identifier of the at least one target control experiment task group; determine the target point rule information according to the at least one hit point rule information, and determine the point layout of the target skill item based on the target point rule information.

[0149] According to one or more embodiments of the present disclosure, the target entity model also includes a traffic layer field and a traffic layer proportion field, wherein the traffic layer field is used to characterize the experimental traffic layer corresponding to the version control experiment task, wherein the version control experiment tasks corresponding to different traffic layer identifiers are orthogonal experiments; the traffic layer proportion field is used to characterize the proportion of the traffic of the target control experiment task group in the experimental traffic layer; when the generation module determines the target point rule information according to the at least one hit point rule information, it is specifically used to: determine the target point rule information according to the traffic layer field and the traffic layer proportion field according to the at least one hit point rule information.

[0150] According to one or more embodiments of the present disclosure, the skill item data includes interaction panel data corresponding to the skill item, and the interaction panel data is used to characterize the layout order of interactive operation items for implementing the corresponding skill item; when the generation module generates the component configuration information corresponding to the target intelligent agent according to the point layout and the skill item data of each target skill item, it is specifically used to: generate first component configuration information according to the point layout, and the first component configuration information is used to characterize the point layout of each target skill item in the target client; generate second component configuration information according to the interaction panel data, and the second component configuration information is used to characterize the layout order of interactive operation items of each target skill item after being triggered; generate component configuration information corresponding to the target intelligent agent according to the first component configuration information and the second component configuration information.

[0151] According to one or more embodiments of the present disclosure, the target entity model also includes a version field, and the version field is used to characterize the configuration version of the agent; when the generation module determines the target skill item of the target agent according to the skill item field in the target entity model corresponding to the target agent, it is specifically used to: determine the target configuration version of the target agent according to the version field; determine the target skill item of the target agent according to the target configuration version and the skill item field in the target entity model corresponding to the target agent.

[0152] In a third aspect, according to one or more embodiments of the present disclosure, there is provided an electronic device, comprising: at least one processor and a memory;

[0153] The memory stores computer-executable instructions;

[0154] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the interactive component layout configuration method described in the first aspect and various possible designs of the first aspect.

[0155] In a fourth aspect, according to one or more embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer execution instructions. When a processor executes the computer execution instructions, the interactive component layout configuration method described in the first aspect and various possible designs of the first aspect is implemented.

[0156] In a fifth aspect, according to one or more embodiments of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the interactive component layout configuration method as described in the first aspect and various possible designs of the first aspect.

[0157] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions 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 disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0158] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0159] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for configuring an interactive component layout, characterized in that: The method comprises: receiving a start request for a target agent; In response to the start request, a target entity model corresponding to the target agent is obtained, wherein the target entity model includes point rule information for the version control experiment task, and the point rule information is used to characterize the point layout of the agent's skill items in the version control experiment task; According to the target entity model corresponding to the target intelligent agent, component configuration information corresponding to the target intelligent agent is generated, and the component configuration information is used to enable the target client to display the skill items of the target intelligent agent based on the point layout corresponding to the point rule information.

2. The method according to claim 1, characterized in that The start request includes a first identifier representing the target intelligent agent, and the target entity model includes an intelligent agent identifier field representing the corresponding intelligent agent; in response to the start request, obtaining the target entity model corresponding to the target intelligent agent includes: According to the first identifier, a target entity model preconfigured in a database is retrieved to obtain a target entity model whose agent identification field is the first identifier; The target entity model whose agent identification field is the first identification is determined as the target entity model of the target agent.

3. The method according to claim 1, characterized in that The point rule information includes a single-point layout rule, which is used to indicate N target display points and the skill items of the target intelligent entity corresponding to each of the target display points, wherein the target client has M display points for displaying the skill items of the target intelligent entity, and N is an integer greater than 0 and less than M.

4. The method according to claim 1, characterized in that The target entity model includes a skill item field, and the skill item field is used to represent the skill item configured by the agent. The component configuration information corresponding to the target agent is generated according to the target entity model corresponding to the target agent, including: Determining a target skill item of the target intelligent agent according to a skill item field in a target entity model corresponding to the target intelligent agent; Determining the point layout of each target skill item according to the point rule information; The skill item data of each of the target skill items is obtained, and component configuration information corresponding to the target intelligent agent is generated according to the point layout and the skill item data of each of the target skill items, wherein the skill item data is used to implement the skill item function of the corresponding target skill item.

5. The method according to claim 4, characterized in that The skill item field includes a first skill item field and a second skill item field, wherein the first skill item field is used to represent the acquisition method of the skill item corresponding to the agent, and the second skill item field is used to represent the acquisition parameter of the skill item; the determining the target skill item of the target agent according to the skill item field in the target entity model corresponding to the target agent includes: Determining a target acquisition method for the skill item corresponding to the target intelligent agent according to the first skill item field in the target entity model corresponding to the target intelligent agent; Determining an acquisition parameter of a skill item according to the second skill item field, and determining a target skill item of the target agent based on the target acquisition method and the acquisition parameter; Among them, the field value of the first skill item field includes one of the following: recommendation system, point experiment configuration, and user customization.

6. The method according to claim 4, characterized in that Determining a target skill item of the target agent according to a skill item field in a target entity model corresponding to the target agent includes: Obtaining a preconfigured skill item corresponding to the target agent according to a skill item field in a target entity model corresponding to the target agent; The preconfigured skill items are screened according to the configuration validity of each of the preconfigured skill items to obtain the target skill items of the target intelligent agent, and the target skill items are the preconfigured skill items that are configured to be in an effective state among all the preconfigured skill items corresponding to the target intelligent agent.

7. The method according to claim 4, characterized in that The target entity model includes a point rule field, in which at least one point rule information and a task group identifier of a control experiment task group corresponding to the point rule information are recorded; Determining the point layout of each target skill item according to the point rule information includes: Determine, according to the client identifier of the target client and the point rule field, a task group identifier of at least one target control experiment task group corresponding to the client identifier; Determine at least one corresponding hit point rule information from the point rule field according to the task group identifier of the at least one target control experiment task group; Target point rule information is determined according to the at least one hit point rule information, and point layout of the target skill item is determined based on the target point rule information.

8. The method according to claim 7, characterized in that The target entity model also includes a traffic layer field and a traffic layer proportion field, wherein the traffic layer field is used to characterize the experimental traffic layer corresponding to the version control experiment task, wherein the version control experiment tasks corresponding to different traffic layer identifiers are orthogonal experiments; the traffic layer proportion field is used to characterize the proportion of the traffic of the target control experiment task group in the experimental traffic layer; The determining target point rule information according to the at least one hit point rule information comprises: According to the traffic layer field and the traffic layer proportion field, target point rule information is determined according to the at least one hit point rule information.

9. The method according to claim 4, characterized in that The skill item data includes interaction panel data corresponding to the skill item, and the interaction panel data is used to represent the arrangement order of interactive operation items for realizing the corresponding skill item; The generating component configuration information corresponding to the target agent according to the point layout and the skill item data of each target skill item includes: Generate first component configuration information according to the point layout, where the first component configuration information is used to represent the point layout of each target skill item in the target client; Generate second component configuration information according to the interaction panel data, where the second component configuration information is used to represent the placement order of the interactive operation items of each target skill item after being triggered; Component configuration information corresponding to the target agent is generated according to the first component configuration information and the second component configuration information.

10. The method according to claim 4, characterized in that The target entity model further includes a version field, and the version field is used to represent the configuration version of the agent; and determining the target skill item of the target agent according to the skill item field in the target entity model corresponding to the target agent includes: Determining a target configuration version of the target agent according to the version field; The target skill item of the target intelligent agent is determined according to the target configuration version and the skill item field in the target entity model corresponding to the target intelligent agent.

11. An interactive component layout configuration device, characterized in that: include: A receiving module, used for receiving a start request for a target intelligent agent; A processing module, configured to obtain, in response to the start request, a target entity model corresponding to the target intelligent agent, wherein the target entity model includes point rule information for the version control experiment task, and the point rule information is used to characterize the point layout of the skill items of the intelligent agent in the version control experiment task; A generation module is used to generate component configuration information corresponding to the target intelligent agent based on the target entity model corresponding to the target intelligent agent, and the component configuration information is used to enable the target client to display the skill items of the target intelligent agent based on the point layout corresponding to the point rule information.

12. An electronic device, characterized in that: include: Processor and memory; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor executes the interactive component layout configuration method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, the interactive component layout configuration method according to any one of claims 1 to 10 is implemented.

14. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for configuring the layout of interactive components according to any one of claims 1 to 10 is implemented.

Images