CN113868481A - Component acquisition method and device, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the application discloses a component obtaining method, a component obtaining device, electronic equipment and a storage medium, wherein at least part of components selected by a target user are obtained; determining the association components of at least part of the components at least based on the at least part of the components, and generating an association component list; a list of associated components is displayed for component selection by the target user. Based on the method and the device, in the process of building the interactive form by the user, the selected related components of at least part of the components can be recommended to the user according to the selected components of at least part of the components, the range of the components selected by the user is reduced, and therefore the difficulty of the user in obtaining the required components is reduced.
Description
Technical Field
The present application relates to the field of software technologies, and in particular, to a component acquisition method and apparatus, an electronic device, and a storage medium.
Background
The low-code platform can enable a user (namely a software developer) to complete the building of the interactive form of the application program by dragging, pulling and dragging the component, and the technical requirements on the user are reduced. However, in the process of developing software by using a low-code platform, a user needs to build an interactive form by acquiring a component resource. At present, a user mainly searches according to search keywords/words to obtain required components, and then drags the obtained components to a design area to build a form. This approach increases the difficulty for a user to retrieve components when the number of components in a low-code platform is large in scale.
Therefore, how to reduce the difficulty of the user to acquire the components becomes a technical problem to be solved urgently.
Disclosure of Invention
The application aims to provide a component obtaining method and device, an electronic device and a storage medium, and the method comprises the following technical scheme:
a component acquisition method, the method comprising:
obtaining at least part of the components selected by the target user;
determining an association component of the at least part of components based on at least the at least part of components, and generating an association component list;
displaying the associated component list for component selection by the target user.
The above method, preferably, further comprises: obtaining attribute characteristics of the target user;
the determining, based on at least the at least some components, associated components of the at least some components includes:
and determining an association component of the at least part of components based on the attribute characteristics of the target user and the at least part of components.
The above method, preferably, the determining, based on the attribute characteristics of the target user and the at least some components, an associated component of the at least some components includes:
processing the attribute characteristics of the target user and the at least part of components through a component prediction engine to obtain associated components of the at least part of components;
the component prediction engine is obtained based on the attribute characteristics and historical behavior data of each user of the low-code platform; the historical behavior data of each user at least comprises the selected components when the user historically builds the interactive form and the sequence of the selected components.
The above method, preferably, the determining, based on the attribute characteristics of the target user and the at least some components, an associated component of the at least some components includes:
obtaining a target component prediction engine associated with the attribute characteristics of the target user;
processing the attribute characteristics of the target user and the at least part of components through the target component prediction engine to obtain associated components of the at least part of components;
the target component prediction engine is obtained based on the attribute characteristics and historical behavior data of each user with the attribute characteristics or similar attribute characteristics of the low-code platform; the historical behavior data of each user at least comprises the selected components when the user historically builds the interactive form and the sequence of the selected components.
In the above method, preferably, the target component prediction engine is obtained by:
clustering attribute characteristics of each user of the low-code platform to obtain a first clustering result;
classifying historical behavior data of each user of the low-code platform according to each clustering category in the first clustering result;
corresponding to each cluster category, training the component prediction engine by using the historical behavior data of the cluster category obtained by classification to obtain a component prediction engine associated with the cluster category;
and the component prediction engine associated with the cluster category to which the attribute features of the target user belong is the target component prediction engine.
The above method, preferably, further comprises:
when the target user does not select the components, determining at least one history selection component according to the attribute characteristics of the target user, and generating a history selection component list; the at least one historical selection component is at least one component with the highest selection frequency in the first selected components of the historical behavior data of the users with the attribute characteristics or similar attribute characteristics;
and displaying the history selection component list so that the target user can select a first component for building a form.
The above method, preferably, further comprises:
acquiring the components in the association component list from a database;
and caching the acquired component.
A component acquisition apparatus, comprising:
an obtaining module, configured to obtain at least part of the components that have been selected by a target user;
a determining module, configured to determine, based on at least the at least some components, an associated component of the at least some components, and generate an associated component list;
and the display module is used for displaying the association component list so as to be used for the target user to select the components.
An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the component acquisition method as described in any one of the above.
A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, carries out the steps of the component acquisition method as claimed in any one of the preceding claims.
According to the scheme, the component obtaining method, the component obtaining device, the electronic equipment and the storage medium provided by the application obtain at least part of components selected by a target user; determining the association components of at least part of the components at least based on the at least part of the components, and generating an association component list; a list of associated components is displayed for component selection by the target user. Based on the method and the device, in the process of building the interactive form by the user, the selected related components of at least part of the components can be recommended to the user according to the selected components of at least part of the components, the range of the components selected by the user is reduced, and therefore the difficulty of the user in obtaining the required components is reduced.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of an implementation of a component acquisition method according to an embodiment of the present disclosure;
FIG. 2 is a flow chart illustrating an implementation of a component for determining an association of at least some of the components based on the attribute characteristics of the target user and at least some of the components according to an embodiment of the present disclosure;
fig. 3 is a flowchart of an implementation of classifying historical behavior data of users of a low-code platform according to each clustering category in a first clustering result according to the embodiment of the present application;
fig. 4 is a schematic structural diagram of an assembly obtaining apparatus according to an embodiment of the present application;
fig. 5 is an exemplary diagram of a hardware structure block diagram of an electronic device according to an embodiment of the present application.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in other sequences than described or illustrated herein.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without inventive step, are within the scope of the present disclosure.
Currently, when a user (i.e., a software developer) builds an interactive form by using a low-code platform, the user mainly searches according to search keywords to obtain a required component, and then drags the obtained component to a design area to build the form (a front-end interactive interface of an application, which may include, for example, an input box, a drop-down box, a list supporting search, a list supporting page turning, and the like). This method based on search keywords/words relies on the user's mastery of the index keywords/words of the component resource, and if the user does not know the keywords/words of the component resource, the component resource cannot be found accurately. Moreover, as the number of component resources increases, the difficulty of the user in mastering the index keywords/words of the component resources also increases (because the number of index keywords/words to be memorized increases), and the difficulty of the user in acquiring the required components further increases.
Some solutions provide browsing and searching functions of component resources by classifying component resources of a low-code platform, but the method relies on the knowledge of a user about the class to which the component of the resource belongs, and if the class of the component of the resource is not clear or the user does not know about the class, it is difficult to quickly locate the required component resource.
The scheme is provided for reducing the difficulty of obtaining the required components by the user.
Referring to fig. 1, a flowchart for implementing the component obtaining method according to the embodiment of the present application may include:
step S101: at least some of the components that have been selected by the target user are obtained.
The target user may refer to any user that logs on to the low code platform. After a user logs in the low-code platform, the components can be selected according to development requirements to build an interactive form.
In this embodiment of the present application, the component that the target user has selected may be a component that the target user has retrieved through a retrieval keyword/word on a low-code platform, or may be a component that is selected based on the component acquisition method provided in this embodiment of the present application, or a component that is partially retrieved through a retrieval keyword/word on a low-code platform and is partially selected based on the component acquisition method provided in this embodiment of the present application.
The at least part of the components selected by the target user may refer to all the components selected by the target user after logging in the low-code platform, or may refer to the last selected part of all the components selected by the target user after logging in the low-code platform (for example, the last selected one or two components, although this is only an example, and in particular implementation, the last selected part of the components may also be other numbers, for example, the last selected 3 components, the last selected 4 components, and so on).
Step S102: and determining the association components of the at least part of components at least based on the at least part of components, and generating an association component list.
In the embodiment of the present application, the association component of the at least part of the components may be determined based on only the at least part of the components, or the association component of the at least part of the components may be determined based on the at least part of the components and other information.
The related component of at least some of the components refers to a component that is likely to be present next in the form design area (i.e., a component that is likely to be selected next by the target user, or a component that is likely to be selected next by the target user) after the at least some of the components are present in the form design area (selected by the target user).
Step S103: a list of associated components is displayed for component selection by the target user.
And displaying the association component list, namely recommending the components in the association component list to a target user, wherein the user can select the components in the association component list to build a form.
The association component list can be displayed through an interactive window, and a user can drag components of the content of the interactive window to the form design area to build a form in the form design area.
The interactive window can be displayed at a certain position around the form design area; or,
the interactive window can be displayed above the form design area in a suspension mode, and the target user can drag the interactive window to change the display position of the interactive window.
According to the component obtaining method provided by the embodiment of the application, in the process of building the interactive form by the user, the related components of at least part of the selected components can be recommended to the user according to at least part of the selected components, the range of the components selected by the user is reduced, and therefore the difficulty of obtaining the required components by the user is reduced.
In an optional embodiment, attribute characteristics of the target user may be further obtained, and based on at least the at least some components, one implementation manner of determining the association component of the at least some components may be:
and determining the association component of the at least part of components based on the attribute characteristics of the target user and the at least part of components.
The attribute characteristics of the user may include professional characteristics of the user, such as a professional field to which the user belongs. Optionally, the attribute characteristics of the user may be extracted from the identity information registered by the target user after the target user logs in the low-code platform, for example, the professional field to which the user belongs may be a work post (software engineer) and a work property (e.g., research and development, operation and maintenance, etc.) in which the user is engaged; alternatively, the professional field to which the user belongs may be an application field (e.g., a financial field, a legal field, etc.) of the developed software registered by the user.
In the embodiment of the application, the attribute characteristics of the low-code platform user and the historical behavior data of the low-code platform user are combined to determine the associated components of at least part of the components selected by the target user, so that the determined associated components conform to the attribute characteristics of the user, and the difficulty of the user in acquiring the required components is further reduced.
In an optional embodiment, one implementation of the determining, based on the attribute characteristics of the target user and the at least some components, an association component of the at least some components may include:
processing the attribute characteristics of the target user and the at least part of components through a component prediction engine to obtain associated components of the at least part of components; wherein,
the component prediction engine is obtained based on the attribute characteristics and historical behavior data of each user of the low-code platform; the historical behavior data of each user at least comprises selected components when the user historically builds the interactive form and the sequence of the selected components.
That is to say, in the embodiment of the application, for each user of the low-code platform, each time the user constructs an interactive form, the code interactive platform may store the component selected by the user for constructing the interactive form and the selection sequence of the component as one historical behavior data of the user.
After obtaining a plurality of historical behavior data of a plurality of users, for each user, attribute characteristics of the user and partial data (the partial data is at least one component in the historical behavior data, and a selection order of the at least one component in the historical behavior data, when the partial data is at least two components in the historical behavior data, the at least two components are at least two components with continuous selection orders) intercepted from the historical behavior data of the user can be used as sample data, and the label of the sample data is the component next to the intercepted at least one component in the historical behavior data of the user. After a sample data set is obtained, a component prediction engine is trained by using the sample data set.
When the component prediction engine is trained, the input of the component prediction engine is sample data (namely attribute characteristics of a user and at least one component and the selection sequence of the at least one component), the output of the component prediction engine is the input prediction result of the next component of the at least one component, the prediction result of the next component approaches to the label of the input sample data to serve as a target, the parameters of the component prediction engine are updated until the training end condition is met, and the trained component prediction engine is obtained. Optionally, the algorithm used by the training component prediction engine may be a regression algorithm (such as a linear regression algorithm), or may be other algorithms, and the present application is not limited in particular.
In an alternative embodiment, an implementation flowchart of the determining, based on the attribute characteristics of the target user and the at least part of the components, an association component of the at least part of the components is shown in fig. 2, and may include:
step S201: and obtaining a target component prediction engine associated with the attribute characteristics of the target user.
In the embodiment of the present application, a plurality of component prediction engines are set according to the attribute characteristics of the user, the attribute characteristics associated with different component prediction engines are different, and the component prediction engines associated with different attribute characteristics may be the same (for example, the component prediction engines associated with similar attribute characteristics are the same), or may be different.
Step S202: and processing the attribute characteristics and at least part of components of the target user through a target component prediction engine to obtain the associated components of the at least part of components.
The target component prediction engine is obtained based on the attribute characteristics and historical behavior data of each user with the attribute characteristics or similar attribute characteristics of the target user of the low-code platform; the historical behavior data of each user at least comprises the selected components when the user historically builds the interactive form and the sequence of the selected components.
In the embodiment of the application, the target component prediction engine is obtained by learning historical behavior data related to attribute features of a target user. The historical behavior data related to the attribute features of the target user may include:
historical behavior data of users having the same attribute characteristics as those of the target user, and historical behavior data of users having attribute characteristics similar to those of the target user.
By setting the component prediction engines associated with different attribute characteristics, the associated components of at least part of the components can be predicted by using the target component prediction engine associated with the attribute characteristics of the target user, so that the predicted associated components are more consistent with the attribute characteristics of the target user, and the difficulty of selecting the components by the user is further reduced.
Optionally, the target component prediction engine may be trained as follows:
step 1, clustering attribute characteristics of each user of the low-code platform to obtain a first clustering result.
The purpose of clustering is to determine similar attribute features.
And 2, classifying the historical behavior data of each user of the low-code platform according to each clustering class in the first clustering result.
The historical behavior data of each user of the low-code platform can be classified according to each clustering category in the first clustering result by using a probability statistical method.
For example, the historical behavior data of each user of the low-code platform may be classified according to each cluster category in the first cluster result by using a bayesian classification algorithm. For each historical behavior data of each user, calculating the probability of each cluster class under the condition that the historical behavior data appears, and taking the cluster class with the highest probability under the condition that the historical behavior data appears as the classification class of the historical behavior data.
And 3, corresponding to each cluster category, training the component prediction engine by using the historical behavior data of the cluster category obtained in the classification (namely step 2) to obtain the component prediction engine associated with the cluster category.
The historical behavior data of the cluster category can be processed to obtain sample data. The specific processing procedure can refer to the foregoing procedure for obtaining sample data, and is not described in detail here.
The component prediction engine is then trained using the sample data, and the training process may refer to the foregoing embodiments, which are not described here.
And the component prediction engine associated with the cluster class to which the attribute characteristics of the target user belong is a target component prediction engine.
In an optional embodiment, in order to improve the effectiveness of the recommended components for the user and further reduce the difficulty of the user in obtaining the components, an implementation flowchart of classifying the historical behavior data of each user of the low-code platform according to each cluster category in the first cluster result is shown in fig. 3, and may include:
step S301: and clustering the historical behavior data of each user of the low-code platform to obtain a second clustering result.
The purpose of clustering the historical behavior data of each user of the low-code platform is to determine similar historical behavior data.
Step S302: extracting target historical behavior data according to the second clustering result; the target historical behavior data is historical behavior data under the clustering category of which the number of the historical behavior data is larger than a threshold value.
That is, the present application retains the use of more historical behavior data (i.e., target historical behavior data). And regarding historical behavior data in the clustering category of which the quantity of the historical behavior data is less than the threshold value, considering that the historical behavior data is less used, and if the historical behavior data cannot be learned, rejecting the historical behavior data.
Step S303: and classifying the target historical behavior data according to each clustering category in the first clustering result.
In an optional embodiment, one implementation of the determining, based on at least the at least some components, an association component of the at least some components may be:
processing the at least part of the components through a component prediction engine to obtain related components of the at least part of the components;
the component prediction engine is obtained based on historical behavior data of each user of the low-code platform; the historical behavior data of each user includes the selected components when the user historically builds the interactive form, and the order of the selected components.
The historical behavior data of each user of the low-code platform can be processed to obtain sample data. For example, for each user, a part of data intercepted from the historical behavior data of the user (the part of data is at least one component in the historical behavior data, and the selection order of the at least one component in the historical behavior data, and when the part of data is at least two components in the historical behavior data, the at least two components are at least two components whose selection orders are consecutive) may be used as sample data, and the label of the sample data is the component next to the intercepted at least one component in the historical behavior data of the user. After a sample data set is obtained, a component prediction engine is trained by using the sample data set.
When the component prediction engine is trained, the input of the component prediction engine is sample data (namely at least one component and the selection sequence of the at least one component), the output of the component prediction engine is the input prediction result of the next component of the at least one component, the prediction result of the next component approaches to the label of the input sample data to serve as a target, the parameters of the component prediction engine are updated until the training end condition is met, and the trained component prediction engine is obtained. Optionally, the algorithm used by the training component prediction engine may be a regression algorithm (such as a linear regression algorithm), or may be other algorithms, and the present application is not limited in particular.
In an optional embodiment, when a target user just logs in a low-code platform and does not select any component, at least one history selection component can be determined according to the attribute characteristics of the target user, and a history selection component list is generated; the at least one history selection component is at least one component with the highest selection frequency from the first selected components of the historical behavior data of the users with the attribute characteristics or similar attribute characteristics of the target user (namely, the attribute characteristics similar to the attribute characteristics of the target user).
A list of history selection components is displayed for the target user to select the first component for building the form.
That is, when the target user does not select a component, a statistical analysis may be performed on a first component in the historical behavior data of each user of the low-code platform having the attribute feature of the target user or the similar attribute feature (i.e., a component selected first in the historical behavior data of the user), and at least one component with the highest selection frequency may be determined, where the at least one component with the highest selection frequency may be at least one component with the top selection frequency ranking N, or may be a component with a selection frequency greater than a frequency threshold.
According to the embodiment of the application, under the condition that the user does not select the component, automatic recommendation of the component can be achieved, and the difficulty of selecting the component by the user is further reduced.
In an optional embodiment, after the association component list is generated, the components in the association component list may be further obtained from the database and cached, so that when the target user selects a component in the association component list, the component may be extracted from the cache instead of being obtained from the database, thereby improving the efficiency of selecting the component by the target user.
In an optional embodiment, after the target user determines to save the built form, the components selected by the target user and the sequence of the selected components can be saved as one-time historical behavior data of the target user, so that the component prediction engine can be optimized and updated later.
In an optional embodiment, if the generated association list does not have the components required by the target user, the target user may search for the required components based on the keywords/words, and based on this, the component obtaining method provided in the embodiment of the present application may further include:
obtaining a retrieval instruction, wherein the retrieval instruction carries keywords/words;
retrieving the components (marked as matching components) matched with the keywords/words in a component database; the specific matching process can refer to the existing scheme and is not detailed here.
The retrieved matching components are displayed for selection by the target user.
Corresponding to the method embodiment, an embodiment of the present application further provides an assembly obtaining apparatus, and a schematic structural diagram of the assembly obtaining apparatus provided in the embodiment of the present application is shown in fig. 4, and the apparatus may include:
an obtaining module 401, a determining module 402 and a display module 403; wherein,
the obtaining module 401 is configured to obtain at least part of the components that have been selected by the target user;
the determining module 402 is configured to determine an association component of the at least some components based on at least the at least some components, and generate an association component list;
the display module 403 is configured to display the associated component list for component selection by the target user.
According to the component obtaining device provided by the embodiment of the application, in the process of building the interactive form by the user, the related components of at least part of the selected components can be recommended to the user according to at least part of the selected components, the range of the components selected by the user is reduced, and therefore the difficulty of obtaining the required components by the user is reduced.
In an alternative embodiment, the obtaining module 401 may further be configured to: obtaining attribute characteristics of the target user;
the determining module 402 is configured to:
and determining an association component of the at least part of components based on the attribute characteristics of the target user and the at least part of components.
In an alternative embodiment, the determining module 402 is configured to:
processing the attribute characteristics of the target user and the at least part of components through a component prediction engine to obtain associated components of the at least part of components;
the component prediction engine is obtained based on the attribute characteristics and historical behavior data of each user of the low-code platform; the historical behavior data of each user at least comprises the selected components when the user historically builds the interactive form and the sequence of the selected components.
In an alternative embodiment, the determining module 402 is configured to:
obtaining a target component prediction engine associated with the attribute characteristics of the target user;
processing the attribute characteristics of the target user and the at least part of components through the target component prediction engine to obtain associated components of the at least part of components;
the target component prediction engine is obtained based on the attribute characteristics and historical behavior data of each user with the attribute characteristics or similar attribute characteristics of the low-code platform; the historical behavior data of each user at least comprises the selected components when the user historically builds the interactive form and the sequence of the selected components.
In an optional embodiment, further comprising: a training module to:
clustering attribute characteristics of each user of the low-code platform to obtain a first clustering result;
classifying historical behavior data of each user of the low-code platform according to each clustering category in the first clustering result;
corresponding to each cluster category, training the component prediction engine by using the historical behavior data of the cluster category obtained by classification to obtain a component prediction engine associated with the cluster category;
and the component prediction engine associated with the cluster category to which the attribute features of the target user belong is the target component prediction engine.
In an optional embodiment, when the training module classifies the historical behavior data of each user of the low-code platform according to each cluster category in the first cluster result, the training module is configured to:
clustering historical behavior data of each user of the low-code platform to obtain a second clustering result;
extracting target historical behavior data according to the second clustering result; the target historical behavior data is historical behavior data under the clustering category of which the number of the historical behavior data is greater than a threshold value;
and classifying the target historical behavior data according to each clustering category in the first clustering result.
In an alternative embodiment, the determining module 402 is configured to:
processing the at least part of the components through a component prediction engine to obtain associated components of the at least part of the components;
the component prediction engine is obtained based on historical behavior data of each user of the low-code platform; the historical behavior data of each user includes the selected components when the user historically builds the interactive form, and the order of the selected components.
In an alternative embodiment, the determining module 402 is further configured to:
when the target user does not select the components, determining at least one history selection component according to the attribute characteristics of the target user, and generating a history selection component list; the at least one historical selection component is at least one component with the highest selection frequency in the first selected components of the historical behavior data of the users with the attribute characteristics or similar attribute characteristics;
the reality module 403 is further configured to display the history selection component list for the target user to select a first component for building a form.
In an optional embodiment, further comprising:
the cache module is used for acquiring the components in the association component list from a database; and caching the acquired component.
In an optional embodiment, further comprising:
and the storage module is used for storing the components selected by the target user and the sequence of the selected components so as to optimally update the component prediction engine.
In an optional embodiment, further comprising:
the retrieval module is used for obtaining a retrieval instruction, and the retrieval instruction carries keywords/words;
the components that match the keyword/word (denoted as matching components) are retrieved in a component database.
The display module 403 is also used to display the retrieved matched components for selection by the target user.
Corresponding to the method embodiment, the application also provides electronic equipment, such as a terminal, a server and the like. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, big data and artificial intelligence platform. The terminal may be a mobile terminal such as a smart phone, a tablet computer, a notebook computer, or a desktop computer, but is not limited thereto. In some embodiments, the terminal or the server may be a node in a distributed system, wherein the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting a plurality of nodes through a network communication form. Nodes can form a Peer-To-Peer (P2P, Peer To Peer) network, and any type of computing device, such as a server, a terminal, and other electronic devices, can become a node in the blockchain system by joining the Peer-To-Peer network.
An exemplary diagram of a hardware structure block diagram of an electronic device provided in an embodiment of the present application is shown in fig. 5, and may include:
a processor 1, a communication interface 2, a memory 3 and a communication bus 4;
wherein, the processor 1, the communication interface 2 and the memory 3 complete the communication with each other through the communication bus 4;
optionally, the communication interface 2 may be an interface of a communication module, such as an interface of a GSM module;
the processor 1 may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present application.
The memory 3 may comprise a high-speed RAM memory and may also comprise a non-volatile memory, such as at least one disk memory.
The processor 1 is specifically configured to execute the computer program stored in the memory 3, so as to execute the following steps:
obtaining at least part of the components selected by the target user;
determining an association component of the at least part of components based on at least the at least part of components, and generating an association component list;
displaying the associated component list for component selection by the target user.
Alternatively, the detailed functions and extended functions of the computer program may be as described above.
Embodiments of the present application further provide a readable storage medium, where the storage medium may store a computer program adapted to be executed by a processor, where the computer program is configured to:
obtaining at least part of the components selected by the target user;
determining an association component of the at least part of components based on at least the at least part of components, and generating an association component list;
displaying the associated component list for component selection by the target user.
Alternatively, the detailed functions and extended functions of the computer program may be as described above.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
It should be understood that the technical problems can be solved by combining and combining the features of the embodiments from the claims.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A component acquisition method, the method comprising:
obtaining at least part of the components selected by the target user;
determining an association component of the at least part of components based on at least the at least part of components, and generating an association component list;
displaying the associated component list for component selection by the target user.
2. The method of claim 1, further comprising: obtaining attribute characteristics of the target user;
the determining, based on at least the at least some components, associated components of the at least some components includes:
and determining an association component of the at least part of components based on the attribute characteristics of the target user and the at least part of components.
3. The method of claim 2, the determining, based on the attribute characteristics of the target user and the at least some components, an associated component of the at least some components comprising:
processing the attribute characteristics of the target user and the at least part of components through a component prediction engine to obtain associated components of the at least part of components;
the component prediction engine is obtained based on the attribute characteristics and historical behavior data of each user of the low-code platform; the historical behavior data of each user at least comprises the selected components when the user historically builds the interactive form and the sequence of the selected components.
4. The method of claim 2, the determining, based on the attribute characteristics of the target user and the at least some components, an associated component of the at least some components comprising:
obtaining a target component prediction engine associated with the attribute characteristics of the target user;
processing the attribute characteristics of the target user and the at least part of components through the target component prediction engine to obtain associated components of the at least part of components;
the target component prediction engine is obtained based on the attribute characteristics and historical behavior data of each user with the attribute characteristics or similar attribute characteristics of the low-code platform; the historical behavior data of each user at least comprises the selected components when the user historically builds the interactive form and the sequence of the selected components.
5. The method of claim 4, the target component prediction engine derived by:
clustering attribute characteristics of each user of the low-code platform to obtain a first clustering result;
classifying historical behavior data of each user of the low-code platform according to each clustering category in the first clustering result;
corresponding to each cluster category, training the component prediction engine by using the historical behavior data of the cluster category obtained by classification to obtain a component prediction engine associated with the cluster category;
and the component prediction engine associated with the cluster category to which the attribute features of the target user belong is the target component prediction engine.
6. The method of any of claims 2-5, further comprising:
when the target user does not select the components, determining at least one history selection component according to the attribute characteristics of the target user, and generating a history selection component list; the at least one historical selection component is at least one component with the highest selection frequency in the first selected components of the historical behavior data of the users with the attribute characteristics or similar attribute characteristics;
and displaying the history selection component list so that the target user can select a first component for building a form.
7. The method of claim 1, further comprising:
acquiring the components in the association component list from a database;
and caching the acquired component.
8. A component acquisition apparatus, comprising:
an obtaining module, configured to obtain at least part of the components that have been selected by a target user;
a determining module, configured to determine, based on at least the at least some components, an associated component of the at least some components, and generate an associated component list;
and the display module is used for displaying the association component list so as to be used for the target user to select the components.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program for carrying out the steps of the component acquisition method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the component acquisition method according to any one of claims 1 to 7.
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