CN113868481A - Component acquisition method, device, electronic device and storage medium - Google Patents

Abstract

The embodiments of the present application disclose a component acquisition method, apparatus, electronic device, and storage medium, to acquire at least some components selected by a target user; based on at least the above at least some components, determine the associated components of at least some of the components, and generate a list of associated components; Displays a list of associated components for component selection by target users. Based on the present application, in the process of building an interactive form by a user, the associated components of at least some of the selected components can be recommended for the user according to at least some of the components selected by the user, which reduces the range of components selected by the user, thereby reducing the need for users to obtain Difficulty of components.

Description

Component acquisition method, device, electronic device and storage medium

technical field

The present application relates to the field of software technology, and more particularly, to a component acquisition method, apparatus, electronic device, and storage medium.

Background technique

The low-code platform enables users (ie, software developers) to complete the construction of interactive forms of applications by dragging, pulling, and dragging components, reducing the technical requirements for users. However, in the process of developing software using a low-code platform, users need to build interactive forms by acquiring resources such as components. At present, the user mainly searches according to the search keywords/words to obtain the required components, and then drags the obtained components to the design area to build the form. This approach increases the difficulty for users to retrieve components when the number of components in a low-code platform is large.

Therefore, how to reduce the difficulty for users to obtain components has become an urgent technical problem to be solved.

SUMMARY OF THE INVENTION

The purpose of this application is to provide a component acquisition method, device, electronic device and storage medium, including the following technical solutions:

A component acquisition method, the method includes:

Obtain at least some of the components that the target user has selected;

At least based on the at least some of the components, determine the associated components of the at least some of the components, and generate a list of associated components;

The list of associated components is displayed for component selection by the target user.

The above method, preferably, further comprises: obtaining the attribute characteristics of the target user;

The determining, based at least on the at least some of the components, an associated component of the at least some of the components includes:

Based on the attribute characteristics of the target user and the at least part of the components, an associated component of the at least part of the components is determined.

In the above method, preferably, the determining of the associated components of the at least part of the components based on the attribute characteristics of the target user and the at least part of the components includes:

Process the attribute feature of the target user and the at least some of the components by a component prediction engine to obtain the associated components of the at least some of the 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 includes the components selected when the user historically built the interactive form, and the order of the selected components.

In the above method, preferably, the determining of the associated components of the at least part of the components based on the attribute characteristics of the target user and the at least part of the components includes:

Obtain the target component prediction engine associated with the attribute feature of the target user;

Process the attribute feature of the target user and the at least some components by the target component prediction engine, and obtain the associated components of the at least some 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 includes the historical behavior data selected by the user when building an interactive form. components, and the order in which the components are selected.

In the above method, preferably, the target component prediction engine is obtained in the following manner:

Clustering the attribute features of each user of the low-code platform to obtain a first clustering result;

classifying the 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, use the historical behavior data of the cluster category obtained by classification to train the component prediction engine, and obtain the component prediction engine associated with the cluster category;

The component prediction engine associated with the cluster category to which the attribute feature of the target user belongs is the target component prediction engine.

The above method, preferably, also includes:

When the target user does not select a component, at least one historical selection component is determined according to the attribute characteristics of the target user, and a list of historical selection components is generated; the at least one historical selection component is each of the attribute characteristics or similar attribute characteristics. Among the first selected components of the user's historical behavior data, at least one component with the highest frequency is selected;

The list of historically selected components is displayed for the target user to select the first component for building the form.

The above method, preferably, also includes:

Obtain the components in the associated component list from the database;

Cache the obtained components.

A component acquisition device, comprising:

Obtaining a module for obtaining at least some of the components selected by the target user;

a determining module, configured to determine the associated components of the at least some of the components based on at least the at least some of the components, and generate a list of associated components;

A display module, configured to display the associated component list for the target user to select components.

An electronic device comprising:

memory for storing computer programs;

The processor is configured to execute the computer program to implement each step of the component acquisition method described in any one of the above.

A computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements each step of the component acquisition method described in any one of the above.

It can be seen from the above solutions that a component acquisition method, device, electronic device, and storage medium provided by the present application can obtain at least some components selected by a target user; based on at least some of the above components, determine the associated components of at least some components, and generate associations Component list; displays a list of associated components for component selection by target users. Based on the present application, in the process of building an interactive form by a user, the associated components of at least some of the selected components can be recommended for the user according to at least some of the components selected by the user, which reduces the range of components selected by the user, thereby reducing the need for users to obtain Component difficulty.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present application more clearly, the following briefly introduces the accompanying drawings that are used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, which are very important in the art. For those of ordinary skill, other drawings can also be obtained from these drawings without any creative effort.

FIG. 1 is a flow chart of an implementation of a method for obtaining a component provided by an embodiment of the present application;

2 is a flowchart of an implementation of determining the associated components of the above at least some components based on the attribute characteristics of the target user and the above at least some components according to the embodiment of the present application;

Fig. 3 is a kind of realization flow chart of classifying the historical behavior data of each user of the low-code platform according to each clustering category in the first clustering result provided by the embodiment of the present application;

FIG. 4 is a schematic structural diagram of a component acquisition device provided by an embodiment of the present application;

FIG. 5 is an example diagram of a hardware structural block diagram of an electronic device provided by an embodiment of the present application.

The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims and the above-mentioned drawings are used to distinguish similar parts and not necessarily to describe a particular order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments of the application described herein can be implemented in sequences other than those illustrated herein.

Detailed ways

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. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present application.

At present, when users (ie software developers) build interactive forms using a low-code platform, they mainly search according to search keywords to obtain the required components, and then drag and drop the obtained components to the design area to make the form (application program). The front-end interactive interface, which can include, for example, an input box, a drop-down box, a list that supports searching, a list that supports page turning, etc.) This method based on retrieval keywords/words depends on the user's mastery of the index keywords/words of the component resources. If the user does not know the keywords/words of the component resources, the component resources cannot be accurately found. Moreover, as the number of component resources increases, the difficulty for users to master the index keywords/words of component resources also increases (because the number of index keywords/words that need to be memorized increases), which further increases users' acquisition of required components difficulty.

There are also some solutions that provide the component resource browsing and searching function by classifying the component resources of the low-code platform, but this method relies on the user's understanding of the classification of the resource component. If the resource component itself is not clearly classified or the user does not understand the classification, it is very It is difficult to quickly locate the required component resources.

In order to reduce the difficulty for users to obtain required components, the solution of the present application is proposed.

Please refer to FIG. 1, which is an implementation flowchart of the component acquisition method provided by the embodiment of the present application, which may include:

Step S101: Obtain at least some components selected by the target user.

The target user here can refer to any user who logs in to the low-code platform. After logging in to the low-code platform, users can select components to build interactive forms according to development requirements.

In this embodiment of the present application, the component selected by the target user may be a component retrieved by the target user by searching keywords/words on the low-code platform, or may be a component selected based on the component acquisition method provided in this embodiment of the present application, or , some of which are components retrieved on the low-code platform by retrieving keywords/words, and some are components selected based on the component acquisition method provided by the embodiments of the present application.

At least some of the components selected by the target user may refer to all the components selected by the target user after logging in to the low-code platform, or may refer to the last selected part of all the components selected by the target user after logging in to the low-code platform (for example, the last selected component). Of course, this is only an exemplary description. During specific implementation, some components selected at the end may also be other numbers, for example, the last selected 3 components, the last selected 4 components, etc.).

Step S102: Based on at least some of the above-mentioned components, determine the associated components of the at least some of the components, and generate a list of associated components.

In this embodiment of the present application, the associated components of the above at least some components may be determined only based on the above at least some components, or the associated components of the above at least some components may be determined based on the above at least some components and other information.

The associated component of the above at least some components refers to the component that may appear in the form design area next after at least some of the above components appear in the form design area (selected by the target user) (that is, the next component that may be selected by the target user, Or the next component the target user might choose).

Step S103: Display a list of associated components for the target user to select components.

Displaying the list of associated components means recommending the components in the list of associated components to the target user, and the user can select components in the list of associated components to build a form.

The list of associated components can be displayed through an interactive window, and the user can drag and drop the components of the interactive window content to the form design area to build a form in the form design area.

The interactive window can be displayed somewhere around the form design area; or,

The interactive window can be suspended above the form design area, and the target user can drag and drop the interactive window to change the display position of the interactive window.

In the component acquisition method provided by the embodiment of the present application, in the process of building the interactive form by the user, the associated components of at least some of the selected components can be recommended for the user according to at least some of the components selected by the user, which reduces the scope of the components selected by the user, thereby reducing the scope of the user's selection of components. It reduces the difficulty for users to obtain the required components.

In an optional embodiment, the attribute characteristics of the target user may also be acquired, and based on this, an implementation manner of determining the associated components of the at least some of the above-mentioned at least some of the above-mentioned components may be as follows:

Based on the attribute characteristics of the target user and the above at least some components, the associated components of the above at least some components are determined.

The attribute feature of the user may include the professional feature of the user, for example, the professional field to which the user belongs. Optionally, the attribute characteristics of the user can be extracted from the identity information registered by the target user after the target user logs in to the low-code platform. For example, the professional field to which the user belongs can be the job position (software engineer) and the nature of the job the user is engaged in. (for example, research and development, operation and maintenance, etc.); or, the professional field to which the user belongs may be the application field of the developed software registered by the user (for example, the financial field, the legal field, etc.).

In the embodiment of the present application, the associated components of at least some components selected by the target user are determined in combination with the attribute characteristics of the low-code platform user and the historical behavior data of the low-code platform user, so that the determined associated components conform to the user's attribute characteristics, This further reduces the difficulty for users to obtain required components.

In an optional embodiment, an implementation manner of determining the associated components of the above at least some components based on the attribute characteristics of the target user and the above at least some components may include:

The attribute features of the target user and the above at least some of the components are processed by the component prediction engine to obtain the associated components of the above at least some of the components; wherein,

The above 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 includes at least the components selected when the user historically builds the interactive form, and the order of the selected components.

That is to say, in this embodiment of the present application, for each user of the low-code platform, each time the user builds the interactive form, the code interactive platform can save the components selected by the user to build the interactive form, and the selection order of the components, as A historical behavior data of the user.

After obtaining a plurality of historical behavior data of a plurality of users, for each user, the attribute characteristics of the user and part of the data intercepted from the historical behavior data of the user (the part of the data is at least one part of the historical behavior data) can be obtained. One component, and the 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 consecutive components in the selection order), as A piece of sample data, where the label of the sample data is located in the next component of the intercepted at least one component in the historical behavior data of the user. After obtaining the sample data set, use the sample data set to train the component prediction engine.

When training the component prediction engine, the input of the component prediction engine is sample data (that is, the attribute characteristics of the user and at least one component and the selection order of the at least one component), and the output of the component prediction engine is the lower part of the input at least one component. For the prediction result of one component, the prediction result of the next component is close to the label of the input sample data as the target, and 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, which are not specifically limited in this application.

In an optional embodiment, an implementation flowchart for determining the associated components of the above at least some components based on the attribute characteristics of the target user and the above at least some components is shown in FIG. 2 , and may include:

Step S201: Obtain a target component prediction engine associated with the attribute feature of the target user.

In the embodiment of the present application, multiple component prediction engines are set according to the attribute characteristics of the user, different component prediction engines are associated with different attribute characteristics, and the component prediction engines associated with different attribute characteristics may be the same (for example, similar attribute component prediction engine is the same), may be different.

Step S202: Process the attribute features of the target user and at least some of the components by the target component prediction engine to obtain the associated components of the above at least some of the components.

Wherein, 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 on the low-code platform; the historical behavior data of each user includes at least the user's historical construction interactive form The components selected at the time, and the order of the selected components.

In the embodiment of the present application, the target component prediction engine is obtained by learning historical behavior data related to the attribute characteristics of the target user. Among them, the historical behavior data related to the attribute characteristics of the target user may include:

The historical behavior data of users whose attribute characteristics are the same as those of the target user, and the historical behavior data of users whose attribute characteristics are similar to those of the target user.

By setting the component prediction engine associated with different attribute features, the target component prediction engine associated with the attribute features of the target user can be used to predict the associated components of at least some of the above components, so that the predicted associated components are more in line with the attribute features of the target user, thereby further Make it easier for users to select components.

Optionally, the target component prediction engine can be trained as follows:

Step 1: Cluster the attribute features of each user of the low-code platform to obtain a first clustering result.

The purpose of clustering is to identify similar attributes.

Step 2, classify the historical behavior data of each user of the low-code platform according to each clustering category in the first clustering result.

The historical behavior data of each user of the low-code platform may be classified according to each clustering category in the first clustering result by using a probability and statistical method.

For example, a Bayesian classification algorithm may be used to classify the historical behavior data of each user of the low-code platform according to each clustering category in the first clustering result. That is, for each historical behavior data of each user, calculate the probability of each cluster category appearing under the condition that the historical behavior data appears, and select the cluster with the largest probability of appearing under the condition that the historical behavior data appears. The category is the classification category of the historical behavior data.

Step 3: Corresponding to each cluster category, use the historical behavior data of the cluster category obtained by classification (ie, step 2) to train the component prediction engine to obtain the component prediction engine associated with the cluster category.

The historical behavior data of the clustering category can be processed first to obtain sample data. For the specific processing process, refer to the aforementioned process of obtaining sample data, which will not be described in detail here.

Then, the component prediction engine is trained by using the sample data. For the training process, reference may be made to the foregoing embodiment, which will not be repeated here.

The component prediction engine associated with the cluster category to which the attribute feature of the target user belongs is the target component prediction engine.

In an optional embodiment, in order to improve the effectiveness of the components recommended for users and further reduce the difficulty of obtaining components for users, the above-mentioned historical behavior data of each user of the low-code platform is based on each cluster in the first clustering result. An implementation flow chart of class classification is shown in Figure 3, which may include:

Step S301: Cluster 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 a clustering category with a quantity of historical behavior data greater than a threshold.

That is to say, the present application retains the historical behavior data that is used more (ie, the target historical behavior data). For the historical behavior data in the clustering category whose quantity of historical behavior data is less than the threshold, it is considered that the historical behavior data is used less, and the historical behavior data will not be learned, and the historical behavior data is eliminated.

Step S303: Classify the target historical behavior data according to each clustering category in the first clustering result.

In an optional embodiment, an implementation manner of determining the associated components of the above at least some components based on at least the above at least some components may be:

Process the at least part of the components by the component prediction engine, and obtain the associated components of the at least part of the components;

The component prediction engine is obtained based on the historical behavior data of each user of the low-code platform; the historical behavior data of each user includes the components selected by the user when the user historically built 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, part of the data can be intercepted from the user's historical behavior data (this 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, When the partial data is at least two components in the historical behavior data, the at least two components are at least two components that are consecutive in the selection sequence), as a sample data, the label of the sample data is in the historical behavior data of the user, Located in the next component of the intercepted at least one component. After obtaining the sample data set, use the sample data set to train the component prediction engine.

When training the component prediction engine, the input of the component prediction engine is sample data (ie at least one component and the selection order of the at least one component), and the output of the component prediction engine is the prediction result of the next component of the input at least one component , the prediction result of the next component is close to the label of the input sample data as the goal, and 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, which are not specifically limited in this application.

In an optional embodiment, when the target user has just logged into the low-code platform and has not selected any components, at least one historical selection component may be determined according to the attribute characteristics of the target user, and a list of historical selection components may be generated; The selection component is at least one component with the highest selection frequency among the first selected components of the historical behavior data of each user having the attribute characteristics of the target user or similar attribute characteristics (ie, attribute characteristics similar to the attribute characteristics of the target user).

Displays a list of historically selected components for the target user to select the first component used to build the form.

That is to say, when the target user does not select a component, the first component in the historical behavior data of each user with the attribute characteristics or similar attribute characteristics of the target user on the low-code platform (that is, the first component in the user's historical behavior data Statistical analysis is performed to determine at least one component with the highest selection frequency, and the at least one component with the highest selection frequency may be at least one component of the top N in the selection frequency ranking, or may be a component whose selection frequency is greater than a frequency threshold.

In this embodiment of the present application, even if the user does not select a component, the automatic recommendation of the component can also be implemented, which further reduces the difficulty for the user to select the component.

In an optional embodiment, after the associated component list is generated, the components in the associated component list can also be obtained from the database and cached, so that when the target user selects a component in the associated component list, the components in the associated component list can be retrieved from the cache. Extract components instead of fetching them from the database, thereby improving the efficiency of component selection for target users.

In an optional embodiment, after the target user decides to save the built form, the components selected by the target user and the order of the selected components can also be saved as the historical behavior data of the target user, so that the component prediction engine can be optimized subsequently. renew.

In an optional embodiment, if there is no component required by the target user in the generated association list, the target user can search for the required component based on keywords/words. Based on this, the component acquisition method provided by the embodiment of the present application Can also include:

Obtain a retrieval instruction, which carries keywords/words;

Retrieve the components matching the keyword/word in the component database (referred to as matching components); the specific matching process can refer to the existing solution, which will not be described in detail here.

Displays the retrieved matching components for selection by the target user.

Corresponding to the method embodiment, the embodiment of the present application further provides a component acquisition device. A schematic structural diagram of the component acquisition device provided by the embodiment of the present application is shown in FIG. 4 , which may include:

Obtaining module 401, determining module 402 and displaying module 403; wherein,

The obtaining module 401 is used to obtain at least some components selected by the target user;

The determining module 402 is configured to determine, based on at least the at least some of the components, associated components of the at least some of the components, and generate a list of associated components;

The display module 403 is configured to display the associated component list for the target user to select components.

In the component acquisition device provided by the embodiment of the present application, in the process of building the interactive form by the user, the associated components of at least some of the selected components can be recommended for the user according to at least some of the components selected by the user, which reduces the scope of the components selected by the user, thereby reducing the scope of the components selected by the user. It reduces the difficulty for users to obtain the required components.

In an optional embodiment, the obtaining module 401 may also be used to: obtain the attribute characteristics of the target user;

The determination module 402 is used to:

Based on the attribute characteristics of the target user and the at least part of the components, an associated component of the at least part of the components is determined.

In an optional embodiment, the determining module 402 is used to:

Process the attribute feature of the target user and the at least some of the components by a component prediction engine to obtain the associated components of the at least some of the 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 includes the components selected when the user historically built the interactive form, and the order of the selected components.

In an optional embodiment, the determining module 402 is used to:

Obtain the target component prediction engine associated with the attribute feature of the target user;

Process the attribute feature of the target user and the at least some components by the target component prediction engine, and obtain the associated components of the at least some 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 includes the historical behavior data selected by the user when building an interactive form. components, and the order in which the components are selected.

In an optional embodiment, it also includes: a training module for:

Clustering the attribute features of each user of the low-code platform to obtain a first clustering result;

classifying the 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, use the historical behavior data of the cluster category obtained by classification to train the component prediction engine, and obtain the component prediction engine associated with the cluster category;

The component prediction engine associated with the cluster category to which the attribute feature of the target user belongs 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 clustering category in the first clustering result, it is used to:

Clustering the historical behavior data of each user of the low-code platform to obtain a second clustering result;

According to the second clustering result, extract the target historical behavior data; the target historical behavior data is the historical behavior data under the clustering category in which the quantity of the historical behavior data is greater than the threshold;

Classify the target historical behavior data according to each clustering category in the first clustering result.

In an optional embodiment, the determining module 402 is used to:

Process the at least part of the components by a component prediction engine to obtain the associated components of the at least part of the components;

The component prediction engine is obtained based on the historical behavior data of each user of the low-code platform; the historical behavior data of each user includes the components selected when the user historically built the interactive form, and the order of the selected components.

In an optional embodiment, the determining module 402 is further configured to:

When the target user does not select a component, at least one historical selection component is determined according to the attribute characteristics of the target user, and a list of historical selection components is generated; the at least one historical selection component is each of the attribute characteristics or similar attribute characteristics. Among the first selected components of the user's historical behavior data, at least one component with the highest frequency is selected;

The reality module 403 is further configured to display the list of historically selected components for the target user to select the first component for building the form.

In an optional embodiment, it also includes:

The caching module is used for acquiring the components in the associated component list from the database; caching the acquired components.

In an optional embodiment, it also includes:

The saving module is used for saving the components selected by the target user and the order of the selected components, so as to optimize and update the component prediction engine.

In an optional embodiment, it also includes:

A retrieval module, used to obtain a retrieval instruction, the retrieval instruction carries keywords/words;

Search the component database for components matching the keyword/word (referred to as matching components).

The display module 403 is also used to display the retrieved matched components for selection by the target user.

Corresponding to the method embodiments, the present application further provides an electronic device, such as a terminal, a server, and the like. The server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or may provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, and cloud communications. , middleware services, domain name services, security services, CDN, and cloud servers for basic cloud computing services such as big data and artificial intelligence platforms. The terminal may be a mobile terminal such as a smart phone, a tablet computer, a notebook computer, etc., or a desktop computer, etc., but is not limited thereto. In some embodiments, the above-mentioned terminal or server may be a node in a distributed system, wherein the distributed system may be a blockchain system, and the blockchain system may be communicated by the plurality of nodes through a network A distributed system formed by formal connections. Among them, a peer-to-peer (P2P, Peer To Peer) network can be formed between nodes, and any form of computing devices, such as servers, terminals and other electronic devices can become a node in the blockchain system by joining the peer-to-peer network.

An example diagram of a hardware structural block diagram of an electronic device provided by an embodiment of the present application is shown in FIG. 5 , which may include:

processor 1, communication interface 2, memory 3 and communication bus 4;

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 can 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 the embodiments of the present application.

The memory 3 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

Wherein, the processor 1 is specifically used for executing the computer program stored in the memory 3 to execute the following steps:

Obtain at least some of the components that the target user has selected;

At least based on the at least some of the components, determine the associated components of the at least some of the components, and generate a list of associated components;

The list of associated components is displayed for component selection by the target user.

Optionally, the detailed functions and extended functions of the computer program may refer to the above description.

Embodiments of the present application further provide a readable storage medium, where the storage medium can store a computer program suitable for execution by a processor, where the computer program is used for:

Obtain at least some of the components that the target user has selected;

At least based on the at least some of the components, determine the associated components of the at least some of the components, and generate a list of associated components;

The list of associated components is displayed for component selection by the target user.

Optionally, the detailed functions and extended functions of the computer program may refer to the above description.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

In the several embodiments provided in this application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.

It should be understood that, in the embodiments of the present application, the rights, various embodiments, and features can be combined with each other, and the aforementioned technical problems can be solved.

The functions, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .

The above description of the disclosed embodiments enables 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 implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, this 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: Obtain at least some of the components that the target user has selected; At least based on the at least some of the components, determine the associated components of the at least some of the components, and generate a list of associated components; The list of associated components is displayed for component selection by the target user. 2. The method according to claim 1, further comprising: obtaining the attribute characteristics of the target user; The determining, based at least on the at least some of the components, an associated component of the at least some of the components includes: Based on the attribute characteristics of the target user and the at least part of the components, an associated component of the at least part of the components is determined. 3. The method according to claim 2, wherein determining the associated components of the at least some components based on the attribute characteristics of the target user and the at least some components, comprising: Process the attribute feature of the target user and the at least some of the components by a component prediction engine to obtain the associated components of the at least some of the 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 includes the components selected when the user historically built the interactive form, and the order of the selected components. 4. The method according to claim 2, wherein determining the associated components of the at least some components based on the attribute characteristics of the target user and the at least some components, comprising: Obtain the target component prediction engine associated with the attribute feature of the target user; Process the attribute feature of the target user and the at least some components by the target component prediction engine, and obtain the associated components of the at least some 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 includes the historical behavior data selected by the user when building an interactive form. components, and the order in which the components are selected. 5. The method according to claim 4, wherein the target component prediction engine is obtained in the following manner: Clustering the attribute features of each user of the low-code platform to obtain a first clustering result; classifying the 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, use the historical behavior data of the cluster category obtained by classification to train the component prediction engine, and obtain the component prediction engine associated with the cluster category; The component prediction engine associated with the cluster category to which the attribute feature of the target user belongs is the target component prediction engine. 6. The method according to any one of claims 2-5, further comprising: When the target user does not select a component, at least one historical selection component is determined according to the attribute characteristics of the target user, and a list of historical selection components is generated; the at least one historical selection component is each of the attribute characteristics or similar attribute characteristics. Among the first selected components of the user's historical behavior data, at least one component with the highest frequency is selected; The list of historically selected components is displayed for the target user to select the first component for building the form. 7. The method of claim 1, further comprising: Obtain the components in the associated component list from the database; Cache the obtained components. 8. A component acquisition device, comprising: Obtaining a module for obtaining at least some of the components selected by the target user; a determining module, configured to determine the associated components of the at least some of the components based on at least the at least some of the components, and generate a list of associated components; A display module, configured to display the associated component list for the target user to select components. 9. An electronic device comprising: memory for storing computer programs; A processor, configured to execute the computer program, to implement each step of the component acquisition method according to any one of claims 1-7. 10. A computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements each step of the component acquisition method according to any one of claims 1-7.

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