An autonomous people circle panel generation method, system, terminal and storage medium

Abstract

The application discloses a kind of self-encircles person panel generation method, system, terminal and storage medium, the method includes: obtaining target dataset, the target dataset is parsed operation, obtains multiple behavior labels;Obtain preset attribute rule, obtain the multiple first attributes corresponding to each behavior label according to the preset attribute rule;According to the target dataset, obtain the multiple options corresponding to each first attribute, and obtain the associated attribute corresponding to each option;According to preset rendering component and all the rendering operation of associated attribute is carried out, and self-encircles person panel is generated.The application can automatically generate self-encircles person panel according to target dataset, improve the efficiency of creating self-encircles person panel, maintenance cost is low, improve the use experience of user.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, system, terminal, and computer-readable storage medium for generating an autonomous human-captured panel. Background Technology

[0002] With the continuous development of society and the economy, people's living standards are constantly improving, and the increasing consumer spending power is also promoting the development of the advertising industry. In order to improve the targeting of advertising, various companies are equipped with corresponding DMP (Data Management Platform) platforms, and perform corresponding autonomous user segmentation based on the DMP platform. Autonomous user segmentation means that the company's operations personnel can segment and further target users according to the required rules.

[0003] Currently, the autonomous user identification panel in the DMP platform requires technical personnel to set it up based on the acquired data. It cannot automatically generate the corresponding autonomous user identification panel based on the target dataset. The hierarchy between various attributes needs to be set manually, which is time-consuming and has high maintenance costs.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide a method, system, terminal, and computer-readable storage medium for generating autonomous user-capture panels. This aims to solve the problem that existing DMP platforms cannot automatically generate corresponding autonomous user-capture panels based on target data, requiring manual setup by technicians, which is time-consuming and costly to maintain.

[0006] To achieve the above objectives, the present invention provides a method for generating an autonomous user-selection panel, the method comprising the following steps:

[0007] Obtain the target dataset, and perform parsing operations on the target dataset to obtain multiple behavior labels;

[0008] Obtain preset attribute rules, and obtain multiple first-level attributes corresponding to each behavior tag according to the preset attribute rules;

[0009] Based on the target dataset, obtain multiple options corresponding to each of the first-level attributes, and obtain the associated attributes corresponding to each option;

[0010] Rendering operations are performed based on preset rendering components and all associated attributes to generate an autonomous user selection panel.

[0011] Optionally, the autonomous user panel generation method, wherein obtaining the target dataset and parsing the target dataset to obtain multiple behavior labels specifically includes:

[0012] Receive the target dataset sent by the backend, wherein the target dataset includes target data corresponding to multiple target objects;

[0013] The target data corresponding to all the target objects is parsed to obtain the behavior tag corresponding to each target data.

[0014] Optionally, in the self-organized user grouping panel generation method, the step of obtaining preset attribute rules and acquiring multiple first-level attributes corresponding to each behavior tag according to the preset attribute rules specifically includes:

[0015] Receive preset attribute rules sent by the backend, wherein the preset attribute rules are set by the backend according to each of the behavior tags;

[0016] Obtain multiple first-level attributes corresponding to each behavior tag in the preset attribute rules.

[0017] Optionally, the autonomous user panel generation method, wherein obtaining multiple options corresponding to each primary attribute based on the target dataset and acquiring the associated attributes corresponding to each option specifically includes:

[0018] The target data corresponding to each target object in the target dataset is parsed to obtain multiple option data corresponding to each first-level attribute;

[0019] The multiple option data corresponding to each of the first-level attributes are classified to obtain multiple options corresponding to each of the first-level attributes;

[0020] For each option, a judgment operation is performed. If the option includes an association field, the association attribute of the corresponding option is determined to have an association relationship, and all corresponding options are treated as dynamic options.

[0021] If there is no associated field in the option, the associated attribute of the corresponding option will be determined to have no associated relationship, and all corresponding options will be treated as static options.

[0022] After completing the judgment operation for all the options, the associated attributes corresponding to each option are obtained.

[0023] Optionally, in the autonomous human-circle panel generation method, the preset rendering component includes a dynamic recursive component and a flattening component.

[0024] Optionally, in the autonomous user selection panel generation method, each of the associated fields includes a dynamic sub-attribute of the corresponding option;

[0025] The step of performing rendering operations based on preset rendering components and all associated attributes to generate an autonomous user selection panel specifically includes:

[0026] Based on the flattened components, static dropdown components are generated for each of the first-level attributes, and multiple options corresponding to each of the first-level attributes are filled into the corresponding static dropdown components to complete the construction of each static dropdown component;

[0027] Based on the dynamic recursive component, a dynamic dropdown component is generated for each of the dynamic options;

[0028] The dynamic sub-attributes corresponding to each dynamic option are populated into the corresponding dynamic dropdown component to complete the construction of each dynamic dropdown component;

[0029] Generate an autonomous grouping panel based on all the static dropdown components and all the dynamic dropdown components.

[0030] Optionally, the self-selection panel generation method further includes, after filling the dynamic sub-attributes corresponding to each dynamic option into the corresponding dynamic dropdown component:

[0031] Perform a judgment operation on each of the dynamic lower-level attributes to obtain the associated attributes of each of the dynamic lower-level attributes;

[0032] If the associated attribute of the dynamic sub-attribute has an association relationship, then retrieve the dynamic associated field corresponding to the dynamic sub-attribute.

[0033] A secondary dynamic dropdown component is generated based on each of the aforementioned dynamic association fields, and each of the aforementioned dynamic association fields is filled into the corresponding secondary dynamic dropdown component to complete the construction of the secondary dynamic dropdown component;

[0034] A judgment operation is performed on each secondary dynamic attribute in the dynamic association field to obtain the association attribute of each secondary dynamic attribute. If the association attribute of the secondary dynamic attribute has an association relationship, the secondary dynamic association field corresponding to the secondary dynamic attribute is obtained, and a tertiary dynamic drop-down component is generated based on the secondary dynamic association field.

[0035] The second-level dynamic association fields in the three-level dynamic dropdown component are judged, ... until all options in the final generated dynamic dropdown component have no association relationship.

[0036] Furthermore, to achieve the above objectives, the present invention also provides an autonomous human-capturing panel generation system, wherein the autonomous human-capturing panel generation system includes:

[0037] The behavior label acquisition module is used to acquire the target dataset, parse the target dataset, and obtain multiple behavior labels.

[0038] The first-level attribute acquisition module is used to acquire preset attribute rules and obtain multiple first-level attributes corresponding to each behavior tag according to the preset attribute rules.

[0039] The associated attribute acquisition module is used to obtain multiple options corresponding to each of the first-level attributes based on the target dataset, and to obtain the associated attribute corresponding to each option;

[0040] The "Circle Panel Generation Module" is used to perform rendering operations based on preset rendering components and all the associated attributes to generate an autonomous circle panel.

[0041] In addition, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and an autonomous human-circling panel generation program stored in the memory and executable on the processor, wherein when the autonomous human-circling panel generation program is executed by the processor, it implements the steps of the autonomous human-circling panel generation method as described above.

[0042] In addition, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an autonomous human-circling panel generation program, which, when executed by a processor, implements the steps of the autonomous human-circling panel generation method as described above.

[0043] In this invention, a target dataset is acquired, parsed to obtain multiple behavior labels; preset attribute rules are obtained, and multiple primary attributes corresponding to each behavior label are obtained according to the preset attribute rules; multiple options corresponding to each primary attribute are obtained according to the target dataset, and associated attributes corresponding to each option are obtained; a rendering operation is performed according to a preset rendering component and all the associated attributes to generate an autonomous user selection panel. This invention can automatically generate an autonomous user selection panel based on a target dataset, improving the efficiency of creating autonomous user selection panels, reducing maintenance costs, and enhancing the user experience. Attached Figure Description

[0044] Figure 1 This is a flowchart of a preferred embodiment of the autonomous human-circle panel generation method of the present invention;

[0045] Figure 2 This is a schematic diagram of an autonomously selected panel, which is a preferred embodiment of the autonomously selected panel generation method of the present invention.

[0046] Figure 3 This is a schematic diagram of a preferred embodiment of the autonomous human-circling panel generation system of the present invention;

[0047] Figure 4 This is a schematic diagram of the operating environment of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0049] The preferred embodiment of the method for generating an autonomous human-captured panel according to the present invention, such as... Figure 1 As shown, the autonomous grouping panel generation method includes the following steps:

[0050] Step S10: Obtain the target dataset and perform a parsing operation on the target dataset to obtain multiple behavior labels.

[0051] Specifically, in a preferred embodiment of the present invention, the target dataset to be processed is first obtained. The target dataset contains target data corresponding to multiple target objects. Then, all target data summarized in the target dataset is parsed to generate multiple behavior labels, such as... Figure 2 As shown, the behavior tags are the bottom row, such as "Start-up Behavior", "Member Status", and "Application Activity".

[0052] Furthermore, the step of obtaining the target dataset and parsing the target dataset to obtain multiple behavior labels specifically includes:

[0053] The system receives a target dataset sent from the backend, wherein the target dataset includes target data corresponding to multiple target objects; it performs a parsing operation on the target data corresponding to all the target objects to obtain the behavior label corresponding to each target data.

[0054] Specifically, the system first receives the target dataset sent by technical personnel on the backend. The target dataset includes target data (equivalent to historical behavior data) of multiple target objects (equivalent to users). Then, the target dataset is parsed, which means parsing the target data corresponding to all target objects in the target dataset to obtain the behavior label corresponding to each target data. The behavior label is set by the technical personnel after preprocessing the target dataset to classify each target data.

[0055] Step S20: Obtain preset attribute rules, and obtain multiple first-level attributes corresponding to each behavior tag according to the preset attribute rules.

[0056] Specifically, in a preferred embodiment of the present invention, preset attribute rules set by technicians in the background are obtained, and then multiple first-level attributes corresponding to each behavior tag are obtained according to the preset attribute rules, such as... Figure 2 As shown, Figure 2 The boxes (drop-down list or input box) following each behavior label are the first-level attributes corresponding to the behavior label. For example, "Education" and "Subject Tutoring" are listed after "Comprehensive Broadcast Behavior" (it should be noted that "Education" and "Subject Tutoring" are only for display purposes, and there are actually many other optional options).

[0057] Furthermore, the step of obtaining preset attribute rules, and obtaining multiple first-level attributes corresponding to each behavior tag according to the preset attribute rules, specifically includes:

[0058] Receive preset attribute rules sent by the backend, wherein the preset attribute rules are set by the backend according to each behavior tag; obtain multiple first-level attributes corresponding to each behavior tag in the preset attribute rules.

[0059] Specifically, technicians can set corresponding preset attribute rules for each behavior tag in the backend. The preset attribute rules refer to the boxes that will be directly displayed after the behavior tag (in fact, whether the box is a drop-down box or an input box is determined by subsequent rendering operations based on associated attributes. In the preferred embodiment of the present invention, all boxes are drop-down boxes by default). Technicians can set corresponding preset attribute rules for each behavior tag according to actual needs.

[0060] After obtaining the preset attribute rules, the preset attribute rules are analyzed to obtain the first-level attribute corresponding to each behavior tag. That is, when the user selects the corresponding behavior tag, it will be directly displayed on the self-selection panel.

[0061] Step S30: Obtain multiple options corresponding to each of the first-level attributes based on the target dataset, and obtain the associated attributes corresponding to each option.

[0062] Specifically, in a preferred embodiment of the present invention, multiple options (i.e., drop-down list options) corresponding to each primary attribute are obtained based on the target dataset, and each option is judged to obtain the associated attribute corresponding to each option.

[0063] Further, the step of obtaining multiple options corresponding to each of the first-level attributes based on the target dataset, and obtaining the associated attributes corresponding to each option, specifically includes:

[0064] The target data corresponding to each target object in the target dataset is parsed to obtain multiple option data corresponding to each primary attribute; the multiple option data corresponding to each primary attribute are categorized to obtain multiple options corresponding to each primary attribute; a judgment operation is performed on each option: if the option includes a correlation field, the correlation attribute of the corresponding option is determined to have a correlation relationship, and all corresponding options are treated as dynamic options; if the option does not have a correlation field, the correlation attribute of the corresponding option is determined to have no correlation relationship, and all corresponding options are treated as static options; after completing the judgment operation on all options, the correlation attribute corresponding to each option is obtained.

[0065] Specifically, the process begins by parsing the target data for each target object in the target dataset. Multiple option data corresponding to each primary attribute are extracted from all target data. For example, the "page active" behavior tag has a primary attribute of "carousel channel." By parsing all target data, the option data corresponding to "carousel channel" is obtained as "channel 1," "channel 2," "channel 3," etc. Then, all option data corresponding to each primary attribute are categorized to obtain multiple options corresponding to each attribute. For instance, after parsing, the option data corresponding to "carousel channel" is "channel 1," "channel 2," and "channel 3." However, these three option data appear with different frequencies, resulting in multiple "channel 1," multiple "channel 2," and multiple "channel 3" options. Therefore, a categorization operation, or deduplication, is needed to remove duplicate option data, keeping only one of each. The final result is that the options corresponding to "carousel channel" are "channel 1," "channel 2," and "channel 3."

[0066] Then, each option is evaluated. The target dataset is pre-processed by backend engineers, so they first set the associated fields for data with parent-child hierarchical relationships. For example, the first-level attribute "Business Category" of the behavior tag "Start Broadcast Behavior" includes options such as "Education Services" and "Leisure and Entertainment." The option "Leisure and Entertainment" actually corresponds to dynamic lower-level attributes such as "Music Programs," "Game Programs," and "Variety Shows." Therefore, the engineers will use "Music Programs," "Game Programs," and "Variety Shows" as associated fields for the option "Leisure and Entertainment" and as built-in attributes of the option. Thus, it is determined that the option contains... If a field indicates a related option, it means the options include further options for selection. Therefore, options with related fields are considered to have a related relationship (and are treated as dynamic options), while options without related fields are considered to have no related relationship (and are treated as static options). A dynamic option means that if the user selects it, a corresponding dropdown list will be dynamically generated for the user to choose from. A static option means that if the user selects it, the corresponding option data will be returned directly as the final selection result. After evaluating all options, the related attributes for each option will be obtained.

[0067] Step S40: Perform rendering operations based on the preset rendering components and all the associated attributes to generate the autonomous user selection panel.

[0068] Specifically, the preset rendering component includes a dynamic recursive component and a flat component. The dynamic recursive component is used to dynamically and recursively generate multiple next-level dropdown boxes for options with related relationships, and the flat component is used to construct dropdown boxes for first-level attributes.

[0069] In a preferred embodiment of the present invention, rendering operations are performed based on dynamic recursive components and flattened components to generate the final autonomous human-circling panel.

[0070] Furthermore, each of the associated fields includes a dynamic subordinate attribute of the corresponding option;

[0071] The step of performing rendering operations based on preset rendering components and all associated attributes to generate an autonomous user selection panel specifically includes:

[0072] Based on the flattened components, static dropdown components are generated for each primary attribute, and multiple options corresponding to each primary attribute are filled into the corresponding static dropdown components to complete the construction of each static dropdown component; based on the dynamic recursive components, dynamic dropdown components are generated for each dynamic option; dynamic sub-attributes corresponding to each dynamic option are filled into the corresponding dynamic dropdown components to complete the construction of each dynamic dropdown component; an autonomous user selection panel is generated based on all the static dropdown components and all the dynamic dropdown components.

[0073] Specifically, each associated field includes dynamic sub-attributes for the corresponding option, which are multiple sub-data for the option.

[0074] First, static dropdown components are generated for each first-level attribute based on the flat components. Since each first-level attribute is independent, the flat components will generate a corresponding dropdown box (static dropdown component) for each first-level attribute and fill all the options (including static and dynamic options) for each first-level attribute into the corresponding dropdown box, thus completing the construction of all static dropdown components.

[0075] Based on the dynamic recursive component, a corresponding dynamic dropdown component is generated for each dynamic option (that is, the dynamic dropdown component will only be displayed after the user selects this dynamic option; the dynamic dropdown component is actually a dropdown box). Then, the dynamic sub-attributes corresponding to each dynamic option are filled into the corresponding dynamic dropdown component. In fact, the dynamic sub-attributes are used as options in the dynamic dropdown component. After the automatic filling is completed, the construction of the dynamic dropdown component is completed.

[0076] After all static and dynamic dropdown components are built, a self-selection panel is generated based on them. It should be noted that during the generation of the self-selection panel, the positions of the static and dynamic dropdown components can be preset by technical personnel or automatically arranged according to preset automatic generation rules.

[0077] Furthermore, the step of filling the corresponding dynamic dropdown component with the dynamic sub-attributes corresponding to each dynamic option further includes:

[0078] For each of the dynamic lower-level attributes, a judgment operation is performed to obtain the associated attributes of each dynamic lower-level attribute. If the associated attributes of the dynamic lower-level attribute have an association relationship, the dynamic association field corresponding to the corresponding dynamic lower-level attribute is obtained. Second-level dynamic drop-down components are generated according to each of the dynamic association fields, and each dynamic association field is filled into the corresponding second-level dynamic drop-down component to complete the construction of the second-level dynamic drop-down component. For each second-level dynamic attribute in the dynamic association field, a judgment operation is performed to obtain the associated attributes of each of the second-level dynamic attributes. If the associated attributes of the second-level dynamic attribute have an association relationship, the second-level dynamic association field corresponding to the corresponding second-level dynamic attribute is obtained, and a third-level dynamic drop-down component is generated according to the second-level dynamic association field. The second-level dynamic association fields in the third-level dynamic drop-down component are judged, ..., until the association attributes of all options in the finally generated dynamic drop-down component have no association relationship.

[0079] Specifically, the dynamic recursive component actually recursively checks all options in each static dropdown component and each dynamic dropdown component to determine if there are any related attributes for the options. If so, it continues to render the corresponding dynamic dropdown component.

[0080] First, the dynamic sub-attributes (i.e., the options in the dynamic dropdown component) in each dynamic dropdown component are evaluated. The specific evaluation process is the same as described above and will not be repeated here. After the evaluation is completed, the associated attributes corresponding to each dynamic sub-attribute are obtained. If the associated attributes of a certain dynamic sub-attribute have a relationship, then the dynamic associated field corresponding to this dynamic sub-attribute is obtained (the dynamic associated field includes the sub-data corresponding to this dynamic sub-attribute, i.e., the second-level dynamic attribute). Then, the second-level dynamic dropdown component corresponding to this dynamic sub-attribute is generated based on the dynamic associated field, and the dynamic associated field is used as the option of the second-level dynamic dropdown component. The dynamic associated field is populated into the second-level dynamic dropdown component, thereby completing the construction of the second-level dynamic dropdown component.

[0081] Then, the dynamic attributes of each second-level dynamic attribute in the dynamic association field of the second-level dynamic dropdown component are judged. If the association attribute of a certain second-level dynamic attribute has an association relationship, then the second-level dynamic association field corresponding to this second-level dynamic attribute is obtained, and the corresponding third-level dynamic dropdown component is generated based on the second-level dynamic association field. Then the second-level dynamic association field in the third-level dynamic dropdown component is judged, and so on, until the association attribute of the options of each dynamic dropdown component (including second-level dynamic dropdown components, third-level dynamic dropdown components, etc.) has no association relationship, and the dynamic rendering is completed.

[0082] For example, the primary attribute "Business Category" of the behavior tag "Start Broadcast Behavior" includes options such as "Education Services" and "Leisure and Entertainment." Therefore, the static dropdown component of the primary attribute "Business Category" will have options such as "Education Services" and "Leisure and Entertainment." "Leisure and Entertainment" actually corresponds to dynamic sub-attributes such as "Music Programs," "Game Programs," and "Variety Shows." This means that the related attributes of "Leisure and Entertainment" have a relationship, so a dynamic dropdown component corresponding to "Leisure and Entertainment" will be generated, with options such as "Music Programs," "Game Programs," and "Variety Shows." The related attributes of "Music Programs"... If there is a relationship between the two programs, and the corresponding dynamic association fields are "Program A", "Program B", and "Program C", then a second-level dynamic dropdown component will be generated based on the dynamic association fields. The options of the second-level dynamic dropdown component are "Program A", "Program B", and "Program C" respectively. Then, a judgment operation is performed on "Program A", "Program B", and "Program C". If it is determined that the association attributes of "Program A", "Program B", and "Program C" are not related, then the dynamic rendering is completed. The rendering process of the second-level dynamic dropdown components for "Game Programs" and "Variety Shows" is the same. In the end, a dynamic dropdown component will be generated for each option whose association attribute is related.

[0083] Furthermore, such as Figure 3 As shown, based on the above-described autonomous user-selection panel generation method, the present invention also provides an autonomous user-selection panel generation system, wherein the autonomous user-selection panel generation system includes:

[0084] The behavior label acquisition module 51 is used to acquire a target dataset and perform parsing operations on the target dataset to obtain multiple behavior labels.

[0085] The primary attribute acquisition module 52 is used to acquire preset attribute rules and obtain multiple primary attributes corresponding to each behavior tag according to the preset attribute rules.

[0086] The associated attribute acquisition module 53 is used to obtain multiple options corresponding to each of the first-level attributes based on the target dataset, and to obtain the associated attribute corresponding to each option;

[0087] The user grouping panel generation module 54 is used to perform rendering operations based on preset rendering components and all the associated attributes to generate an autonomous user grouping panel.

[0088] Furthermore, such as Figure 4 As shown, based on the above-described autonomous human-circling panel generation method and system, the present invention also provides a terminal, which includes a processor 10, a memory 20, and a display 30. Figure 4Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0089] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores an autonomous user-selection panel generation program 40, which can be executed by the processor 10 to implement the autonomous user-selection panel generation method of this application.

[0090] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the autonomous human-circling panel generation method.

[0091] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface. The components 10-30 of the terminal communicate with each other via a system bus.

[0092] In one embodiment, when the processor 10 executes the autonomous human-circling panel generation program 40 in the memory 20, the following steps are performed:

[0093] Obtain the target dataset, and perform parsing operations on the target dataset to obtain multiple behavior labels;

[0094] Obtain preset attribute rules, and obtain multiple first-level attributes corresponding to each behavior tag according to the preset attribute rules;

[0095] Based on the target dataset, obtain multiple options corresponding to each of the first-level attributes, and obtain the associated attributes corresponding to each option;

[0096] Rendering operations are performed based on preset rendering components and all associated attributes to generate an autonomous user selection panel.

[0097] Optionally, the autonomous user panel generation method, wherein obtaining the target dataset and parsing the target dataset to obtain multiple behavior labels specifically includes:

[0098] Receive the target dataset sent by the backend, wherein the target dataset includes target data corresponding to multiple target objects;

[0099] The target data corresponding to all the target objects is parsed to obtain the behavior tag corresponding to each target data.

[0100] Optionally, in the self-organized user grouping panel generation method, the step of obtaining preset attribute rules and acquiring multiple first-level attributes corresponding to each behavior tag according to the preset attribute rules specifically includes:

[0101] Receive preset attribute rules sent by the backend, wherein the preset attribute rules are set by the backend according to each of the behavior tags;

[0102] Obtain multiple first-level attributes corresponding to each behavior tag in the preset attribute rules.

[0103] Optionally, the autonomous user panel generation method, wherein obtaining multiple options corresponding to each primary attribute based on the target dataset and acquiring the associated attributes corresponding to each option specifically includes:

[0104] The target data corresponding to each target object in the target dataset is parsed to obtain multiple option data corresponding to each first-level attribute;

[0105] The multiple option data corresponding to each of the first-level attributes are classified to obtain multiple options corresponding to each of the first-level attributes;

[0106] For each option, a judgment operation is performed. If the option includes an association field, the association attribute of the corresponding option is determined to have an association relationship, and all corresponding options are treated as dynamic options.

[0107] If there is no associated field in the option, the associated attribute of the corresponding option will be determined to have no associated relationship, and all corresponding options will be treated as static options.

[0108] After completing the judgment operation for all the options, the associated attributes corresponding to each option are obtained.

[0109] Optionally, in the autonomous human-circle panel generation method, the preset rendering component includes a dynamic recursive component and a flattening component.

[0110] Optionally, in the autonomous user selection panel generation method, each of the associated fields includes a dynamic sub-attribute of the corresponding option;

[0111] The step of performing rendering operations based on preset rendering components and all associated attributes to generate an autonomous user selection panel specifically includes:

[0112] Based on the flattened components, static dropdown components are generated for each of the first-level attributes, and multiple options corresponding to each of the first-level attributes are filled into the corresponding static dropdown components to complete the construction of each static dropdown component;

[0113] Based on the dynamic recursive component, a dynamic dropdown component is generated for each of the dynamic options;

[0114] The dynamic sub-attributes corresponding to each dynamic option are populated into the corresponding dynamic dropdown component to complete the construction of each dynamic dropdown component;

[0115] Generate an autonomous grouping panel based on all the static dropdown components and all the dynamic dropdown components.

[0116] Optionally, the self-selection panel generation method further includes, after filling the dynamic sub-attributes corresponding to each dynamic option into the corresponding dynamic dropdown component:

[0117] Perform a judgment operation on each of the dynamic lower-level attributes to obtain the associated attributes of each of the dynamic lower-level attributes;

[0118] If the associated attribute of the dynamic sub-attribute has an association relationship, then retrieve the dynamic associated field corresponding to the dynamic sub-attribute.

[0119] A secondary dynamic dropdown component is generated based on each of the aforementioned dynamic association fields, and each of the aforementioned dynamic association fields is filled into the corresponding secondary dynamic dropdown component to complete the construction of the secondary dynamic dropdown component;

[0120] A judgment operation is performed on each secondary dynamic attribute in the dynamic association field to obtain the association attribute of each secondary dynamic attribute. If the association attribute of the secondary dynamic attribute has an association relationship, the secondary dynamic association field corresponding to the secondary dynamic attribute is obtained, and a tertiary dynamic drop-down component is generated based on the secondary dynamic association field.

[0121] The second-level dynamic association fields in the three-level dynamic dropdown component are judged, ... until all options in the final generated dynamic dropdown component have no association relationship.

[0122] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an autonomous human-circling panel generation program, which, when executed by a processor, implements the steps of the autonomous human-circling panel generation method as described above.

[0123] In summary, this invention provides a method, system, terminal, and storage medium for generating an autonomous user identification panel. The method includes: acquiring a target dataset; parsing the target dataset to obtain multiple behavior labels; acquiring preset attribute rules; obtaining multiple primary attributes corresponding to each behavior label according to the preset attribute rules; obtaining multiple options corresponding to each primary attribute based on the target dataset, and acquiring associated attributes corresponding to each option; and performing a rendering operation based on a preset rendering component and all the associated attributes to generate an autonomous user identification panel. This invention can automatically generate an autonomous user identification panel based on a target dataset, improving the efficiency of creating autonomous user identification panels, reducing maintenance costs, and enhancing the user experience.

[0124] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0125] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0126] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for generating an autonomous user-selection panel, characterized in that, The method for generating the autonomous user grouping panel includes: Obtain the target dataset, and perform parsing operations on the target dataset to obtain multiple behavior labels; Obtain preset attribute rules, and obtain multiple first-level attributes corresponding to each behavior tag according to the preset attribute rules; Based on the target dataset, obtain multiple options corresponding to each of the first-level attributes, and obtain the associated attributes corresponding to each option; The step of obtaining multiple options corresponding to each primary attribute based on the target dataset, and obtaining the associated attributes corresponding to each option, specifically includes: The target data corresponding to each target object in the target dataset is parsed to obtain multiple option data corresponding to each first-level attribute; The multiple option data corresponding to each of the first-level attributes are classified to obtain multiple options corresponding to each of the first-level attributes; For each option, a judgment operation is performed. If the option includes an association field, the association attribute of the corresponding option is determined to have an association relationship, and all corresponding options are treated as dynamic options. If there is no associated field in the option, the associated attribute of the corresponding option will be determined to have no associated relationship, and all corresponding options will be treated as static options. After completing the judgment operation for all the options, the associated attributes corresponding to each option are obtained; Rendering operations are performed based on preset rendering components and all the associated attributes to generate an autonomous user-selection panel. The preset rendering component includes a dynamic recursive component and a flattened component. The dynamic recursive component is used to dynamically and recursively generate multiple lower-level dropdowns for options with related relationships. The flattened component is used to construct dropdowns based on first-level attributes. Each related field includes the dynamic lower-level attributes of the corresponding option. The step of performing rendering operations based on preset rendering components and all associated attributes to generate an autonomous user selection panel specifically includes: Based on the flattened components, static dropdown components are generated for each of the first-level attributes, and multiple options corresponding to each of the first-level attributes are filled into the corresponding static dropdown components to complete the construction of each static dropdown component; Based on the dynamic recursive component, a dynamic dropdown component is generated for each of the dynamic options; The dynamic sub-attributes corresponding to each dynamic option are populated into the corresponding dynamic dropdown component to complete the construction of each dynamic dropdown component; Generate an autonomous grouping panel based on all the static dropdown components and all the dynamic dropdown components.

2. The autonomous human-circle panel generation method according to claim 1, characterized in that, The process of obtaining the target dataset and parsing it to obtain multiple behavior labels specifically includes: Receive the target dataset sent by the backend, wherein the target dataset includes target data corresponding to multiple target objects; The target data corresponding to all the target objects is parsed to obtain the behavior tag corresponding to each target data.

3. The autonomous human-circle panel generation method according to claim 1, characterized in that, The step of obtaining preset attribute rules, specifically including obtaining multiple first-level attributes corresponding to each behavior tag according to the preset attribute rules, includes: Receive preset attribute rules sent by the backend, wherein the preset attribute rules are set by the backend according to each of the behavior tags; Obtain multiple first-level attributes corresponding to each behavior tag in the preset attribute rules.

4. The autonomous human-circle panel generation method according to claim 1, characterized in that, The step of filling the dynamic sub-attributes corresponding to each dynamic option into the corresponding dynamic dropdown component further includes: Perform a judgment operation on each of the dynamic lower-level attributes to obtain the associated attributes of each of the dynamic lower-level attributes; If the associated attribute of the dynamic sub-attribute has an association relationship, then retrieve the dynamic associated field corresponding to the dynamic sub-attribute. A secondary dynamic dropdown component is generated based on each of the aforementioned dynamic association fields, and each of the aforementioned dynamic association fields is filled into the corresponding secondary dynamic dropdown component to complete the construction of the secondary dynamic dropdown component; A judgment operation is performed on each second-level dynamic attribute in the dynamic association field to obtain the association attribute of each second-level dynamic attribute. If the association attribute of the second-level dynamic attribute has an association relationship, the second-level dynamic association field corresponding to the second-level dynamic attribute is obtained, and a third-level dynamic drop-down component is generated based on the second-level dynamic association field. The second-level dynamic association fields in the three-level dynamic dropdown component are judged, ... until all options in the final generated dynamic dropdown component have no association relationship.

5. An autonomous human-circle panel generation system, characterized in that, The autonomous human-identification panel generation system is used to implement the autonomous human-identification panel generation method according to any one of claims 1-4, and the autonomous human-identification panel generation system includes: The behavior label acquisition module is used to acquire the target dataset, parse the target dataset, and obtain multiple behavior labels. The first-level attribute acquisition module is used to acquire preset attribute rules and obtain multiple first-level attributes corresponding to each behavior tag according to the preset attribute rules. The associated attribute acquisition module is used to obtain multiple options corresponding to each of the first-level attributes based on the target dataset, and to obtain the associated attribute corresponding to each option; The "Circle Panel Generation Module" is used to perform rendering operations based on preset rendering components and all the associated attributes to generate an autonomous circle panel.

6. A terminal, characterized in that, The terminal includes: a memory, a processor, and an autonomous human-circling panel generation program stored in the memory and executable on the processor. When the autonomous human-circling panel generation program is executed by the processor, it implements the steps of the autonomous human-circling panel generation method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an autonomous human-identifying panel generation program, which, when executed by a processor, implements the steps of the autonomous human-identifying panel generation method as described in any one of claims 1-4.

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