Method, device, equipment, storage medium and program product for determining interface component

Abstract

The application relates to a method and 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: obtaining behavior data; the behavior data is generated by a target distributed application program generated by a low-code development platform; according to a first call chain in which a current interface component in the behavior data is located and a pre-constructed knowledge graph, a target range in which a next interface component corresponding to the current interface component is located is determined; according to an interface component in the target range and a pre-constructed time sequence database, a target recommendation list of the next interface component corresponding to the current interface component is determined; wherein the knowledge graph and the time sequence database are constructed according to information of the interface components called when the low-code development platform generates the distributed application program in a historical time period. By using the method, a target recommendation list with high matching degree with the target distributed application program can be determined.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a method, apparatus, device, storage medium, and program product for determining interface components. Background Technology

[0002] Low-code development platforms are development platforms that allow for the rapid generation of applications with no coding (0 code) or with minimal coding. Generating applications through a low-code development platform can improve the efficiency of the generated applications.

[0003] Typically, when generating applications through a low-code development platform, corresponding interface components need to be called. However, for the generation of distributed applications, due to the large number of interface components to be called, there may be a problem that the interface components to be called do not match the interface components required by the distributed application to be generated. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, device, storage medium, and program product for determining interface components that can improve the matching degree between the invoked interface components and the interface components required to generate the distributed application, in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a method for determining interface components. The method includes:

[0006] The behavioral data is obtained; the behavioral data is generated by the low-code development platform when generating the target distributed application.

[0007] Based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, determine the target range of the next interface component corresponding to the current interface component;

[0008] Based on the interface components in the target scope and the pre-built time-series database, a target recommendation list for the next interface component corresponding to the current interface component is determined; wherein, the knowledge graph and the time-series database are constructed based on information of the interface components called when the low-code development platform generates distributed applications within a historical time period.

[0009] In one embodiment, the target scope includes a first target scope and a second target scope; determining the target scope of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph includes:

[0010] The first target range is determined based on the second call chain in the knowledge graph that has the same MD5 signature as the first call chain;

[0011] The second target range is determined based on a third call chain in the knowledge graph whose MD5 signature differs from that of the first call chain.

[0012] In one embodiment, determining the target recommendation list of the next interface component corresponding to the current interface component based on the interface components in the target range and a pre-built time-series database includes:

[0013] Based on the business scenario tags of each interface component in the first target range, the business scenario tag of the current interface component, and the time-series database, obtain the first recommendation list;

[0014] A second recommendation list is obtained based on the business scenario tags of each interface component in the second target range, the business scenario tags of the current interface component, and the time-series database.

[0015] The first recommendation list and the second recommendation list are combined to obtain the target recommendation list.

[0016] In one embodiment, obtaining the first initial recommendation list based on the business scenario tags of the call chains of each interface component in the first target range, the business scenario tags of the current interface component, and the time-series database includes:

[0017] The interface component whose business scenario label is the same as that of the current interface component in the first target range is identified as the first target interface component;

[0018] Based on the number of times the first target interface component is called in the time series database, a first initial recommendation list is determined from the first target interface component;

[0019] The interface components whose business scenario labels are different from those of the current interface component in the first target range are identified as the second target interface components;

[0020] Based on the number of times the second target interface component is called in the time series database, a second initial recommendation list is determined from the second target interface component;

[0021] The first initial recommendation list and the second initial recommendation list are combined to obtain the first recommendation list.

[0022] In one embodiment, obtaining the second recommendation list based on the business scenario tags of each interface component in the second target range, the business scenario tag of the current interface component, and the time-series database includes:

[0023] The interface components whose business scenario labels are the same as those of the current interface component in the second target range are identified as the third target interface components;

[0024] Based on the number of times the third target interface component is called in the time series database, a third initial recommendation list is determined from the third target interface component;

[0025] The interface component whose business scenario label is different from that of the current interface component in the second target range is identified as the fourth target interface component;

[0026] Based on the number of calls to the fourth target interface component in the time series database, a fourth initial recommendation list is determined from the fourth target interface component;

[0027] The third initial recommendation list and the fourth initial recommendation list are combined to obtain the second recommendation list.

[0028] In one embodiment, before determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, the method further includes:

[0029] Determine whether there exists a call chain in the knowledge graph that has the same MD5 signature as the first call chain;

[0030] If it exists, then the step of determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavior data and the pre-built knowledge graph is executed.

[0031] In one embodiment, the method further includes:

[0032] Obtain information about the interface components called by the low-code development platform when generating distributed applications within the historical time period; the information includes the called interface components, the calling relationships between the called interface components, and the number of times the called interface components are called;

[0033] The knowledge graph is constructed using the invoked interface components as nodes and the invocation relationships between the invoked interface components as edges.

[0034] The time-series database is constructed based on the number of times the interface components are called.

[0035] Secondly, this application also provides an apparatus for determining an interface component. The apparatus includes:

[0036] The first acquisition module is used to acquire behavioral data; the behavioral data is generated by the low-code development platform to generate the target distributed application.

[0037] The first determining module is used to determine the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavior data and the pre-built knowledge graph;

[0038] The second determining module is used to determine the target recommendation list of the next interface component corresponding to the current interface component based on the interface components in the target range and the pre-built time series database.

[0039] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method for determining interface components as described in any embodiment of the first aspect.

[0040] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the method for determining interface components as described in any embodiment of the first aspect.

[0041] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the method for determining interface components as described in any embodiment of the first aspect.

[0042] The aforementioned method, apparatus, device, storage medium, and program product for determining interface components acquire behavioral data generated when a low-code development platform generates a target distributed application. Based on the first call chain of the current interface component in the behavioral data and a pre-built knowledge graph, the target range of the next interface component corresponding to the current interface component is determined. Based on the interface components in the target range and a pre-built time-series database, a target recommendation list of the next interface component corresponding to the current interface component is determined. The aforementioned knowledge graph and time-series database are constructed based on information about the interface components called by the low-code development platform when generating the distributed application within a historical time period. In this embodiment, since the knowledge graph and time-series database are constructed based on information about the interface components called by the low-code development platform when generating distributed applications within a historical time period, the knowledge graph contains the interface components called during the generation of distributed applications within that historical time period. In other words, the knowledge graph contains prior information about the interface components called during the generation of distributed applications. Furthermore, since the target range of the next interface component corresponding to the current interface component is determined based on the first call chain of the current interface component in the behavioral data of the target distributed application generated by the current low-code development platform and the knowledge graph, and since the knowledge graph contains prior information about the interface components called during the generation of distributed applications, the target range of the next interface component corresponding to the current interface component has a high degree of matching with the target distributed application. Therefore, based on the interface components in the target range and the pre-built time-series database, a target recommendation list with a high degree of matching with the target distributed application can be determined. In addition, constructing a knowledge graph also allows for the management of the interface components called during the generation of distributed applications, reducing the management cost of a large number of interface components to be called. Attached Figure Description

[0043] Figure 1 This is an application environment diagram of a method for determining interface components in one embodiment;

[0044] Figure 2 This is a flowchart illustrating a method for determining interface components in one embodiment;

[0045] Figure 3 This is a flowchart illustrating the method for determining interface components in another embodiment;

[0046] Figure 4 This is a flowchart illustrating the method for determining interface components in another embodiment;

[0047] Figure 5 This is a flowchart illustrating the method for determining interface components in another embodiment;

[0048] Figure 6This is a flowchart illustrating the method for determining interface components in another embodiment;

[0049] Figure 7 This is a structural block diagram of the interface component determination device in another embodiment;

[0050] Figure 8 This is a structural block diagram of the interface component determination device in another embodiment. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] It should be noted that the method, apparatus, device, storage medium, and program product for determining the interface component of this application can be applied in the field of big data, as well as in other technical fields. This application does not limit the application field of the method, apparatus, device, storage medium, and program product for determining the interface component.

[0053] The method for determining interface components provided in this application embodiment can be applied to, for example, Figure 1 The application environment shown. Figure 1 A computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 1 As shown, the computer device integrates a low-code development platform, through which applications can be built. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data involved in the method for determining interface components. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for determining interface components.

[0054] In one embodiment, such as Figure 2 As shown, a method for determining interface components is provided, which is then applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps:

[0055] S201, Obtain behavioral data; behavioral data is generated by the low-code development platform to create the target distributed application.

[0056] The low-code development platform is a platform that allows for the rapid generation of applications with no coding (zero code) or with minimal coding. When generating an application using the low-code platform, behavioral data is generated. Optionally, this behavioral data may include the current interface component name, the parent interface component name, the business scenario tag of the generated application, and the call chain ID. Optionally, in this embodiment, the aforementioned behavioral data generated during the generation of the target distributed application can be obtained by pre-setting a capture code segment on the low-code development platform. For example, this capture code segment can be embedded in the front end of the low-code development platform, or it can be set in the computer device, or the behavioral data can be obtained using third-party tools. Further, as an optional implementation, when generating the target distributed application using the low-code development platform, the computer device can collect the generated behavioral data and transmit the collected behavioral data to the monitoring center for storage via a gateway.

[0057] S202, based on the first call chain where the current interface component is located in the behavioral data and the pre-built knowledge graph, determine the target range where the next interface component corresponding to the current interface component is located.

[0058] The current interface component is the interface component currently invoked when the low-code development platform generates the target distributed application. The first call chain is a call chain formed based on the call relationship between the current interface component and previously invoked interface components. The pre-built knowledge graph can be a graph database composed of interface components invoked during the generation of the distributed application within a historical time period and the call relationships between these invoked interface components. Optionally, the knowledge graph may include call chains composed of interface components invoked during the generation of the distributed application within a historical time period. Optionally, the constructed knowledge graph can be used to find and recommend the interface components required for generating the distributed application. Optionally, the target scope of the next interface component corresponding to the current interface component can be the scope of the call chain of the interface component in the knowledge graph that matches the generated target distributed application.

[0059] Optionally, in this embodiment, the target range can be determined by calculating the similarity between the first call chain containing the current interface component and the call chains in the knowledge graph, and identifying the range of call chains in the knowledge graph whose similarity to the first call chain is greater than a preset threshold. Alternatively, the target range can be determined by identifying the range of call chains in the knowledge graph with the same length as the first call chain.

[0060] S203, based on the interface components in the target scope and the pre-built time-series database, determine the target recommendation list of the next interface component corresponding to the current interface component; wherein, the knowledge graph and the time-series database are constructed based on the information of the interface components called when the low-code development platform generates distributed applications within a historical time period.

[0061] The historical time period refers to the period prior to the generation of the target distributed application. Optionally, the pre-built time-series database can be composed of the number of times the interface components were called when generating the distributed application based on the low-code development platform within the historical time period. It can be understood that the time-series database can be used to count the number of times the interface components were called under different dimensions within a certain period.

[0062] Optionally, in this embodiment, the interface components in the target scope can be grouped by application and cluster according to the business scenario tags of the interface components included in the call chain in the target scope and the business scenario tags of the current interface component. The call count of the interface components in each cluster group under different applications can be obtained from the time series database. The interface components in each cluster group can be sorted in reverse order according to the call count, and the top N interface components after sorting can be used to form the target recommendation list.

[0063] The method for determining interface components provided in this application involves acquiring behavioral data generated when a low-code development platform generates a target distributed application, determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and a pre-built knowledge graph, and determining a target recommendation list of the next interface component corresponding to the current interface component based on the interface components in the target range and a pre-built time-series database. The aforementioned knowledge graph and time-series database are constructed based on information about the interface components called by the low-code development platform when generating the distributed application within a historical time period. In this embodiment, since the knowledge graph and time-series database are constructed based on information about the interface components called by the low-code development platform when generating distributed applications within a historical time period, the knowledge graph contains the interface components called during the generation of distributed applications within that historical time period. In other words, the knowledge graph contains prior information about the interface components called during the generation of distributed applications. Furthermore, since the target range of the next interface component corresponding to the current interface component is determined based on the first call chain of the current interface component in the behavioral data of the target distributed application generated by the current low-code development platform and the knowledge graph, and since the knowledge graph contains prior information about the interface components called during the generation of distributed applications, the target range of the next interface component corresponding to the current interface component has a high degree of matching with the target distributed application. Therefore, based on the interface components in the target range and the pre-built time-series database, a target recommendation list with a high degree of matching with the target distributed application can be determined. In addition, constructing a knowledge graph also allows for the management of the interface components called during the generation of distributed applications, reducing the management cost of a large number of interface components to be called.

[0064] Furthermore, in Figure 2 Based on the illustrated embodiment, the target scope of the next interface component corresponding to the current interface component may include a first target scope and a second target scope. In one embodiment, such as... Figure 3 As shown, the above S202 includes:

[0065] S301, determine the first target range based on the second call chain in the knowledge graph that has the same MD5 signature as the first call chain.

[0066] The Message Digest Algorithm Version 5 (MD5) signature is a cryptographic hash function used to ensure the complete transmission of information. The MD5 signature of the first call chain is obtained by encrypting the call chain formed by the call relationship between the current interface component and previously called interface components using a message encryption algorithm. Optionally, the aforementioned message encryption algorithm can be the MD5 signature algorithm or the Secure Hash Algorithm (SHA) algorithm; this application does not impose any limitation on this.

[0067] Optionally, the first target range can be the range of all second call chains in the knowledge graph that have the same MD5 signature as the first call chain. Optionally, in this embodiment, the call chain in the knowledge graph whose MD5 signature hash value is the same as the MD5 signature hash value of the first call chain can be determined as the second call chain.

[0068] S302, determine the second target range based on the third call chain in the knowledge graph that has a different MD5 signature from the first call chain.

[0069] The second target range is the range of all third call chains in the knowledge graph whose MD5 signatures differ from those of the first call chain. Optionally, in this embodiment, the call chain whose MD5 signature hash value differs from that of the first call chain can be identified as the third call chain.

[0070] In this embodiment, a first target range is determined based on the first call chain of the current interface component in the behavioral data and a pre-built knowledge graph. A second target range is determined based on the second call chain in the knowledge graph that has the same MD5 signature as the first call chain. A second target range is determined based on the third call chain in the knowledge graph that has a different MD5 signature from the first call chain. This determines the target range of the next interface component corresponding to the current interface component. This comprehensively considers both call chains in the knowledge graph that have the same MD5 signature as the first call chain and call chains that have a different MD5 signature, thereby improving the richness of the determined target range of the next interface component corresponding to the current interface component.

[0071] Furthermore, in the scenario described above where a target recommendation list for the next interface component corresponding to the current interface component is determined based on the interface components within the target scope and a pre-built time-series database, in one embodiment, as follows: Figure 4 As shown, the above S203 includes:

[0072] S401, based on the business scenario tags of each interface component in the first target scope, the business scenario tags of the current interface component, and the time-series database, obtain the first recommendation list.

[0073] Among them, the business scenario tag is the identifier of the business scenario in which the low-code development platform generates the distributed application. Optionally, the above business scenario identifier can be obtained by applying algorithms such as abstraction, induction, and reasoning to the static and dynamic characteristics of the target object according to the requirements of the corresponding business scenario. Optionally, interface components whose business scenario tags are the same as those of the current interface component within the first target scope can be identified as first target interface components. The call count of the first target interface components is determined from the aforementioned time-series database. The first target interface components are then sorted in reverse order based on their call count. The top N interface components from the sorted first target interface components are selected as the first initial recommendation list. In other words, the top N interface components with the highest call counts among the sorted first target interface components can be used as the first initial recommendation list. Similarly, interface components whose business scenario tags are different from those of the current interface component within the first target scope can be identified as second target interface components. The call count of the second target interface components is determined from the aforementioned time-series database. The second target interface components are then sorted in reverse order based on their call counts. The top N interface components from the sorted second target interface components are selected as the second initial recommendation list. Furthermore, the first and second initial recommendation lists can be combined to obtain a first recommendation list.

[0074] Optionally, in this embodiment, a text matching algorithm can be used to compare the similarity between the business scenario tags of each interface component in the first target range and the business scenario tag of the current interface component, thereby determining the first target interface component and the second target interface component. Optionally, the above text matching algorithm can be a brute force (BF) algorithm, a vector space model (VSM) algorithm, or an edit distance similarity algorithm; this application does not impose any limitations on this.

[0075] S402, based on the business scenario tags of each interface component in the second target scope, the business scenario tags of the current interface component, and the time series database, obtain the second recommendation list.

[0076] Optionally, interface components whose business scenario tags are the same as those of the current interface component within the second target scope can be identified as third target interface components. The call count of the third target interface components is determined from the aforementioned time-series database. The third target interface components are then sorted in reverse order based on their call count. The top N interface components from this sorted list are selected as the third initial recommendation list. In other words, the top N interface components with the highest call counts from the sorted third target interface components are selected as the third initial recommendation list. Similarly, interface components whose business scenario tags are different from those of the current interface component within the second target scope can be identified as fourth target interface components. The call count of the fourth target interface components is determined from the aforementioned time-series database. The fourth target interface components are then sorted in reverse order based on their call count. The top N interface components from this sorted list are selected as the fourth initial recommendation list. The third and fourth initial recommendation lists are then combined to obtain the second recommendation list.

[0077] Optionally, in this embodiment, a text matching algorithm can be used to compare the similarity between the business scenario tags of each interface component in the second target range and the business scenario tag of the current interface component, thereby determining the third target interface component and the fourth target interface component. The text matching algorithm can be a brute-force (BF) algorithm, a vector space model (VSM) algorithm, or an edit distance similarity algorithm; this application does not impose any limitations on this.

[0078] S403, combine the first recommendation list and the second recommendation list to obtain the target recommendation list.

[0079] Optionally, the first recommendation list and the second recommendation list can be directly combined to obtain the target recommendation list. Alternatively, the interface components in the first recommendation list and the interface components in the second recommendation list can be mixed and arranged to obtain the target recommendation list.

[0080] In this embodiment, a first recommendation list can be quickly obtained based on the business scenario tags of each interface component in the first target range, the business scenario tags of the current interface component, and the time-series database. A second recommendation list can be quickly obtained based on the business scenario tags of each interface component in the second target range, the business scenario tags of the current interface component, and the time-series database. This improves the efficiency of obtaining the first and second recommendation lists, thereby improving the efficiency of combining the first and second recommendation lists to obtain the target recommendation list.

[0081] In the scenario described above, where the target range of the next interface component corresponding to the current interface component is determined based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, it is necessary to first determine whether there is a call chain in the knowledge graph with the same MD5 signature as the first call chain. In one embodiment, such as Figure 5 As shown, prior to S202 above, the method further includes:

[0082] S501, Determine whether there exists a call chain in the knowledge graph that has the same MD5 signature as the first call chain.

[0083] Optionally, in this embodiment, the hash values ​​of the MD5 signatures of each call chain in the knowledge graph can be compared with the hash value of the MD5 signature of the first call chain. Based on the comparison result, it can be determined whether there is a call chain in the knowledge graph with the same MD5 signature as the first call chain. 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 can be determined that there is a call chain in the knowledge graph with the same MD5 signature as the first call chain; 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 can be determined that there is a call chain in the knowledge graph with the same MD5 signature as the first call chain; or, if the comparison result does not include a call chain with 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 with the same MD5 signature as the first call chain.

[0084] S502, if it exists, then execute the step of determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavior data and the pre-built knowledge graph.

[0085] Optionally, in this embodiment, if there is a call chain in the knowledge graph that has the same MD5 signature as the first call chain, the above-mentioned step of determining the target range of the next interface component corresponding to the current interface component based on the first call chain in the behavior data and the pre-built knowledge graph can be executed; if there is no call chain in the knowledge graph that has the same MD5 signature as the first call chain, the first call chain in the current interface component can be stored in the knowledge graph to increase the richness of the call chains in the knowledge graph.

[0086] In this embodiment, by determining whether a call chain with the same MD5 signature as the first call chain exists in the knowledge graph, and if such a call chain exists, the step of determining the target range of the next interface component corresponding to the current interface component based on the first call chain in the behavior data and the pre-built knowledge graph is executed. This ensures the reliability of determining the target range of the next interface component corresponding to the current interface component based on the first call chain in the behavior data and the pre-built knowledge graph. Furthermore, if no call chain with the same MD5 signature as the first call chain exists in the knowledge graph, the information of the current interface component is stored in the knowledge graph, which can enrich the call chain richness in the knowledge graph.

[0087] In the scenario described above, where the target recommendation list for the next interface component corresponding to the current interface component is determined, a pre-built knowledge graph and time-series database are required. In one embodiment, such as... Figure 6 As shown, the above method also includes:

[0088] S601, obtain information about the interface components called when the low-code development platform generates distributed applications within a historical time period; the information includes the interface components called, the calling relationships between the called interface components, and the number of times the called interface components are called.

[0089] Optionally, in this embodiment, information about the interface components called by the low-code development platform when generating distributed applications within a historical time period can be obtained by pre-setting a capture code segment on the low-code development platform. For example, the capture code segment can be embedded in the front end of the low-code development platform, or it can be set in the computer device. Alternatively, third-party tools can be used to obtain information about the interface components called by the low-code development platform when generating distributed applications within a historical time period.

[0090] S602 constructs a knowledge graph using the invoked interface components as nodes and the calling relationships between the invoked interface components as edges.

[0091] Optionally, the knowledge graph can be constructed using the interface components called within a historical time period as nodes and the call relationships between the called interface components as edges. Optionally, in this embodiment, the attributes of the nodes in the knowledge graph may include information such as the application, cluster, or unit to which the interface component belongs, and the attributes of the edges in the knowledge graph may include the call direction, call count, business scenario tag, and call chain signature of the interface component.

[0092] S603: Construct a time-series database based on the number of times the invoked interface components are called.

[0093] Optionally, the number of times each interface component is called when the low-code development platform generates a distributed application can be obtained, and the number of times each interface component is called can be stored in a database to construct the aforementioned time-series database.

[0094] In this embodiment of the application, by generating information on the interface components called by the distributed application and the number of times the interface components are called within a historical time period through the low-code development platform, the knowledge graph and time series database can be accurately constructed, thereby improving the accuracy of the constructed knowledge graph and time series database.

[0095] To facilitate understanding by those skilled in the art, the method for determining the interface components provided in this application will be described in detail below. This method may include:

[0096] S1, obtain information about the interface components called by the low-code development platform when generating distributed applications within the historical time period; the information includes the called interface components, the calling relationships between the called interface components, and the number of times the called interface components are called.

[0097] S2, construct the knowledge graph by using the invoked interface components as nodes and the calling relationships between the invoked interface components as edges.

[0098] S3, construct the time-series database based on the number of times the interface component is called.

[0099] S4, Obtain behavioral data; the behavioral data is generated by the low-code development platform when generating the target distributed application.

[0100] S5, determine whether there exists a call chain in the knowledge graph that has the same MD5 signature as the first call chain.

[0101] S6, if so, based on the second call chain in the knowledge graph that has the same MD5 signature as the first call chain, determine the first target range where the next interface component corresponding to the current interface component is located; based on the third call chain in the knowledge graph that has a different MD5 signature from the first call chain, determine the second target range where the next interface component corresponding to the current interface component is located.

[0102] S7, the interface components whose business scenario tags are the same as those of the current interface component in the first target range are identified as the first target interface components, and a first initial recommendation list is determined from the first target interface components based on the number of times the first target interface components are called in the time series database.

[0103] S8, the interface components whose business scenario tags are different from those of the current interface component in the first target range are identified as the second target interface components, and a second initial recommendation list is determined from the second target interface components based on the number of times the second target interface components are called in the time series database.

[0104] S10, combine the first initial recommendation list and the second initial recommendation list to obtain the first recommendation list.

[0105] S11, the interface components whose business scenario tags are the same as those of the current interface component in the second target range are identified as the third target interface components, and a third initial recommendation list is determined from the third target interface components based on the number of times the third target interface components are called in the time series database.

[0106] S12, the interface components whose business scenario tags are different from those of the current interface component in the second target range are identified as the fourth target interface components. Based on the number of times the fourth target interface components are called in the time series database, a fourth initial recommendation list is determined from the fourth target interface components.

[0107] S13, combine the third initial recommendation list and the fourth initial recommendation list to obtain the second recommendation list.

[0108] S14, combine the first recommendation list and the second recommendation list to obtain the target recommendation list.

[0109] The implementation principles in S1-S14 above can be found in the description in the above embodiments, and will not be repeated here.

[0110] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0111] Based on the same inventive concept, this application also provides a loss determination apparatus for implementing the loss determination method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more loss determination apparatus embodiments provided below can be found in the limitations of the loss determination method described above, and will not be repeated here.

[0112] In one embodiment, such as Figure 7 As shown, an interface component determination device is provided, including: a first acquisition module 11, used to acquire behavioral data; the behavioral data is generated by a low-code development platform to generate a target distributed application;

[0113] The first determining module 12 is used to determine the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavior data and the pre-built knowledge graph;

[0114] The second determining module 13 is used to determine the target recommendation list of the next interface component corresponding to the current interface component based on the interface components in the target range and the pre-built time series database.

[0115] The interface component determination device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0116] In one embodiment, such as Figure 8 As shown, the first determining module 12 includes:

[0117] The first determining unit 121 is used to determine the first target range based on the second call chain in the knowledge graph that has the same MD5 signature as the first call chain;

[0118] The second determining unit 122 is used to determine the second target range based on a third call chain in the knowledge graph that has a different MD5 signature from the first call chain.

[0119] The interface component determination device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0120] In one embodiment, continue to refer to Figure 8 As shown, the second determining module 13 includes:

[0121] The first acquisition unit 131 is used to acquire a first recommendation list based on the business scenario tags of each interface component in the first target range, the business scenario tags of the current interface component, and the time series database.

[0122] The second acquisition unit 132 acquires a second recommendation list based on the business scenario tags of each interface component in the second target range, the business scenario tags of the current interface component, and the time-series database.

[0123] The third determining unit 133 combines the first recommendation list and the second recommendation list to obtain the target recommendation list.

[0124] The interface component determination device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0125] In one embodiment, the first acquisition unit 131 is specifically used for:

[0126] Interface components whose business scenario tags are the same as those of the current interface component within the first target scope are identified as first target interface components. A first initial recommendation list is determined from the first target interface components based on the number of calls to these components in the time-series database. Interface components whose business scenario tags are different from those of the current interface component within the first target scope are identified as second target interface components. A second initial recommendation list is determined from the second target interface components based on the number of calls to these components in the time-series database. The first and second initial recommendation lists are combined to obtain the first recommendation list.

[0127] The interface component determination device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0128] In one embodiment, the second acquisition unit 132 is specifically used for:

[0129] Interface components whose business scenario tags are the same as those of the current interface component within the second target scope are identified as third target interface components. A third initial recommendation list is determined from the third target interface components based on the number of calls to these third target interface components in the time-series database. Interface components whose business scenario tags are different from those of the current interface component within the second target scope are identified as fourth target interface components. A fourth initial recommendation list is determined from the fourth target interface components based on the number of calls to these fourth target interface components in the time-series database. The third initial recommendation list and the initial recommendation list are combined to obtain the second recommendation list.

[0130] The interface component determination device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0131] In one embodiment, continue to refer to Figure 8 As shown, the above-mentioned device further includes: a third determining module 14 and an execution module 15; wherein:

[0132] The third determining module 14 is used to determine whether there is a call chain in the knowledge graph that has the same MD5 signature as the first call chain.

[0133] The execution module 15 is used to execute the step of determining the target range of the next interface component corresponding to the current interface component based on the first call chain in the behavior data and the pre-built knowledge graph if there is a call chain in the knowledge graph with the same MD5 signature as the first call chain.

[0134] The interface component determination device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0135] In one embodiment, continue to refer to Figure 8 As shown, the above-mentioned device further includes: a second acquisition module 16, a first construction module 17, and a second construction module 18; wherein:

[0136] The second acquisition module 16 is used to acquire information about the interface components called by the low-code development platform when generating distributed applications during the historical time period. The information includes the interface components called, the calling relationships between the interface components called, and the number of times the interface components are called.

[0137] The first construction module 17 is used to construct the knowledge graph by using the called interface components as nodes and the calling relationships between the called interface components as edges.

[0138] The second construction module 18 is used to construct the time series database based on the number of times the interface component is called.

[0139] The interface component determination device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0140] Each module in the aforementioned interface component determining device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0141] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0142] Acquire behavioral data; behavioral data is generated by the low-code development platform when generating the target distributed application.

[0143] Based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, determine the target range of the next interface component corresponding to the current interface component;

[0144] Based on the interface components within the target scope and a pre-built time-series database, a target recommendation list for the next interface component corresponding to the current interface component is determined; wherein, the knowledge graph and the time-series database are constructed based on information about the interface components called when the low-code development platform generates distributed applications within a historical time period.

[0145] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, including:

[0146] The first target range is determined based on the second call chain in the knowledge graph that has the same MD5 signature as the first call chain;

[0147] The second target range is determined based on the third call chain in the knowledge graph whose MD5 signature is different from that of the first call chain.

[0148] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining a target recommendation list of the next interface component corresponding to the current interface component based on the interface components in the target scope and a pre-built time-series database, including:

[0149] Based on the business scenario tags of each interface component in the first target scope, the business scenario tags of the current interface component, and the time-series database, obtain the first recommendation list;

[0150] Based on the business scenario tags of each interface component in the second target scope, the business scenario tags of the current interface component, and the time series database, obtain the second recommendation list;

[0151] The first recommendation list and the second recommendation list are combined to obtain the target recommendation list.

[0152] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a first recommendation list based on the business scenario tags of the call chains of each interface component in the first target scope, the business scenario tags of the current interface component, and a time-series database, including:

[0153] The interface components whose business scenario labels are the same as the business scenario labels of the current interface component are identified as the first target interface components.

[0154] Based on the number of times the first target interface component is called in the time series database, the first initial recommendation list is determined from the first target interface component;

[0155] The interface components whose business scenario labels are different from those of the current interface component in the first target scope are identified as the second target interface components;

[0156] Based on the number of calls to the second target interface component in the time series database, a second initial recommendation list is determined from the second target interface component;

[0157] The first initial recommendation list and the second initial recommendation list are combined to obtain the first recommendation list.

[0158] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a second recommendation list based on the business scenario tags of each interface component in the second target scope, the business scenario tags of the current interface component, and a time-series database, including:

[0159] The interface components whose business scenario labels are the same as those of the current interface component in the second target scope are identified as the third target interface components;

[0160] Based on the number of calls to the third target interface component in the time series database, the third initial recommendation list is determined from the third target interface component;

[0161] The interface components whose business scenario labels are different from those of the current interface component in the second target scope are identified as the fourth target interface components;

[0162] Based on the number of calls to the fourth target interface component in the time series database, the fourth initial recommendation list is determined from the fourth target interface component;

[0163] The third and fourth initial recommendation lists are combined to obtain the second recommendation list.

[0164] In one embodiment, before the processor executes the computer program, the method further includes the following steps: before determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, the method further includes:

[0165] Determine whether there exists a call chain in the knowledge graph that has the same MD5 signature as the first call chain;

[0166] If it exists, then execute the step of determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph.

[0167] In one embodiment, the processor, while executing a computer program, further performs the following steps: the method further includes:

[0168] Obtain information about the interface components called when the low-code development platform generates distributed applications within a historical time period; the information includes the interface components called, the calling relationships between the called interface components, and the number of times the called interface components are called;

[0169] A knowledge graph is constructed using the invoked interface components as interface components and the calling relationships between the invoked interface components as edges;

[0170] A time-series database is built based on the number of times the API components are called.

[0171] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0172] Acquire behavioral data; behavioral data is generated by the low-code development platform when generating the target distributed application.

[0173] Based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, determine the target range of the next interface component corresponding to the current interface component;

[0174] Based on the interface components within the target scope and a pre-built time-series database, a target recommendation list for the next interface component corresponding to the current interface component is determined; wherein, the knowledge graph and the time-series database are constructed based on information about the interface components called when the low-code development platform generates distributed applications within a historical time period.

[0175] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, including:

[0176] The first target range is determined based on the second call chain in the knowledge graph that has the same MD5 signature as the first call chain;

[0177] The second target range is determined based on the third call chain in the knowledge graph whose MD5 signature is different from that of the first call chain.

[0178] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining a target recommendation list of the next interface component corresponding to the current interface component based on the interface components in the target scope and a pre-built time-series database, including:

[0179] Based on the business scenario tags of each interface component in the first target scope, the business scenario tags of the current interface component, and the time-series database, obtain the first recommendation list;

[0180] Based on the business scenario tags of each interface component in the second target scope, the business scenario tags of the current interface component, and the time series database, obtain the second recommendation list;

[0181] The first recommendation list and the second recommendation list are combined to obtain the target recommendation list.

[0182] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a first recommendation list based on the business scenario tags of the call chains of each interface component in the first target scope, the business scenario tags of the current interface component, and a time-series database, including:

[0183] The interface components whose business scenario labels are the same as the business scenario labels of the current interface component are identified as the first target interface components.

[0184] Based on the number of times the first target interface component is called in the time series database, the first initial recommendation list is determined from the first target interface component;

[0185] The interface components whose business scenario labels are different from those of the current interface component in the first target scope are identified as the second target interface components;

[0186] Based on the number of calls to the second target interface component in the time series database, a second initial recommendation list is determined from the second target interface component;

[0187] The first initial recommendation list and the second initial recommendation list are combined to obtain the first recommendation list.

[0188] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a second recommendation list based on the business scenario tags of each interface component in the second target scope, the business scenario tags of the current interface component, and a time-series database, including:

[0189] The interface components whose business scenario labels are the same as those of the current interface component in the second target scope are identified as the third target interface components;

[0190] Based on the number of calls to the third target interface component in the time series database, the third initial recommendation list is determined from the third target interface component;

[0191] The interface components whose business scenario labels are different from those of the current interface component in the second target scope are identified as the fourth target interface components;

[0192] Based on the number of calls to the fourth target interface component in the time series database, the fourth initial recommendation list is determined from the fourth target interface component;

[0193] The third and fourth initial recommendation lists are combined to obtain the second recommendation list.

[0194] In one embodiment, before the processor executes the computer program, the method further includes the following steps: before determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, the method further includes:

[0195] Determine whether there exists a call chain in the knowledge graph that has the same MD5 signature as the first call chain;

[0196] If it exists, then execute the step of determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph.

[0197] In one embodiment, the processor, while executing a computer program, further performs the following steps: the method further includes:

[0198] Obtain information about the interface components called when the low-code development platform generates distributed applications within a historical time period; the information includes the interface components called, the calling relationships between the called interface components, and the number of times the called interface components are called;

[0199] A knowledge graph is constructed using the invoked interface components as interface components and the calling relationships between the invoked interface components as edges;

[0200] A time-series database is built based on the number of times the API components are called.

[0201] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0202] Acquire behavioral data; behavioral data is generated by the low-code development platform when generating the target distributed application.

[0203] Based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, determine the target range of the next interface component corresponding to the current interface component;

[0204] Based on the interface components within the target scope and a pre-built time-series database, a target recommendation list for the next interface component corresponding to the current interface component is determined; wherein, the knowledge graph and the time-series database are constructed based on information about the interface components called when the low-code development platform generates distributed applications within a historical time period.

[0205] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, including:

[0206] The first target range is determined based on the second call chain in the knowledge graph that has the same MD5 signature as the first call chain;

[0207] The second target range is determined based on the third call chain in the knowledge graph whose MD5 signature is different from that of the first call chain.

[0208] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining a target recommendation list of the next interface component corresponding to the current interface component based on the interface components in the target scope and a pre-built time-series database, including:

[0209] Based on the business scenario tags of each interface component in the first target scope, the business scenario tags of the current interface component, and the time-series database, obtain the first recommendation list;

[0210] Based on the business scenario tags of each interface component in the second target scope, the business scenario tags of the current interface component, and the time series database, obtain the second recommendation list;

[0211] The first recommendation list and the second recommendation list are combined to obtain the target recommendation list.

[0212] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a first recommendation list based on the business scenario tags of the call chains of each interface component in the first target scope, the business scenario tags of the current interface component, and a time-series database, including:

[0213] The interface components whose business scenario labels are the same as the business scenario labels of the current interface component are identified as the first target interface components.

[0214] Based on the number of times the first target interface component is called in the time series database, the first initial recommendation list is determined from the first target interface component;

[0215] The interface components whose business scenario labels are different from those of the current interface component in the first target scope are identified as the second target interface components;

[0216] Based on the number of calls to the second target interface component in the time series database, a second initial recommendation list is determined from the second target interface component;

[0217] The first initial recommendation list and the second initial recommendation list are combined to obtain the first recommendation list.

[0218] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a second recommendation list based on the business scenario tags of each interface component in the second target scope, the business scenario tags of the current interface component, and a time-series database, including:

[0219] The interface components whose business scenario labels are the same as those of the current interface component in the second target scope are identified as the third target interface components;

[0220] Based on the number of calls to the third target interface component in the time series database, the third initial recommendation list is determined from the third target interface component;

[0221] The interface components whose business scenario labels are different from those of the current interface component in the second target scope are identified as the fourth target interface components;

[0222] Based on the number of calls to the fourth target interface component in the time series database, the fourth initial recommendation list is determined from the fourth target interface component;

[0223] The third and fourth initial recommendation lists are combined to obtain the second recommendation list.

[0224] In one embodiment, before the processor executes the computer program, the method further includes the following steps: before determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, the method further includes:

[0225] Determine whether there exists a call chain in the knowledge graph that has the same MD5 signature as the first call chain;

[0226] If it exists, then execute the step of determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph.

[0227] In one embodiment, the processor, while executing a computer program, further performs the following steps: the method further includes:

[0228] Obtain information about the interface components called when the low-code development platform generates distributed applications within a historical time period; the information includes the interface components called, the calling relationships between the called interface components, and the number of times the called interface components are called;

[0229] A knowledge graph is constructed using the invoked interface components as interface components and the calling relationships between the invoked interface components as edges;

[0230] A time-series database is built based on the number of times the API components are called.

[0231] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0232] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. 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). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0233] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0234] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining an interface component, characterized in that, The method includes: Acquire behavioral data; the behavioral data is generated by the low-code development platform when generating the target distributed application, and the behavioral data includes the current interface component name, the parent interface component name, the business scenario tag of the target distributed application, and the call chain ID; Based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, the target range of the next interface component corresponding to the current interface component is determined. The pre-built knowledge graph is a graph database constructed with the interface components called when the low-code development platform generates a distributed application within a historical time period as nodes and the call relationships between the called interface components as edges. The attributes of the nodes in the knowledge graph include the application, cluster, or unit information to which the interface component belongs, and the attributes of the edges include the call direction, call count, business scenario label, and call chain signature of the interface component. The first call chain is a call chain formed based on the call relationship between the current interface component and the previously called interface components. The target range is the range of the call chain of the interface component in the knowledge graph that matches the target distributed application being generated. Based on the interface components within the target scope and a pre-built time-series database, a target recommendation list for the next interface component corresponding to the current interface component is determined. The pre-built time-series database is a database consisting of the number of times interface components are called when generating distributed applications based on a low-code development platform within a historical time period, used to statistically analyze the number of times interface components are called under different dimensions over a period of time. The information of the interface components includes the called interface components, the call relationships between the called interface components, and the number of times the called interface components are called.

2. The method according to claim 1, characterized in that, The target scope includes a first target scope and a second target scope; determining the target scope of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph includes: Based on the second call chain in the knowledge graph that has the same MD5 signature as the first call chain, the first target range is determined, and the first target range is used to represent the set of candidate interface components that completely match the structure of the first call chain. Based on the third call chain in the knowledge graph whose MD5 signature is different from that of the first call chain, the second target range is determined. The second target range is used to represent a set of candidate interface components that do not completely match the structure of the first call chain but belong to the same business scenario.

3. The method according to claim 2, characterized in that, The step of determining the target recommendation list of the next interface component corresponding to the current interface component based on the interface components in the target range and the pre-built time-series database includes: Based on the business scenario tags of each interface component in the first target range, the business scenario tag of the current interface component, and the time-series database, a first recommendation list is obtained. The business scenario tag is a unique identifier used to identify the business scenario of the distributed application, which is obtained by combining the business scenario requirements with the static and dynamic characteristics of the target object through an abstract inductive algorithm. A second recommendation list is obtained based on the business scenario tags of each interface component in the second target range, the business scenario tags of the current interface component, and the time-series database. The first recommendation list and the second recommendation list are combined to obtain the target recommendation list. The first recommendation list is obtained by grouping and filtering based on the similarities and differences between the business scenario tags of each interface component in the first target range and the business scenario tags of the current interface component. The second recommendation list is obtained by grouping and filtering based on the similarities and differences between the business scenario tags of each interface component in the second target range and the business scenario tags of the current interface component.

4. The method according to claim 3, characterized in that, The step of obtaining the first recommendation list based on the business scenario tags of the call chains of each interface component in the first target range, the business scenario tags of the current interface component, and the time-series database includes: The interface component whose business scenario label is the same as that of the current interface component in the first target range is identified as the first target interface component; Based on the number of times the first target interface component is called in the time series database, a first initial recommendation list is determined from the first target interface component; The interface components whose business scenario labels are different from those of the current interface component in the first target range are identified as the second target interface components; Based on the number of times the second target interface component is called in the time series database, a second initial recommendation list is determined from the second target interface component; The first initial recommendation list and the second initial recommendation list are combined to obtain the first recommendation list.

5. The method according to claim 3, characterized in that, The step of obtaining the second recommendation list based on the business scenario tags of each interface component in the second target range, the business scenario tag of the current interface component, and the time-series database includes: The interface components whose business scenario labels are the same as those of the current interface component in the second target range are identified as the third target interface components; Based on the number of times the third target interface component is called in the time series database, a third initial recommendation list is determined from the third target interface component; The interface component whose business scenario label is different from that of the current interface component in the second target range is identified as the fourth target interface component; Based on the number of calls to the fourth target interface component in the time series database, a fourth initial recommendation list is determined from the fourth target interface component; The third initial recommendation list and the fourth initial recommendation list are combined to obtain the second recommendation list.

6. The method according to claim 1, characterized in that, Before determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph, the method further includes: Determine whether there exists a call chain in the knowledge graph that has the same MD5 signature as the first call chain; If it exists, then execute the step of determining the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavior data and the pre-built knowledge graph; If it does not exist, the first call chain is stored in the knowledge graph, and the target range is determined from the knowledge graph based on the similarity matching method.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: Obtain information about the interface components called by the low-code development platform when generating distributed applications within the historical time period; the information includes the called interface components, the calling relationships between the called interface components, and the number of times the called interface components are called; The knowledge graph is constructed using the invoked interface components as nodes and the invocation relationships between the invoked interface components as edges. The time-series database is constructed based on the number of times the interface components are called.

8. A device for determining an interface component, characterized in that, The device includes: The first acquisition module is used to acquire behavioral data; the behavioral data is generated by the low-code development platform to generate the target distributed application, and the behavioral data includes the current interface component name, the parent interface component name, the business scenario tag of the target distributed application, and the call chain ID. The first determining module is used to determine the target range of the next interface component corresponding to the current interface component based on the first call chain of the current interface component in the behavioral data and the pre-built knowledge graph. The pre-built knowledge graph is a graph database constructed with the interface components called when the low-code development platform generates a distributed application within a historical time period as nodes and the call relationships between the called interface components as edges. The attributes of the nodes in the knowledge graph include the application, cluster or unit information to which the interface component belongs, and the attributes of the edges include the call direction, call count, business scenario label and call chain signature of the interface component. The first call chain is a call chain formed based on the call relationship between the current interface component and the previously called interface components. The target range is the range of the call chain of the interface component in the knowledge graph that matches the target distributed application being generated. The second determining module is used to determine a target recommendation list of the next interface component corresponding to the current interface component based on the interface components in the target range and a pre-built time-series database. The pre-built time-series database is a database consisting of the number of times the interface components are called when generating distributed applications based on the low-code development platform within a historical time period, and is used to count the number of times the interface components are called under different dimensions within a period of time. The information of the interface components includes the called interface components, the call relationship between the called interface components, and the number of times the called interface components are called.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements 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, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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