CN112650482A

CN112650482A - Recommendation method and related device for logic component

Info

Publication number: CN112650482A
Application number: CN202011566230.6A
Authority: CN
Inventors: 陈立忠; 阮志坚; 杨明明; 彭后萍; 张吕炯
Original assignee: Zhejiang Lanzhuo Industrial Internet Information Technology Co ltd
Current assignee: Zhejiang Lanzhuo Industrial Internet Information Technology Co ltd
Priority date: 2020-12-25
Filing date: 2020-12-25
Publication date: 2021-04-13

Abstract

The application provides a recommendation method and a related device for a logic component, wherein the method comprises the following steps: extracting target information under the condition that a logic component is added to a visual IDE interface by a user to be recommended is received; acquiring the information of the knowledge graph under the condition that the knowledge graph is constructed; determining a logic component added by a target person after the currently added logic component is added from the knowledge graph to obtain a candidate recommended logic component of the currently added logic component; determining a logic component to be recommended of the currently added logic component at least according to the candidate recommendation logic component; and taking the logic component to be recommended as prompt information and displaying the prompt information on a visual IDE interface. Because the correlation degree between the user to be recommended and the colleagues of the same company is higher, the logic component to be recommended, which is prompted by the application, is a logic component which the user to be recommended actually wants to add after the currently added logic component, so that the development efficiency of the user can be improved.

Description

Recommendation method and related device for logic component

Technical Field

The present application relates to the field of data recommendation processing, and in particular, to a method and a related apparatus for recommending a logic component.

Background

When an Application (Application) is developed on a visual IDE, a developer can complete design and development by operating a logic component on a visual IDE interface according to own requirements.

At present, a visual IDE provides many atomic components, such as graphs, algorithms, processes, and the like, and developers combine and assemble these logical components according to their own needs to complete the design and development of applications. Because the use mode between the logic components is complicated, for example, some graphic association components can be nested for use. Thus, each time a logical component is combined during the development process, the developer is required to be very familiar with the visual IDE, understand the definitions of the logical components, and combine the logical components.

Therefore, the development and learning costs for developers are high, resulting in low development efficiency.

Disclosure of Invention

The application provides a recommendation method and a related device for a logic component, and aims to solve the problem of low development efficiency of application development in a visual IDE.

In order to achieve the above object, the present application provides the following technical solutions:

the application provides a recommendation method of a logic component, which comprises the following steps:

extracting target information under the condition that a logic component is added to a visual IDE interface by a user to be recommended is received; the target information at least includes: the information used for representing the user to be recommended, the information of the company to which the user to be recommended belongs and the information of the currently added logic component;

acquiring the information of the knowledge graph under the condition that the knowledge graph is constructed; the knowledge-graph includes at least: the information is used for representing a historical user, the information is used for representing a company to which the historical user belongs, and the information of the logical components and the adding sequence added by the historical user in the process of logically configuring the visual IDE;

determining the logic components added by the target person after the currently added logic components are added from the knowledge graph to obtain candidate recommended logic components of the currently added logic components; the target person includes: the user to be recommended and the colleague of the same company;

determining a logic component to be recommended of the currently added logic component at least according to the candidate recommended logic component;

and taking the logic component to be recommended as prompt information and displaying the prompt information on the visual IDE interface.

Optionally, the target information further includes: information of the industry of the company to which the user to be recommended belongs; the knowledge-graph further comprises: information for characterizing an industry of a company to which the historical user belongs; the target person further comprises: and the same-industry personnel of the user to be recommended.

Optionally, after determining, from the knowledge graph, a logic component that is added by a target person after the currently added logic component is added, and obtaining a candidate recommended logic component of the currently added logic component, the method further includes:

obtaining a recommended score of each candidate recommendation logic component; the recommended score for any candidate recommendation logic component represents: a recommended priority of the candidate recommendation logic component; wherein, the larger the recommended score is, the higher the recommended priority is;

the determining the logic component to be recommended of the currently added logic component at least according to the candidate recommendation logic component comprises:

and under the condition that the total number of the candidate recommendation logic components is greater than a preset number, determining the preset number of candidate recommendation logic components from the candidate recommendation logic components as the to-be-recommended logic components according to the sequence of the recommended scores from high to low.

Optionally, the knowledge-graph further comprises: presetting candidate recommending logic components and recommended scores of the logic components; wherein the recommended score of any one of the preset candidate recommendation logic components of any one of the preset logic components represents: the priority of the preset candidate recommendation logic component being recommended; wherein, the larger the recommended score is, the higher the priority is;

under the condition that the total number of the candidate recommended logic components is not greater than the preset number, determining a preset candidate recommended logic component corresponding to the currently added logic component from preset candidate recommended logic components of the preset logic components to obtain a supplementary candidate recommended logic component and a recommended score of the currently added logic component;

and determining the preset number of candidate recommendation logic components as the logic components to be recommended according to the sequence of the recommended scores of all candidate recommendation logic components of the currently added logic components from high to low.

Optionally, the method further includes: and under the condition that a knowledge graph is not constructed, determining a candidate recommended logic component of the currently added logic component from preset candidate recommended logic components of preset logic components.

Optionally, the process of constructing the knowledge graph includes:

acquiring logic component information, user attribute information and user behavior data for constructing a knowledge graph;

extracting entities, entity relationships, attributes and attribute values from the logic component information, the user attribute information and the user behavior data;

constructing a triple according to the entity, the entity relationship, the attribute and the attribute value;

and constructing the knowledge graph according to the triples.

Optionally, after the constructing the knowledge graph according to the triples, the method further includes:

under the condition that a new logic component is detected to be added in the visual IDE interface, determining the logic component with the similarity degree between the knowledge graph and the new logic component larger than a preset threshold value as a target logic component;

establishing a link between the target logical component and the new logical component in the knowledge-graph.

The present application further provides a recommendation apparatus for a logic component, including:

the extraction module is used for extracting target information under the condition that a logic component is added to a visual IDE interface by a user to be recommended; the target information at least includes: the information used for representing the user to be recommended, the information of the company to which the user to be recommended belongs and the information of the currently added logic component;

the first acquisition module is used for acquiring the information of the knowledge graph under the condition that the knowledge graph is constructed; the knowledge-graph includes at least: the information is used for representing a historical user, the information is used for representing a company to which the historical user belongs, and the information of the logical components and the adding sequence added by the historical user in the process of logically configuring the visual IDE;

the first determination module is used for determining the logic components added by the target person after the currently added logic components are added from the knowledge graph to obtain candidate recommended logic components of the currently added logic components; the target person includes: the user to be recommended and the colleague of the same company;

a second determining module, configured to determine, at least according to the candidate recommended logic component, a logic component to be recommended of the currently added logic component;

and the display module is used for displaying the logic component to be recommended on the visual IDE interface as prompt information.

The application also provides a storage medium, which comprises a stored program, wherein the program executes any one of the recommendation methods of the logic component.

The application also provides a device, which comprises at least one processor, at least one memory connected with the processor, and a bus; the processor and the memory complete mutual communication through the bus; the processor is used for calling the program instructions in the memory so as to execute any recommended method of the logic component.

The method and the device for recommending the logic component extract target information when receiving that a user to be recommended adds the logic component in a visual IDE interface, wherein the target information at least comprises the following steps: the information used for representing the user to be recommended, the information of the company to which the user to be recommended belongs and the information of the currently added logic component. And acquiring the information of the knowledge graph under the condition that the knowledge graph is constructed.

Since the knowledge-graph includes at least: the system comprises information used for representing historical users, information used for representing companies to which the historical users belong, and information of logical components and adding sequences added by the historical users in the process of carrying out logical configuration on a visual IDE. Therefore, the user to be recommended and the co-company can be determined from the knowledge graph, and the logic components added after the currently added logic components are added in the process of using the visual IDE historically. Because the correlation degree between the user to be recommended and the user to be recommended is higher, the candidate recommendation logic component is the logic component which the user to be recommended actually wants to add after the currently added logic component. Therefore, the logic component to be recommended determined according to the candidate recommendation logic component is the logic component which the user to be recommended actually wants to add after the currently added logic component.

Therefore, the logic component to be recommended is used as prompt information and is displayed on the visual IDE interface, and for a user of the visual IDE, the prompt information contains the logic component recommended to be added next step, and the recommended logic component is high in accuracy, so that the development efficiency of the user can be improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic view of an application scenario disclosed in an embodiment of the present application;

FIG. 2 is a flow chart illustrating a method for recommending logical components according to an embodiment of the present disclosure;

FIG. 3 is an exemplary diagram of a knowledge-graph as disclosed in an embodiment of the present application;

FIG. 4 is a schematic diagram of a low-dimensional vector structure according to an embodiment of the present disclosure;

FIG. 5 is a flow chart illustrating a method for recommending another logic component disclosed in an embodiment of the present application;

FIG. 6 is a schematic structural diagram of a recommendation apparatus for logic components according to an embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of an apparatus disclosed in an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application. Fig. 1 shows a visualization IDE platform that includes IDE visualization software, a log system connected to the software, a building apparatus connected to the log system, and a recommendation apparatus connected to the building apparatus.

The visual IDE software is used for application development of a user, and specifically, logical configuration of logical components can be performed. The log system is used for storing logs generated by the visualization IDE software in the using process. The construction device can perform analysis processing according to the data stored by the log system. And the recommending device is used for recommending the logic components which can be added in the next step to the user after the user adds the logic components in the interface of the visual IDE software based on the analysis result of the constructing device.

In the embodiment of the application, data embedding is performed in the visualization IDE software in advance, and the data embedding collects use data generated locally in the process of using the visualization IDE by a user in the process of using the visualization IDE by a developer. The behavior data of the users who agree to the data sharing can be used as the user behavior data for subsequent analysis so as to provide better services for developers.

In an embodiment of the present application, visualizing an IDE refers to: the visual integrated development environment can provide an integrated development software service set for the developer to design and develop. Configuration: configuration is a process of combining a combined configuration of applications by performing visual programming on a visual IDE by dragging and grasping different components. A configuration period: the configuration phase is the state of development on the visualization IDE. Logic configuration: the method aims to meet the configuration requirements of complex data calculation, complex front-end and back-end data calculation and synchronization, and a logic flow is rapidly configured on an interface.

Fig. 2 is a process for constructing a knowledge graph according to an embodiment of the present application, where an execution subject is the construction apparatus in fig. 1, and the process may include the following steps:

s201, acquiring data for constructing the knowledge graph.

In step, data may be obtained from a log system, wherein the obtained data may include: logic component information, user attribute information, user behavior data, and preset candidate recommended logic components of the preset logic components.

Wherein, the logic component information refers to: information of logical components present in the IDE platform is visualized. May include the identity, name and type of the logical component.

As an example, in the present embodiment, the logical component information may be represented by a logical component table, as shown in table 1.

TABLE 1

Logical component id	Logical component name	Component type
			8001	Component A	Summing
8002	Component B	Deposit value
			8003	Component C	Value taking

The user attribute information means: the basic attribute information of the user may include: information for characterizing the historical users and information for characterizing companies to which the historical users belong. The information for characterizing the historical user may be any one of an identifier, a name and an employee identifier of the historical user. The historical users refer to users using the visual IDE in the historical time period, and for convenience of description, the users are simply referred to as users, that is, the users appearing in the following description all refer to the historical users.

The information for characterizing the company to which the user belongs may be any one of an identifier and a name of the company to which the user belongs.

Optionally, in this embodiment, the user attribute information may further include: information of an industry to which a company to which the user belongs. The information of the company-owned industry may be any one of an identifier and a name of the company-owned industry.

As an example, in the present embodiment, the user attribute information may be represented by a user table, a company table, an employee table, and an industry table. The user table is shown in table 2, the company table is shown in table 3, the employee table is shown in table 4, and the industry table is shown in table 5.

TABLE 2

User id	User name
		1	Jerry
2	Tom
		3	Tony
4	JacK

TABLE 3

Company id	Company name	The related industries
			1	xxxA	1，2
2	xxxB	2，3
			3	xxxC	4

TABLE 4

Company id	Employee id	User id
			1	1	1
1	2	2
			2	1	3
3	1	4

TABLE 5

Industry id	Trade name
		1	Metal
2	Chemical engineering
		3	Wood material
4	Textile fabric

In this embodiment, the user behavior data may include: user identification (e.g., user ID), timestamp, IP, OS, device type, and behavioral details. The action details may include: an application identification (e.g., ID), a logical configuration identification (e.g., ID), an action, a logical component identification (e.g., ID), and a pre-logical component identification (e.g., ID).

In order to visually display the format of the user behavior data, the present embodiment is given as shown in table 6.

TABLE 6

Table 6 gives three pieces of user behavior data, where each piece of user behavior data includes: user identification, timestamp, IP address, operating system type, device type, and behavioral details.

As can be seen from the content contained in the user behavior data, in this embodiment, the user behavior data contains: logical components added during user use of the visual IDE and order of addition. The adding sequence refers to the information of the logic components added next after the user adds the logic components each time.

In this embodiment, in order to visually display the preset candidate recommended logic components of the preset logic components, the logic component recommended data table shown in table 7 is provided in this embodiment.

TABLE 7

Recommendation id	Front-end component id	Component id	sortscore
				1	8001	8002	100
2	8001	8003	91
				3	8003	8002	88

In table 7, three groups of logical components are given, each group of logical components is configured with a recommendation identifier (id), which is 1, 2, and 3 respectively. The column of the preposed logic components corresponds to the preset logic components, and the column of the components refers to candidate recommended logic components corresponding to each preset logic component. In table 7, a recommended score (SortScore) is also shown for each set of logical components.

It should be noted that, in this embodiment, the type information in the logic component and the preset candidate recommended logic component of the preset logic component are optional information for constructing the knowledge graph.

In this embodiment, a process of constructing a knowledge graph is described by taking, as an example, that the acquired data includes type information of a logic component and a preset candidate recommended logic component of a preset logic component.

As can be seen from the data shown in tables 1 to 6, the data obtained in this step has a certain data structure.

S202, extracting entities, entity relations and entity attribute values from the acquired data.

In this embodiment, the entities may include: user entities, logical component entities, user behavior entities, corporate entities, and industry entities.

Because the data acquired in this embodiment has a certain data structure, the manner for the computer to identify the entity, the entity relationship, the attribute, and the attribute value can be determined according to the data structure, where the specific determination manner is the prior art and is not described herein again.

And S203, constructing the triples according to the extracted entities, entity relationships and entity attribute values.

In this embodiment, from the extracted entities, entity relationships, and entity attribute values, data satisfying the entity-relationship-entity is taken as a triple. And using the data meeting the entity-attribute value structure as the triple.

And S204, constructing a knowledge graph according to the triples.

In this embodiment, a specific implementation manner of this step is the prior art, and is not described herein again.

In the present embodiment, in order to visually demonstrate the knowledge graph constructed in the present embodiment, the knowledge graph shown in fig. 3 is given in the present embodiment. As can be seen from fig. 3, the companies "Jerry" and "Tom" are "XXXA" companies, "XXXA" belongs to the metal and chemical industries, "Tony" is "XXXB" company, "XXXB" is the wood industry, "Jack" is "XXXC" and "XXXC" is the textile industry.

"Tom" created the component A record, and after creating the component A record, the current component A was added. It follows from the historical behavior log that after component A is added, there are 18 times that downstream components (component C) are added. It follows from the historical behavior log that after component A is added, there are 30 times that downstream components (component B) are added. According to the system preset component recommendation data, after adding component a, the ranking value (recommended score) of adding component C is 91. According to the system preset component recommendation data, after adding component a, the ranking value of adding component B is 77. According to the system preset component recommendation data, after adding component C, the rank value of adding component B is 88.

As can also be seen in fig. 3, Tom has also created a component B record, and after the component B record is created, component B is added. In fig. 3, the type of component a is summation, the type of component B is storage, and the type of component C is value.

In practice, as the number of times of using the visualization IDE increases, users increase, and user attribute information, logic component information, and user behavior data all increase, so the knowledge graph constructed in this embodiment also increases information as the acquired data increases. That is, in this embodiment, the knowledge graph is updated as the number of data acquired by the user increases.

The process of adding new logical components in the visualization IDE software is as follows S205 to S206.

S205, under the condition that a new logic component is added in the visual IDE interface, determining the logic component with the similarity degree between the knowledge graph and the new logic component larger than a preset threshold value as a target logic component.

In this embodiment, since the logic component includes information such as type and function, in this step, the similarity between the new logic component and the logic component in the knowledge graph can be calculated according to the information such as type and function of the logic component. And taking the logic component with the similarity larger than a preset threshold value with the new logic component in the knowledge graph as a target logic component.

S206, establishing the relation between the target logic component and the new logic component in the knowledge graph.

Through the steps, the new logic component is added into the knowledge graph, and the knowledge graph is updated.

Optionally, in this embodiment, S207 may also be included.

And S207, identifying a user vector and an article vector from the knowledge graph.

In this embodiment, triples may be converted to low-dimensional vectors by a TransE model. Any low-dimensional vector can be represented as (h, r, t), and the structure is shown in fig. 4.

In this embodiment, a user vector and an item vector are identified from the low-dimensional vectors.

Wherein, the user vector refers to: a user feature vector. If the user comprises: company, metal, chemical, wood, textile, other features. That is, for example, if the company of user a is XXXA, the vector corresponding to the triplet formed by the sentence is the user vector.

The item vector indicates: an item feature vector. Such as an assembly comprising: type, other characteristics. For example, if the type of component a is sum, the vector corresponding to the triplet formed by the sentence is the item vector.

The specific implementation manner of this step is the prior art, and is not described herein again.

Fig. 5 is a component recommendation method provided in an embodiment of the present application, where an execution subject is the recommendation apparatus in fig. 1, and the method may include the following steps:

s501, extracting target information when receiving that a user to be recommended adds a logic component in a visual IDE interface.

In this step, the target information may include: the information used for representing the user to be recommended, the information of the company to which the user to be recommended belongs and the information of the currently added logic component. The information used for characterizing the user to be recommended may be a name or an identifier of the user to be recommended. The information of the company to which the user to be recommended belongs may be a name or an identification of the company to which the user to be recommended belongs. The information of the currently added logical component may be a name or an identification of the currently added logical component.

S502, judging whether the constructed knowledge graph exists, if so, executing S503, and if not, executing S506.

S503, acquiring the information of the constructed knowledge graph.

In this step, the user vector and the item vector of the constructed knowledge-graph may be obtained from the construction apparatus.

In this step, the knowledge-graph comprises at least: the information used for representing the historical users, the information used for representing the companies to which the historical users belong, and the information of the logical components and the adding sequence added by the historical users in the process of logically configuring the visual IDE.

Optionally, in this embodiment, the knowledge graph may further include information of a logic component, information for characterizing a business to which the company to which the historical user belongs, and a preset candidate recommended logic component and a recommended score of the preset logic component.

The information of the logic component may include not only the identifier or name of the logic component, but also the number of additions. Wherein, the adding times of any logic component indicate that: after a user adds a logical component, the total number of times the logical component has been added next.

Wherein the recommended score of any one of the preset candidate recommendation logic components of any one of the preset logic components represents: the priority of the preset candidate recommendation logic component being recommended; wherein the larger the recommended score, the higher the priority. Taking table 7 as an example, in table 7, the recommendation scores of the preset recommendation logic components corresponding to different preset logic components are different, the first group is 100 scores, the second group is 91 scores, and the third group is 88 scores.

S504, determining the logic component added by the target person after the currently added logic component is added from the knowledge graph, and obtaining a candidate recommended logic component of the currently added logic component.

In this embodiment, the target person may include: the user to be recommended and the colleague of the company.

Optionally, in this embodiment, the target person may further include a person in the same industry as the user to be recommended.

Optionally, in this step, the logic component added after the currently added logic component is added by the target person may be determined from the knowledge graph according to the sequence of the user to be recommended first, the colleague of the company of the user to be recommended later, and the staff of the same industry. For convenience of description, the logic component determined in this step is referred to as a candidate recommended logic component of the currently added logic component.

And S505, acquiring the recommended score of each candidate recommendation logic component.

In this embodiment, after determining the candidate recommendation logic components, obtaining a recommended score of each candidate recommendation logic component is further included. Wherein the recommended score for any candidate recommendation logic component represents: a recommended priority of the candidate recommendation logic component; wherein, the larger the recommended score, the higher the recommended priority.

Specifically, the recommended score of the candidate recommendation logic component may be determined according to the total number of times that each candidate recommendation logic component in the knowledge graph is added after the currently added logic component. Wherein, the larger the value of the total times is, the larger the value of the recommended score is.

It should be noted that, in practice, this step is an optional step.

In this embodiment, S504 to S505 can be implemented by an existing collaborative filtering algorithm.

S506, determining candidate recommended logic components of the currently added logic components from the preset candidate recommended logic components of the preset logic components.

In the case where no knowledge-graph is constructed, the operation of this step is performed.

And S507, determining a logic component to be recommended of the currently added logic component according to the candidate recommended logic component.

In this step, the candidate recommendation logic components may all be regarded as the logic components to be recommended.

And S508, displaying the logic component to be recommended on a visual IDE interface as prompt information.

In this step, the logic component to be recommended is used as prompt information and fed back to the visual IDE software, so that the information of the logic component to be recommended is displayed on the visual IDE interface for the user to refer to.

In this embodiment, in order to further improve the recommendation accuracy of the to-be-recommended logic component, the process of determining the to-be-recommended logic component according to the candidate recommendation logic component in S507 may specifically include two ways.

The first mode is as follows:

and under the condition that the total number of the candidate recommendation logic components determined in the step S504 is greater than the preset number, determining the preset number of candidate recommendation logic components from the candidate recommendation logic components according to the sequence of the recommended scores from high to low, and taking the determined preset number of candidate recommendation logic components as the logic components to be recommended.

The second mode is as follows: the method comprises the following steps A1-A2:

and A1, under the condition that the total number of the candidate recommended logic components is not more than the preset number, determining the preset candidate recommended logic components corresponding to the currently added logic components and the recommended scores corresponding to each preset candidate recommended logic component from the preset candidate recommended logic components of the preset logic components. For convenience of description, the determined preset candidate recommendation logic components and corresponding recommended scores are referred to as supplementary candidate recommendation logic components and recommended scores of the currently added logic components.

And A2, determining a preset number of candidate recommendation logic components as logic components to be recommended according to the sequence of the recommended scores of all candidate recommendation logic components of the currently added logic components from high to low.

In this step, the candidate recommended logical component determined in S504 and the complementary candidate recommended logical component determined in step a1 are used as all candidate recommended logical components of the currently added logical component.

Since all the candidate recommendation logic components respectively correspond to the recommended scores, in this step, a preset number of candidate recommendation logic components are determined according to the order of the recommended scores from high to low, so as to obtain the logic components to be recommended.

In the embodiment, in both the first manner and the second manner, a preset number of candidate recommendation logic components are determined in the order of the recommended score from high to low. Since the recommended score value represents the recommended priority, and the higher the score value is, the higher the priority is, the present embodiment takes the preset number of candidate recommendation logic components with higher priorities as the logic components to be recommended. On one hand, the recommended scores of the logic components to be recommended are higher, and therefore the recommendation accuracy is improved. On the other hand, the preset number of candidate recommendation logic components are determined to serve as the logic components to be recommended, so that the number of the logic components to be recommended is not large, and a user can conveniently select the logic components to be recommended.

It should be noted that both of these methods are applicable to the case where the knowledge map is constructed. For the case where no knowledge-graph is constructed, only the first approach is applicable.

Fig. 6 is a recommendation apparatus for a logic component according to an embodiment of the present application, and the recommendation apparatus may include: an extraction module 601, an acquisition module 602, a first determination module 603, a second determination module 604, and a display module 605, wherein,

the extracting module 601 is configured to extract target information when it is received that a user to be recommended adds a logic component in a visual IDE interface; the target information at least includes: the information used for representing the user to be recommended, the information of the company to which the user to be recommended belongs and the information of the currently added logic component;

a first obtaining module 602, configured to obtain information of a knowledge graph in a case that the knowledge graph is constructed; the knowledge-graph includes at least: the information is used for representing a historical user, the information is used for representing a company to which the historical user belongs, and the information of the logical components and the adding sequence added by the historical user in the process of logically configuring the visual IDE;

a first determining module 603, configured to determine, from the knowledge graph, a logic component added by a target person after the currently added logic component is added, so as to obtain a candidate recommended logic component of the currently added logic component; the target person includes: the user to be recommended and the colleague of the same company;

a second determining module 604, configured to determine, according to at least the candidate recommended logic component, a logic component to be recommended of the currently added logic component;

and a display module 605, configured to display the to-be-recommended logical component on the visual IDE interface as a prompt message.

Optionally, the apparatus may further include: a second obtaining module, configured to obtain a recommended score of each candidate recommended logic component after the first determining module 603 determines, from the knowledge graph, a logic component added by a target person after the currently added logic component is added and obtains a candidate recommended logic component of the currently added logic component; the recommended score for any candidate recommendation logic component represents: a recommended priority of the candidate recommendation logic component; wherein, the larger the recommended score is, the higher the recommended priority is;

the second determining module 604 is configured to determine, according to at least the candidate recommended logical component, a to-be-recommended logical component of the currently added logical component, and includes:

the second determining module 604 is specifically configured to determine, when the total number of the candidate recommendation logic components is greater than a preset number, the preset number of candidate recommendation logic components from the candidate recommendation logic components as the to-be-recommended logic components according to a sequence from high to low of a recommended score.

the second determining module 604 is specifically configured to determine, from preset candidate recommended logic components of the preset logic components, a preset candidate recommended logic component corresponding to the currently added logic component when the total number of the candidate recommended logic components is not greater than the preset number, so as to obtain a supplementary candidate recommended logic component and a recommended score of the currently added logic component; and determining the preset number of candidate recommendation logic components as the logic components to be recommended according to the sequence of the recommended scores of all candidate recommendation logic components of the currently added logic components from high to low.

Optionally, the apparatus may further include:

and the third determining module is used for determining the candidate recommended logic component of the currently added logic component from the preset candidate recommended logic components of the preset logic components under the condition that the knowledge graph is not constructed.

Optionally, the apparatus may further include a construction module configured to construct the knowledge-graph;

the construction module is used for constructing the knowledge graph and comprises the following steps:

the building module is specifically used for acquiring logic component information, user attribute information and user behavior data for building the knowledge graph; extracting entities, entity relationships, attributes and attribute values from the logic component information, the user attribute information and the user behavior data; constructing a triple according to the entity, the entity relationship, the attribute and the attribute value; and constructing the knowledge graph according to the triples.

Optionally, the building module is further configured to, after the knowledge graph is built according to the triples, determine, when it is detected that a new logic component is added to the visualization IDE interface, a logic component in the knowledge graph, of which the similarity degree with the new logic component is greater than a preset threshold, as a target logic component; establishing a link between the target logical component and the new logical component in the knowledge-graph.

The logic component recommendation device comprises a processor and a memory, wherein the above-mentioned extracting module 601, the first obtaining module 602, the first determining module 603, the second determining module 604, the display module 605, and the like are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to implement corresponding functions.

The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to one or more than one, and the development efficiency of the application is low by adjusting the kernel parameters in the visualization IDE.

An embodiment of the present invention provides a storage medium on which a program is stored, the program implementing the recommendation method for a logic component when executed by a processor.

The embodiment of the invention provides a processor, which is used for running a program, wherein the recommendation method of the logic component is executed when the program runs.

An embodiment of the present invention provides an apparatus, as shown in fig. 7, the apparatus includes at least one processor, and at least one memory and a bus connected to the processor; the processor and the memory complete mutual communication through a bus; the processor is used for calling the program instructions in the memory to execute the identification method of the peer. The device herein may be a server, a PC, a PAD, a mobile phone, etc.

The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device:

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.

The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

The functions described in the method of the embodiment of the present application, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution to the prior art of the embodiments of the present application or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including several instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

Features described in the embodiments of the present specification may be replaced with or combined with each other, each embodiment is described with a focus on differences from other embodiments, and the same or similar portions among the embodiments may be referred to each other.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for recommending logical components, comprising:

2. The method of claim 1, wherein the target information further comprises: information of the industry of the company to which the user to be recommended belongs; the knowledge-graph further comprises: information for characterizing an industry of a company to which the historical user belongs; the target person further comprises: and the same-industry personnel of the user to be recommended.

3. The method of claim 1 or 2, wherein after determining, from the knowledge-graph, the logical components that the target person added after adding the currently added logical component, resulting in candidate recommended logical components for the currently added logical component, further comprising:

4. The method of claim 3, wherein the knowledge-graph further comprises: presetting candidate recommending logic components and recommended scores of the logic components; wherein the recommended score of any one of the preset candidate recommendation logic components of any one of the preset logic components represents: the priority of the preset candidate recommendation logic component being recommended; wherein, the larger the recommended score is, the higher the priority is;

5. The method of claim 1, further comprising:

and under the condition that a knowledge graph is not constructed, determining a candidate recommended logic component of the currently added logic component from preset candidate recommended logic components of preset logic components.

6. The method of claim 2, wherein the construction of the knowledge-graph comprises:

and constructing the knowledge graph according to the triples.

7. The method of claim 6, after said constructing the knowledge-graph from the triples, further comprising:

8. An apparatus for recommending logical components, comprising:

9. A storage medium comprising a stored program, wherein the program executes the method for recommending a logical component according to any one of claims 1 to 7.

10. An apparatus comprising at least one processor, and at least one memory, bus connected to the processor; the processor and the memory complete mutual communication through the bus; the processor is used for calling the program instructions in the memory to execute the recommended method of the logic component according to any one of claims 1-7.