CN115237617A

CN115237617A - Interface component determination method, device, equipment, storage medium and program product

Info

Publication number: CN115237617A
Application number: CN202210716052.3A
Authority: CN
Inventors: 张丹枫; 张家宇; 谢鹏
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2022-06-23
Filing date: 2022-06-23
Publication date: 2022-10-25

Abstract

The application relates to a method and a device for determining an interface component, computer equipment, a storage medium and a program product, and relates to the technical field of big data. The method comprises the following steps: acquiring behavior data; the behavior data is generated by generating a target distributed application program for the low-code development platform; determining a target range of a next interface component corresponding to the current interface component according to a first call chain in which the current interface component is positioned and a pre-constructed knowledge graph in the behavior data; determining a target recommendation list of a next interface component corresponding to the current interface component according to the interface components in the target range and a pre-constructed time sequence database; the knowledge graph and the time sequence database are constructed according to information of interface components called when the low-code development platform generates the distributed application program in historical time periods. By adopting the method, the target recommendation list with high matching degree with the target distributed application program can be determined.

Description

Interface component determination method, device, equipment, storage medium and program product

Technical Field

The present application relates to the field of big data technologies, and in particular, to a method, an apparatus, a device, a storage medium, and a program product for determining an interface component.

Background

The low-code development platform is a development platform that can quickly generate an application without encoding (0 code) or with a small amount of code. Generating an application through a low-code development platform can improve the efficiency of the generated application.

Generally, when an application program is generated through a low-code development platform, a corresponding interface component needs to be called, but for the generation of the distributed application program, because the number of interface components to be called is huge, the matching degree between the called interface component and the interface component required by the distributed application program to be generated may be low.

Disclosure of Invention

In view of the foregoing, it is necessary to provide a method, an apparatus, a device, a storage medium, and a program product for determining an interface component, which can improve the matching degree between a called interface component and an interface component required by a distributed application to be generated.

In a first aspect, a method for determining an interface component is provided. The method comprises the following steps:

acquiring the behavior data; the behavior data is generated by generating a target distributed application program by a low-code development platform;

determining a target range of a next interface component corresponding to the current interface component according to a first call chain where the current interface component is located in the behavior data and a pre-constructed knowledge graph;

determining a target recommendation list of a next interface component corresponding to the current interface component according to the interface components in the target range and a pre-constructed time sequence database; the knowledge graph and the time sequence database are constructed according to information of interface components called when the low-code development platform generates the distributed application program in historical time periods.

In one embodiment, the target range comprises a first target range and a second target range; determining a target range of a next interface component corresponding to the current interface component according to a first call chain where the current interface component is located in the behavior data and a pre-constructed knowledge graph, wherein the target range comprises:

determining the first target scope according to a second call chain in the knowledge graph, wherein the second call chain is the same as the MD5 signature of the first call chain;

determining the second target scope based on a third call chain in the knowledge-graph that is different from the MD5 signature of the first call chain.

In one embodiment, the determining a target recommendation list of a next interface component corresponding to a current interface component according to the interface components in the target range and a pre-constructed time sequence database includes:

acquiring a first recommendation list according to the service scene label of each interface component in the first target range, the service scene label of the current interface component and the time sequence database;

acquiring a second recommendation list according to the service scene label of each interface component in the second target range, the service scene label of the current interface component and the time sequence database;

and combining the first recommendation list and the second recommendation list to obtain the target recommendation list.

In one embodiment, the obtaining a first initial recommendation list according to the service scenario label of the call chain in which each interface component is located in the first target range, the service scenario label of the current interface component, and the time sequence database includes:

determining the interface component with the same service scene label of each interface component in the first target range and the service scene label of the current interface component as a first target interface component;

determining a first initial recommendation list from the first target interface component according to the calling times of the first target interface component in the time sequence database;

determining the interface component with the service scene label different from the service scene label of the current interface component in the first target range as a second target interface component;

determining a second initial recommendation list from the second target interface component according to the calling times of the second target interface component in the time sequence database;

and combining the first initial recommendation list and the second initial recommendation list to obtain the first recommendation list.

In one embodiment, the obtaining a second recommendation list according to the service scenario labels of the interface components in the second target range, the service scenario label of the current interface component, and the time sequence database includes:

determining the interface component with the same service scene label of each interface component in the second target range and the service scene label of the current interface component as a third target interface component;

determining a third initial recommendation list from the third target interface component according to the calling times of the third target interface component in the time sequence database;

determining an interface component with a service scene label different from the service scene label of the current interface component in the second target range as a fourth target interface component;

determining a fourth initial recommendation list from the fourth target interface component according to the calling times of the fourth target interface component in the time sequence database;

and combining the third initial recommendation list and the fourth initial recommendation list to obtain the second recommendation list.

In one embodiment, before determining a target range in which a next interface component corresponding to a current interface component is located according to a first call chain in which the current interface component is located in the behavior data and a pre-constructed knowledge graph, the method further includes:

determining whether a call chain identical to the MD5 signature of the first call chain exists in the knowledge-graph;

and if the target range exists, the step of determining the target range of the next interface component corresponding to the current interface component according to the first call chain where the current interface component in the behavior data is located and a pre-constructed knowledge graph is executed.

In one embodiment, the method further comprises:

acquiring information of an interface component called when the low-code development platform generates a distributed application program in the historical time period; the information comprises called interface components, calling relations among the called interface components and calling times of the called interface components;

constructing the knowledge graph by taking the called interface components as nodes and taking calling relations among the called interface components as edges;

and constructing the time sequence database according to the calling times of the called interface components.

In a second aspect, the present application further provides an interface component determination apparatus. The device comprises:

the first acquisition module is used for acquiring behavior data; the behavior data is generated by a target distributed application program generated by a low-code development platform;

the first determining module is used for determining a target range of a next interface component corresponding to the current interface component according to a first calling chain where the current interface component is located in the behavior data and a pre-constructed knowledge graph;

and the second determination module is used for determining a target recommendation list of the next interface component corresponding to the current interface component according to the interface components in the target range and a pre-constructed time sequence database.

In a third aspect, the application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the method of determining an interface component as described in any of the embodiments of the first aspect when the processor executes the computer program.

In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method for determining an interface component according to any of the embodiments of the first aspect.

In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, implements the method for determining an interface component as set forth in any of the embodiments of the first aspect.

The method, the device, the equipment, the storage medium and the program product for determining the interface component determine the target range of the next interface component corresponding to the current interface component by acquiring the behavior data generated when the low-code development platform generates the target distributed application program, according to the first calling chain of the current interface component in the behavior data and the pre-constructed knowledge graph, and determine the target recommendation list of the next interface component corresponding to the current interface component according to the interface component in the target range and the pre-constructed time sequence database, wherein the knowledge graph and the time sequence database are constructed according to the information of the interface component called when the low-code development platform generates the distributed application program in the historical time period. In the embodiment of the application, the knowledge graph and the time sequence database are constructed according to the information of the interface component called when the distributed application program is generated by the low-code development platform in the historical time period, so that the knowledge graph contains the interface component called by the distributed application program in the historical time period, namely the knowledge graph contains the prior information of the interface component called by the generated distributed application program, further, the target range of the next interface component corresponding to the current interface component is determined according to the first call chain and the knowledge graph of the current interface component in the behavior data of the target distributed application program generated by the current low-code development platform, and the knowledge graph contains the prior information of the interface component called by the generated distributed application program, so that the matching degree between the target range of the next interface component corresponding to the determined current interface component and the target distributed application program is higher, and therefore, a target list with higher matching degree with the target distributed application program can be recommended according to the interface component in the target range and the pre-constructed time sequence database; in addition, the interface components called by the distributed application program can be managed by constructing the knowledge graph, and the management cost of huge interface components to be called is also reduced.

Drawings

FIG. 1 is a diagram of an application environment for a determination method of an interface component in one embodiment;

FIG. 2 is a flow diagram illustrating a method for determining an interface component in one embodiment;

FIG. 3 is a flow diagram illustrating a method for determining an interface component in another embodiment;

FIG. 4 is a flow chart illustrating a method for determining an interface component according to another embodiment;

FIG. 5 is a flow chart illustrating a method for determining an interface component according to another embodiment;

FIG. 6 is a flow chart illustrating a method for determining an interface component according to another embodiment;

FIG. 7 is a block diagram showing the structure of a determining means of the interface module in another embodiment;

fig. 8 is a block diagram showing the structure of the interface component determination means in another embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

It should be noted that the method, apparatus, device, storage medium and program product for determining an interface component according to the present application may be applied to the field of big data, and may also be applied to other technical fields except the field of big data.

The method for determining the interface component provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Fig. 1 provides a computer device, which may be a server, and its internal structure diagram may be as shown in fig. 1. The computer equipment is integrated with a low-code development platform, and an application program can be constructed through the low-code development platform. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data involved in the determination method of the interface component. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of determining an interface component.

In one embodiment, as shown in fig. 2, a method for determining an interface component is provided, which is described by taking the method as an example applied to the computer device in fig. 1, and includes the following steps:

s201, acquiring behavior data; the behavior data is generated by the low code development platform generating the target distributed application program.

The low-code development platform is a development platform which can quickly generate the application program without coding (0 code) or by a small amount of codes, behavior data can be generated when the application program is generated by the low-code development platform, and optionally, the behavior data can include a current interface component name, a parent interface component name, a generated service scene tag of the application program, and a call chain ID. Optionally, in this embodiment, the behavior data generated when the target distributed application is generated may be obtained by presetting a capture code segment on the low-code development platform, for example, the capture code segment may be embedded in a front end of the low-code development platform, or the capture code segment may be set in a computer device, or the behavior data may be obtained by using a third-party tool. Further, as an alternative implementation, when the target distributed application is generated by using a low-code development platform, the computer device may collect the generated behavior data, and transmit the collected behavior data to the monitoring center for storage through the gateway.

S202, determining a target range of a next interface component corresponding to the current interface component according to the first call chain of the current interface component in the behavior data and the pre-constructed knowledge graph.

The current interface component is an interface component which is called currently when the low-code development platform generates a target distributed application program; the first call chain is formed according to the call relation between the current interface component and the interface component called before; the pre-constructed knowledge graph may be a graph database composed of interface components called when the distributed application program is generated in the historical time period and call relations between the called interface components, and optionally, the knowledge graph may include a call chain composed of the interface components called when the distributed application program is generated in the historical time period. Optionally, the constructed knowledge graph can be used for searching and recommending interface components required when the distributed application program is generated. Optionally, the target range of the next interface component corresponding to the current interface component may be a range of a call chain in which the interface component matched with the generated target distributed application program is located in the knowledge graph.

Optionally, in this embodiment, a range where a call chain in the knowledge graph, of which the similarity to the first call chain is greater than a preset threshold, is located may be determined as the target range by calculating a similarity between the first call chain where the current interface component is located and the call chain in the knowledge graph. Alternatively, a range in which a call chain having the same length as the first call chain is located in the knowledge graph may be determined as the target range.

S203, determining a target recommendation list of a next interface component corresponding to the current interface component according to the interface components in the target range and a pre-constructed time sequence database; the knowledge graph and the time sequence database are constructed according to information of interface components called when the low-code development platform generates the distributed application program in historical time periods.

The historical time period refers to a time period before the target distributed application program is generated. Optionally, the pre-constructed time sequence database may be formed by the number of times of calling the interface component called when the distributed application program is generated according to the low-code development platform in a historical time period, and it can be understood that the time sequence database may be used to count the number of times of calling the interface component in different dimensions in a period of time.

Optionally, in this embodiment, the interface components in the target range may be grouped according to applications and clusters according to the service scene tag of the interface component and the service scene tag of the current interface component included in the call chain in the target range, the call times of the interface components in each cluster group under different applications are obtained from the time sequence database, the interface components in each cluster group are sorted in a reverse order according to the call times, and the top N sorted interface components are taken to form the target recommendation list.

The method for determining the interface component, provided by the embodiment of the application, includes the steps of obtaining behavior data generated when a low-code development platform generates a target distributed application program, determining a target range where a next interface component corresponding to a current interface component is located according to a first calling chain where the current interface component is located in the behavior data and a pre-constructed knowledge graph, and determining a target recommendation list of the next interface component corresponding to the current interface component according to the interface component in the target range and a pre-constructed time sequence database, wherein the knowledge graph and the time sequence database are constructed according to information of the interface component called when the low-code development platform generates the distributed application program in a historical time period. In the embodiment of the application, the knowledge graph and the time sequence database are constructed according to information of an interface component called when a distributed application program is generated by a low-code development platform in a historical time period, so that the knowledge graph comprises the interface component called when the distributed application program is generated in the historical time period, namely the knowledge graph comprises prior information of the interface component called when the distributed application program is generated, further, a target range where a next interface component corresponding to a current interface component is located is determined according to a first call chain where the current interface component is located in behavior data of the target distributed application program generated by the current low-code development platform and the knowledge graph, and the knowledge graph comprises the prior information of the interface component called when the distributed application program is generated, so that the matching degree between the target range where the next interface component corresponding to the determined current interface component is located and the target distributed application program is high, and a target recommendation list with a high matching degree with the target distributed application program can be determined according to the interface component in the target range and the pre-constructed time sequence database; in addition, the interface components called by the distributed application program can be managed by constructing the knowledge graph, and the management cost of huge interface components to be called is also reduced.

Further, on the basis of the embodiment shown in fig. 2, the target range where the next interface component corresponding to the current interface component is located may include a first target range and a second target range, and in an embodiment, as shown in fig. 3, the step S202 includes:

s301, determining a first target range according to a second call chain with the same MD5 signature as the first call chain in the knowledge graph.

The Message Digest Algorithm fifth edition (MD 5) signature is a cryptographic hash function to ensure the complete transmission of information. The MD5 signature of the first call chain is a signature obtained by encrypting information with an information encryption algorithm for a call chain formed by a call relation between the current interface component and the interface component called before. Optionally, the information encryption Algorithm may be an MD5 signature Algorithm, and may also be a Secure Hash Algorithm (SHA) Algorithm, which is not limited herein.

Alternatively, the first target scope may be the scope in which all second call chains in the knowledge-graph have the same MD5 signature as the first call chain. Optionally, in this embodiment, a call chain in which the hash value of the MD5 signature of the call chain in the knowledge graph is the same as the hash value of the MD5 signature of the first call chain may be determined as the second call chain.

S302, determining a second target range according to a third calling chain with different MD5 signature from the first calling chain in the knowledge graph.

And the second target range is a range in which all third call chains with different MD5 signatures from the first call chain are located in the knowledge graph. Optionally, in this embodiment, a call chain in which the hash value of the MD5 signature of the call chain in the knowledge graph is different from the hash value of the MD5 signature of the first call chain may be determined as the third call chain.

According to the embodiment of the application, a first target range is determined according to a first calling chain where a current interface component is located in behavior data and a pre-constructed knowledge graph, a second calling chain with the same signature as the MD5 of the first calling chain in the knowledge graph is used, a second target range is determined according to a third calling chain with the signature different from the MD5 of the first calling chain in the knowledge graph, and therefore the target range where a next interface component corresponding to the current interface component is located is determined.

Further, in the above scenario that the target recommendation list of the next interface component corresponding to the current interface component is determined according to the interface components in the target range and the pre-constructed time sequence database, in an embodiment, as shown in fig. 4, step S203 includes:

s401, acquiring a first recommendation list according to the service scene labels of the interface components in the first target range, the service scene label of the current interface component and the time sequence database.

The service scene label is an identifier of a service scene of a distributed application program generated by a low-code development platform, and optionally, the service scene identifier can be obtained by applying algorithms such as abstraction, induction and reasoning to static and dynamic characteristics of a target object according to the requirement of the corresponding service scene. Optionally, the interface components with the same service scene tags as the service scene tags of the current interface components in the first target range may be determined as first target interface components, the number of times of calling the first target interface components is determined from the time sequence database, the first target interface components are arranged in a reverse order according to the number of times of calling the first target interface components, and the first N interface components are taken from the sorted first target interface components as a first initial recommendation list, that is, the first N interface components with the largest number of times of calling in the sorted first target interface components may be taken as the first initial recommendation list; determining interface components with different service scene labels of each interface component in the first target range and the current interface component as second target interface components, determining the calling times of the second target interface components from the time sequence database, arranging the second target interface components in a reverse order according to the calling times of the second target interface components, and taking the first N interface components from the ordered second target interface components as a second initial recommendation list, namely taking the first N interface components with the largest calling times in the ordered second target interface components as the second initial recommendation list; further, the first initial recommendation list and the second initial recommendation list may be combined to obtain a first recommendation list.

Optionally, in this embodiment, the similarity between the service scene tag of each interface component in the first target range and the service scene tag of the current interface component may be compared through a text matching algorithm, so as to determine the first target interface component and the second target interface component. Optionally, the text matching algorithm may be a Brute Force (BF) algorithm, a Vector Space Model (VSM) algorithm, or an edit distance similarity algorithm, which is not limited herein.

S402, acquiring a second recommendation list according to the service scene labels of the interface components in the second target range, the service scene label of the current interface component and the time sequence database.

Optionally, the interface components with the same service scene tag of each interface component in the second target range as the service scene tag of the current interface component may be determined as third target interface components, the number of times of calling the third target interface components is determined from the time sequence database, the third target interface components are arranged in a reverse order according to the number of times of calling the third target interface components, and the first N interface components are taken from the sorted third target interface components as a third initial recommendation list, that is, the first N interface components with the largest number of times of calling in the sorted third target interface components may be taken as the third initial recommendation list; determining interface components with different service scene labels of each interface component in the second target range and the current interface component as fourth target interface components, determining the calling times of the fourth target interface components from the time sequence database, arranging the fourth target interface components in a reverse order according to the calling times of the fourth target interface components, and taking the first N interface components from the ordered fourth target interface components as a fourth initial recommendation list, namely taking the first N interface components with the largest calling times in the ordered fourth target interface components as the fourth initial recommendation list; and combining the third initial recommendation list and the fourth initial recommendation list to obtain a second recommendation list.

Optionally, in this embodiment, the similarity between the service scene label of each interface component in the second target range and the service scene label of the current interface component may be compared through a text matching algorithm, so as to determine a third target interface component and a fourth target interface component. The text matching algorithm may be a Brute Force (BF) algorithm, a Vector Space Model (VSM) algorithm, or an edit distance similarity algorithm, which is not limited herein.

And S403, combining the first recommendation list and the second recommendation list to obtain a target recommendation list.

Optionally, the first recommendation list and the second recommendation list may be directly combined to obtain a target recommendation list, or each interface component in the first recommendation list and each interface component in the second recommendation list may be mixed and arranged to obtain the target recommendation list.

In the embodiment of the application, the first recommendation list can be quickly obtained according to the service scene labels of the interface components in the first target range, the service scene labels of the current interface components and the time sequence database, the second recommendation list can be quickly obtained according to the service scene labels of the interface components in the second target range, the service scene labels of the current interface components and the time sequence database, and the efficiency of obtaining the first recommendation list and the second recommendation list is improved, so that the efficiency of combining the first recommendation list and the second recommendation list to obtain the target recommendation list is improved.

In the above scenario in which the target range of the next interface component corresponding to the current interface component is determined according to the first call chain in which the current interface component is located in the behavior data and the pre-constructed knowledge graph, it is necessary to first determine whether a call chain with the same signature as the MD5 signature of the first call chain exists in the knowledge graph. In an embodiment, as shown in fig. 5, before the above S202, the method further includes:

s501, whether a call chain with the same MD5 signature as the first call chain exists in the knowledge graph is determined.

Optionally, in this embodiment, the hash value of the MD5 signature of each call chain in the knowledge graph may be compared with the hash value of the MD5 signature of the first call chain, and it may be determined whether a call chain identical to the MD5 signature of the first call chain exists in the knowledge graph according to the comparison result. For example, if the comparison result includes a call chain with the same hash value as the MD5 signature of the first call chain, it may be determined that a call chain with the same MD5 signature as the first call chain exists in the knowledge graph; or if the comparison result includes two or more call chains with the same hash value as the MD5 signature of the first call chain, it may be determined that the call chain with the same MD5 signature as the first call chain exists in the knowledge graph; alternatively, if the comparison result does not include a call chain having the same hash value as the MD5 signature of the first call chain, it can be determined that there is no call chain in the knowledge graph having the same MD5 signature as the first call chain.

And S502, if the target range exists, determining a target range of a next interface component corresponding to the current interface component according to the first call chain of the current interface component in the behavior data and a pre-constructed knowledge graph.

Optionally, in this embodiment, if a call chain with the same signature as the MD5 signature of the first call chain exists in the knowledge graph, the step of determining the target range of the next interface component corresponding to the current interface component according to the first call chain where the current interface component in the behavior data is located and the pre-constructed knowledge graph may be performed; if the call chain with the same signature as the MD5 signature of the first call chain does not exist in the knowledge graph, the first call chain where the current interface component is located can be stored in the knowledge graph so as to increase the richness of the call chain in the knowledge graph.

In the embodiment of the application, the step of determining the target range of the next interface component corresponding to the current interface component according to the first calling chain and the pre-constructed knowledge graph in the behavior data is executed when the knowledge graph has the calling chain with the same signature as the MD5 signature of the first calling chain by determining whether the calling chain with the same signature as the MD5 signature of the first calling chain exists in the knowledge graph, so that the reliability of the target range of the next interface component corresponding to the current interface component is determined according to the first calling chain with the current interface component in the behavior data and the pre-constructed knowledge graph; in addition, if the call chain with the same signature as the MD5 signature of the first call chain does not exist in the knowledge graph, the information of the current interface component is stored in the knowledge graph, and the richness of the call chain in the knowledge graph can be enriched.

In the above scenario of determining the target recommendation list of the next interface component corresponding to the current interface component, a pre-constructed knowledge graph and a time sequence database are required. In one embodiment, as shown in fig. 6, the method further includes:

s601, acquiring information of an interface component called when a low-code development platform generates a distributed application program in a historical time period; the information comprises the called interface components, the calling relation among the called interface components and the calling times of the called interface components.

Optionally, in this embodiment, the information of the interface component called when the low-code development platform generates the distributed application program in the historical time period may be obtained by presetting a capture code segment on the low-code development platform. For example, the capture code segment may be embedded in the front end of the low-code development platform, or the capture code segment may be provided in the computer device, or information of an interface component called when the low-code development platform generates the distributed application program in a historical time period may be acquired by using a third-party tool.

S602, constructing the knowledge graph by taking the called interface components as nodes and taking the calling relationship between the called interface components as edges.

Optionally, the interface components called in the historical time period may be used as nodes in the knowledge graph, and the calling relationship between the called interface components is used as edges in the knowledge graph to construct the knowledge graph. Optionally, in this embodiment, the attributes of the nodes of the knowledge graph may include information about an application, a cluster, or a unit to which the interface component belongs, and the attributes of the edges in the knowledge graph may include a call direction, a call frequency, a service scene tag, a call chain signature, and the like of the interface component.

S603, constructing a time sequence database according to the calling times of the called interface component.

Optionally, the number of times of calling each interface component called when the low-code development platform generates the distributed application program may be obtained, and the number of times of calling each interface component is stored in the database to construct the time sequence database.

In the embodiment of the application, the knowledge graph and the time sequence database can be accurately constructed by generating the information of the interface component called by the distributed application program and the calling times of the called interface component through the low-code development platform in the historical time period, and the accuracy of the constructed knowledge graph and the time sequence database is improved.

To facilitate understanding of those skilled in the art, the following provides a detailed description of a method for determining an interface component, which may include:

s1, acquiring information of an interface component called when the low-code development platform generates a distributed application program in the historical time period; the information comprises the called interface components, the calling relation among the called interface components and the calling times of the called interface components.

And S2, constructing the knowledge graph by taking the called interface components as nodes and taking the calling relation between the called interface components as edges.

And S3, constructing the time sequence database according to the calling times of the called interface components.

S4, acquiring behavior data; the behavior data is generated by the low-code development platform generating the target distributed application program.

And S5, determining whether a call chain with the same MD5 signature as the first call chain exists in the knowledge graph.

And S6, if so, determining a first target range in which a next interface component corresponding to the current interface component is located according to a second call chain with the same signature as the MD5 signature of the first call chain in the knowledge graph, and determining a second target range in which the next interface component corresponding to the current interface component is located according to a third call chain with a signature different from the MD5 signature of the first call chain in the knowledge graph.

S7, determining the interface component with the same service scene label of each interface component in the first target range and the service scene label of the current interface component as a first target interface component, and determining a first initial recommendation list from the first target interface component according to the calling times of the first target interface component in the time sequence database.

And S8, determining the interface component with the service scene label different from the service scene label of the current interface component in the first target range as a second target interface component, and determining a second initial recommendation list from the second target interface component according to the calling times of the second target interface component in the time sequence database.

S10, combining the first initial recommendation list and the second initial recommendation list to obtain the first recommendation list.

S11, determining the interface component with the same service scene label of each interface component in the second target range as the service scene label of the current interface component as a third target interface component, and determining a third initial recommendation list from the third target interface component according to the calling times of the third target interface component in the time sequence database.

And S12, determining the interface component with the service scene label of each interface component in the second target range different from the service scene label of the current interface component as a fourth target interface component, and determining a fourth initial recommendation list from the fourth target interface component according to the calling times of the fourth target interface component in the time sequence database.

And S13, combining the third initial recommendation list and the fourth initial recommendation list to obtain the second recommendation list.

S14, combining the first recommendation list and the second recommendation list to obtain the target recommendation list.

For the implementation principle in S1 to S14, please refer to the description in the above embodiments, which is not repeated herein.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the present application further provides a loss determining apparatus for implementing the loss determining method mentioned above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so specific limitations in one or more embodiments of the loss determination device provided below may refer to the limitations in the loss determination method above, and details are not described here.

In one embodiment, as shown in fig. 7, there is provided an interface component determination apparatus including: a first obtaining module 11, configured to obtain behavior data; the behavior data is generated by generating a target distributed application program for the low-code development platform;

the first determining module 12 is configured to determine, according to a first call chain in which a current interface component is located in the behavior data and a pre-constructed knowledge graph, a target range in which a next interface component corresponding to the current interface component is located;

and a second determining module 13, configured to determine a target recommendation list of a next interface component corresponding to the current interface component according to the interface components in the target range and a pre-constructed time sequence database.

The determining apparatus of the interface component provided in this embodiment may perform the method embodiments described above, and the implementation principle and the technical effect are similar, which are not described herein again.

In one embodiment, as shown in fig. 8, the first determining module 12 includes:

a first determining unit 121, configured to determine a first target scope according to a second call chain in the knowledge-graph, which is the same as the MD5 signature of the first call chain;

a second determining unit 122, configured to determine a second target scope according to a third call chain in the knowledge-graph, which is different from the MD5 signature of the first call chain.

The determining apparatus of the interface component provided in this embodiment may execute the method embodiments described above, and the implementation principle and the technical effect are similar, which are not described herein again.

In one embodiment, and with continued reference to fig. 8, the second determining module 13 includes:

a first obtaining unit 131, configured to obtain a first recommendation list according to the service scenario tag of each interface component in the first target range, the service scenario tag of the current interface component, and the time sequence database;

the second obtaining unit 132 obtains a second recommendation list according to the service scenario labels of the interface components in the second target range, the service scenario label of the current interface component, and the time sequence database,

the third determining unit 133 combines the first recommendation list and the second recommendation list to obtain a target recommendation list.

In one embodiment, the first obtaining unit 131 is specifically configured to:

determining the interface component with the same service scene label of each interface component in the first target range and the service scene label of the current interface component as a first target interface component; determining a first initial recommendation list from a first target interface component according to the calling times of the first target interface component in the time sequence database; determining interface components with different service scene labels of each interface component in the first target range and the current interface component as second target interface components; determining a second initial recommendation list from a second target interface component according to the calling times of the second target interface component in the time sequence database; and combining the first initial recommendation list and the second initial recommendation list to obtain a first recommendation list.

In one embodiment, the second obtaining unit 132 is specifically configured to:

determining the interface component with the same service scene label of each interface component in the second target range and the service scene label of the current interface component as a third target interface component; determining a third initial recommendation list from a third target interface component according to the calling times of the third target interface component in the time sequence database; determining an interface component with a service scene label different from the service scene label of the current interface component in the second target range as a fourth target interface component; determining a fourth initial recommendation list from a fourth target interface component according to the calling times of the fourth target interface component in the time sequence database; and combining the third initial recommendation list and the initial recommendation list to obtain a second recommendation list.

In one embodiment, with continued reference to fig. 8, the apparatus further comprises: a third determination module 14 and an execution module 15; wherein:

a third determining module 14, configured to determine whether there is a call chain in the knowledge-graph that is the same as the MD5 signature of the first call chain.

And an executing module 15, configured to execute, if a call chain with a signature that is the same as the MD5 signature of the first call chain exists in the knowledge graph, the step of determining, according to the first call chain where the current interface component in the behavior data is located and the pre-constructed knowledge graph, the target range where the next interface component corresponding to the current interface component is located.

In one embodiment, with continued reference to fig. 8, the apparatus further comprises: a second acquisition module 16, a first construction module 17 and a second construction module 18; wherein:

a second obtaining module 16, configured to obtain information of the interface component called when the low-code development platform generates the distributed application program in the historical time period, where the information includes the called interface component, a call relationship between the called interface components, and a number of calls of the called interface component.

A first constructing module 17, configured to construct the knowledge graph by using the called interface components as nodes and using the calling relationship between the called interface components as edges.

And a second constructing module 18, configured to construct the time sequence database according to the number of times of calling the called interface component.

The various modules in the interface component-determining apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:

acquiring behavior data; the behavior data is generated by generating a target distributed application program for the low-code development platform;

In one embodiment, the processor, when executing the computer program, further performs the steps of: determining a target range of a next interface component corresponding to the current interface component according to a first call chain where the current interface component is located in the behavior data and a pre-constructed knowledge graph, wherein the target range comprises:

determining a first target range according to a second call chain with the same MD5 signature as the first call chain in the knowledge graph;

determining a second target scope based on a third call chain in the knowledge-graph that is different from the MD5 signature of the first call chain.

In one embodiment, the processor when executing the computer program further performs the steps of: determining a target recommendation list of a next interface component corresponding to the current interface component according to the interface components in the target range and a pre-constructed time sequence database, wherein the target recommendation list comprises:

acquiring a first recommendation list according to the service scene label of each interface component in the first target range, the service scene label of the current interface component and a time sequence database;

acquiring a second recommendation list according to the service scene labels of the interface components in the second target range, the service scene label of the current interface component and the time sequence database;

and combining the first recommendation list and the second recommendation list to obtain a target recommendation list.

In one embodiment, the processor when executing the computer program further performs the steps of: acquiring a first recommendation list according to the service scene label of the call chain where each interface component is located in the first target range, the service scene label of the current interface component and a time sequence database, wherein the first recommendation list comprises:

determining a first initial recommendation list from a first target interface component according to the calling times of the first target interface component in the time sequence database;

determining interface components with different service scene labels of each interface component in the first target range and the current interface component as second target interface components;

and combining the first initial recommendation list and the second initial recommendation list to obtain a first recommendation list.

In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring a second recommendation list according to the service scene labels of the interface components in the second target range, the service scene label of the current interface component and the time sequence database, wherein the second recommendation list comprises:

determining a third initial recommendation list from a third target interface component according to the calling times of the third target interface component in the time sequence database;

determining a fourth initial recommendation list from a fourth target interface component according to the calling times of the fourth target interface component in the time sequence database;

and combining the third initial recommendation list and the fourth initial recommendation list to obtain a second recommendation list.

In one embodiment, the processor when executing the computer program further performs the steps of: before determining a target range of a next interface component corresponding to a current interface component according to a first call chain where the current interface component is located in behavior data and a pre-constructed knowledge graph, the method further comprises:

determining whether a call chain identical to the MD5 signature of the first call chain exists in the knowledge graph;

and if the target range exists, determining the target range of the next interface component corresponding to the current interface component according to the first call chain in which the current interface component exists in the behavior data and the pre-constructed knowledge graph.

In one embodiment, the processor, when executing the computer program, further performs the steps of: the method further comprises the following steps:

acquiring information of an interface component called when a low-code development platform generates a distributed application program in a historical time period; the information comprises called interface components, calling relations among the called interface components and calling times of the called interface components;

constructing a knowledge graph by taking the called interface component as an interface component and taking a calling relation between the called interface components as an edge;

and constructing a time sequence database according to the calling times of the called interface components.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, performs the steps of:

determining a target recommendation list of a next interface component corresponding to the current interface component according to the interface components in the target range and a pre-constructed time sequence database; the knowledge graph and the time sequence database are constructed according to information of interface components called when the low-code development platform generates the distributed application program in the historical time period.

In one embodiment, the processor when executing the computer program further performs the steps of: determining a target range of a next interface component corresponding to the current interface component according to a first call chain where the current interface component is located in the behavior data and a pre-constructed knowledge graph, wherein the target range comprises:

In one embodiment, the processor, when executing the computer program, further performs the steps of: determining a target recommendation list of a next interface component corresponding to a current interface component according to the interface components in the target range and a pre-constructed time sequence database, wherein the target recommendation list comprises the following steps:

In one embodiment, the processor when executing the computer program further performs the steps of: acquiring a first recommendation list according to the service scene label of the call chain of each interface component in the first target range, the service scene label of the current interface component and a time sequence database, wherein the first recommendation list comprises:

determining a second initial recommendation list from a second target interface component according to the calling times of the second target interface component in the time sequence database;

and if so, determining a target range of a next interface component corresponding to the current interface component according to the first call chain of the current interface component in the behavior data and a pre-constructed knowledge graph.

In one embodiment, the processor when executing the computer program further performs the steps of: the method further comprises the following steps:

In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of:

determining a target range of a next interface component corresponding to the current interface component according to a first call chain in which the current interface component is positioned and a pre-constructed knowledge graph in the behavior data;

In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring a first recommendation list according to the service scene label of the call chain where each interface component is located in the first target range, the service scene label of the current interface component and a time sequence database, wherein the first recommendation list comprises:

determining the interface component with the service scene label different from the service scene label of the current interface component in the second target range as a fourth target interface component;

In one embodiment, the processor, when executing the computer program, further performs the steps of: before determining a target range of a next interface component corresponding to a current interface component according to a first call chain where the current interface component is located in behavior data and a pre-constructed knowledge graph, the method further comprises:

It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for determining an interface component, the method comprising:

acquiring behavior data; the behavior data is generated by generating a target distributed application program by a low-code development platform;

2. The method of claim 1, wherein the target range comprises a first target range and a second target range; determining a target range of a next interface component corresponding to the current interface component according to a first call chain where the current interface component is located in the behavior data and a pre-constructed knowledge graph, wherein the target range comprises:

determining the second target scope based on a third call chain of the knowledge-graph that is different from the MD5 signature of the first call chain.

3. The method of claim 2, wherein determining a target recommendation list of a next interface component corresponding to a current interface component according to the interface components in the target scope and a pre-constructed time sequence database comprises:

4. The method according to claim 3, wherein the obtaining a first recommendation list according to the service scenario label of the call chain in which each interface component is located in the first target range, the service scenario label of the current interface component, and the time sequence database includes:

5. The method according to claim 3, wherein the obtaining a second recommendation list according to the service scenario labels of the interface components in the second target range, the service scenario label of the current interface component, and the time sequence database includes:

6. The method according to claim 1, wherein before determining a target range in which a next interface component corresponding to a current interface component is located according to a first call chain in which the current interface component is located in the behavior data and a pre-constructed knowledge graph, the method further comprises:

determining whether a call chain exists in the knowledge-graph that is the same as the MD5 signature of the first call chain;

and if so, executing the step of determining the target range of the next interface component corresponding to the current interface component according to the first calling chain of the current interface component in the behavior data and the pre-constructed knowledge graph.

7. The method according to any one of claims 1-6, further comprising:

8. An apparatus for determining an interface component, the apparatus comprising:

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any 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 method according to any one of claims 1 to 7.

11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.