CN112860655B - Visual knowledge model construction method and device - Google Patents
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- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
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Abstract
The embodiment of the application provides a method and a device for constructing a visual knowledge model, wherein the method comprises the following steps: obtaining a result set node according to the calculation of at least one data source node and a calculation edge, wherein the data source node is a node formed by dragging original data on a user interface, the calculation edge is a local preset algorithm, and the result set node is a result node generated after the calculation of the calculation edge; according to the calculation of at least one result set node and an operator edge, obtaining a calculated result set node, wherein the operator edge is an external algorithm interface; the method can support general big data modeling design through abstract and normative definition of points and edges, and simultaneously realizes integration with external interface capacity and algorithm capacity in model modeling.
Description
Technical Field
The application relates to the field of data models, in particular to a method and a device for constructing a visual knowledge model.
Background
At present, some problems and weak links still exist in the construction and application processes of the model.
The data standards are not uniform, and each front-end data acquisition manufacturer does not have uniform standard and standard data structures, so that trouble is brought to modeling of subsequent data.
The data structure is single in form, various sensing neurons are installed in place, and the data shows the characteristic of 'multi-source isomerization': the method has the advantages that the structured data of the literal and digital types obtained from the traditional database and the unstructured data of the image, video and the like obtained from the front-end sensing equipment exist. At present, structured data is mainly applied in data modeling, and unstructured data such as text, images, video and the like cannot be processed and applied well at present.
The data model is single, the data which can be used by different business departments are limited, the model is built step by step based on the data, and due to the limitation of the data, the model cannot be built completely according to the thought of a user, the creativity and thinking of the user are limited, and only the tradition business which needs to be completed manually is automated, and the distance is greatly different from the big data analysis and intelligent prediction of multi-source heterogeneous data by combining a machine learning algorithm.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a visual knowledge model construction method and device, which can support the general big data modeling design through abstract and normative definition of points and edges, and simultaneously realize the integration with external interface capacity and algorithm capacity in model modeling.
In order to solve at least one of the above problems, the present application provides the following technical solutions:
in a first aspect, the present application provides a method for constructing a visual knowledge model, including:
obtaining a result set node according to the calculation of at least one data source node and a calculation edge, wherein the data source node is a node formed by dragging original data on a user interface, the calculation edge is a local preset algorithm, and the result set node is a result node generated after the calculation of the calculation edge;
and according to the calculation of at least one result set node and the operator edge, obtaining the calculated result set node, wherein the operator edge is an external algorithm interface.
Further, the method further comprises the following steps:
and dragging the data source node and the result set node, selecting a local data source from the data source node, establishing an association relationship between the data source node and the local data, and setting a selected data source column as a display field and a condition field of the data source node.
Further, the method further comprises the following steps:
and defaulting to take the condition field of the input node as the condition field of the computing edge and the display field of the input node as the display field of the output node.
Further, the method further comprises the following steps:
and selecting a local data source as an input parameter of the operator edge by dragging the operator edge, and outputting the result generated structured data to the result set node after the operator edge is subjected to background batch operation.
In a second aspect, the present application provides a visual knowledge model construction apparatus, including:
the computing side computing module is used for obtaining a result set node according to the computation of at least one data source node and a computing side, wherein the data source node is a node formed by dragging original data at a user interface, the computing side is a local preset algorithm, and the result set node is a result node generated after the computation of the computing side;
and the operator edge calculation module is used for obtaining the calculated result set node according to the calculation of at least one result set node and the operator edge, wherein the operator edge is an external algorithm interface.
Further, the method further comprises the following steps:
the node dragging unit is used for dragging the data source node and the result set node, selecting a local data source from the data source node, establishing an association relationship between the data source node and the local data, and setting a selected data source column as a display field and a condition field of the data source node.
Further, the method further comprises the following steps:
and the computing edge input and output unit is used for defaulting to take the condition field of the input node as the condition field of the computing edge and taking the display field of the input node as the display field of the output node.
Further, the method further comprises the following steps:
and the edge dragging calculation unit is used for selecting a local data source as an input parameter of the operator edge by dragging the operator edge, and outputting the result generated structured data to the result set node after the operator edge is subjected to background batch operation.
In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the visual knowledge model construction method when the program is executed by the processor.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the visual knowledge model construction method.
According to the technical scheme, the visual knowledge model construction method and device are provided, the general big data modeling design is supported through abstract and normative definition of points and edges, and meanwhile integration of the model modeling with external interface capacity and algorithm capacity is achieved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for constructing a visual knowledge model in an embodiment of the application;
FIG. 2 is a block diagram of a visual knowledge model construction device in an embodiment of the application;
FIG. 3 is a schematic diagram of a visual knowledge model construction flow in an embodiment of the application;
fig. 4 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, 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 apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The application provides a visual knowledge model construction method and device, which support general big data modeling design through abstract and normative definition of points and edges, and simultaneously realize integration with external interface capacity and algorithm capacity in model modeling.
In order to support general big data modeling design through abstract and normative definition of points and edges and simultaneously realize integration with external interface capability and algorithm capability in model modeling, the application provides an embodiment of a visual knowledge model construction method, referring to fig. 1, wherein the visual knowledge model construction method specifically comprises the following contents:
step S101: obtaining a result set node according to the calculation of at least one data source node and a calculation edge, wherein the data source node is a node formed by dragging original data on a user interface, the calculation edge is a local preset algorithm, and the result set node is a result node generated after the calculation of the calculation edge;
step S102: and according to the calculation of at least one result set node and the operator edge, obtaining the calculated result set node, wherein the operator edge is an external algorithm interface.
From the above description, the visual knowledge model construction method provided by the embodiment of the application can support the general big data modeling design through abstract and normative definition of points and edges, and simultaneously realizes integration with external interface capability and algorithm capability in model modeling.
In an embodiment of the method for constructing a visual knowledge model of the present application, the method may further specifically include the following:
and dragging the data source node and the result set node, selecting a local data source from the data source node, establishing an association relationship between the data source node and the local data, and setting a selected data source column as a display field and a condition field of the data source node.
In an embodiment of the method for constructing a visual knowledge model of the present application, the method may further specifically include the following:
and defaulting to take the condition field of the input node as the condition field of the computing edge and the display field of the input node as the display field of the output node.
In an embodiment of the method for constructing a visual knowledge model of the present application, the method may further specifically include the following:
and selecting a local data source as an input parameter of the operator edge by dragging the operator edge, and outputting the result generated structured data to the result set node after the operator edge is subjected to background batch operation.
In order to support general big data modeling design through abstract and normative definition of points and edges and simultaneously realize integration with external interface capability and algorithm capability in model modeling, the application provides an embodiment of a visual knowledge model construction device for realizing all or part of contents of the visual knowledge model construction method, and referring to fig. 2, the visual knowledge model construction device specifically comprises the following contents:
the computing edge computing module 10 is configured to obtain a result set node according to computation of at least one data source node and a computing edge, where the data source node is a node formed by dragging original data at a user interface, the computing edge is a local preset algorithm, and the result set node is a result node generated after computation of the computing edge;
and the operator edge calculation module 20 is configured to obtain a calculated result set node according to the at least one result set node and the calculation of the operator edge, where the operator edge is an external algorithm interface.
From the above description, the visual knowledge model construction device provided by the embodiment of the application can support the general big data modeling design through abstract and normative definition of points and edges, and simultaneously realizes integration with external interface capability and algorithm capability in model modeling.
In an embodiment of the visual knowledge model construction device of the present application, the visual knowledge model construction device further specifically includes the following:
the node dragging unit is used for dragging the data source node and the result set node, selecting a local data source from the data source node, establishing an association relationship between the data source node and the local data, and setting a selected data source column as a display field and a condition field of the data source node.
In an embodiment of the visual knowledge model construction device of the present application, the visual knowledge model construction device further specifically includes the following:
and the computing edge input and output unit is used for defaulting to take the condition field of the input node as the condition field of the computing edge and taking the display field of the input node as the display field of the output node.
In an embodiment of the visual knowledge model construction device of the present application, the visual knowledge model construction device further specifically includes the following:
and the edge dragging calculation unit is used for selecting a local data source as an input parameter of the operator edge by dragging the operator edge, and outputting the result generated structured data to the result set node after the operator edge is subjected to background batch operation.
In order to further explain the scheme, the application also provides a specific application example for realizing the visual knowledge model construction method by applying the visual knowledge model construction device, which specifically comprises the following contents:
1. model design:
referring to fig. 3, first, the data structure defining the nodes and edges is normalized, the calculation paradigm of the normalization operator, the input and output are normalized, and the knowledge model organization DAG, g= (V, E) knowledge model organization includes: nodes and edges.
Node V:
v is a rank two-dimensional table.
Defining node instances, and classifying the nodes into two types according to types:
data source node: i.e. dragging the node formed by the raw data at the interface.
Result set node: i.e. the resulting nodes generated after edge computation.
Edge E:
e= (Vinput, voutput, operation)
Vinput＝[Input1|Input1,Input2|Input1,Input2,。。。，InputN]
Vinput, voutput belongs to Vector
Edge instances are defined, mainly comprising attributes and types. Each edge includes an input node and an output node. The edges are further divided into computation edges and operator edges according to the operation type.
Calculating edges: edges are supported that filter, group, intersect, union, difference, etc. the nodes.
Operator edges: and introducing an interface or algorithm, taking the data information of the input node as the input of the operator edge, and outputting a two-dimensional table with a row-column structure to generate a result node. Calculation paradigm of operator: input: monobasic, dibasic, tribasic. And (3) outputting: and (3) unifying.
External AI, industry interfaces, custom algorithms are introduced, fusing these capabilities into modeling nodes, each called an operator.
AI capabilities, such as: speech semantic recognition, text context recognition, image processing, etc.
The interfaces provided are as follows: national population information queries, national vehicle gear queries, national personnel queries, etc., these interfaces are packaged in a model as operators.
And the custom algorithm is used for customizing an algorithm model in the model construction process, and defining input and output after training of simulation data to form a custom AI operator interface.
Model design:
(1) Dragging a data source node and a result set node, selecting a local data source on the data source node, establishing an association relationship between the node and data, and selecting a data source column to be set as a display field and a condition field of the node. The result set node attribute is temporarily unset.
(2) The drag edge connects the data source node and the result set node, and the use of the edge has some standard specifications: the default data source node of the edge between the data source node and the result set node is an input node, and the result set node is an output node and cannot be changed. The input nodes and the output nodes can be determined between the result set nodes according to the directions of the edges. There is only one output node for an edge, but there may be one or two input nodes.
Selecting an edge type according to actual service requirements:
(2.1) if the computing edge is selected, defaulting to take the condition field of the input node as the condition field of the computing edge, taking the display field of the input node as the display field of the output node, and manually changing the display field. Since the computation edge contains: the various calculation functions such as filtering, grouping, intersection, union, difference and the like are different in configuration calculation rules. For example: intersection, the condition fields of two input nodes need to be configured into a condition group (input node a.condition1= input node b.condition1 +& input node a.condition2= input node b.condition2 … …), and the presentation fields of the two are taken as the presentation fields of the result set node.
(2.2) if the operator edge is selected, selecting a condition column in the data source node as an input parameter of the operator interface, and configuring an output result as a presentation field of the result set node.
(3) Typically in business practice, not only structured text data, but also image or video data may be needed, at which time the business operator nodes are dragged. Taking the video feature extraction operator as an example, the standard interface of the video operator is a single video or a video address directory. After dragging the video operator, selecting a picture address column in a local data source as an input parameter of the video operator, and after the operator is operated in batches in the background, generating structured data from results (whether the operator is provided with a cap, clothes colors, whether the operator wears glasses, men and women, approximate age groups and heights) and outputting the structured data to a result set node, so that the follow-up continuous modeling is convenient.
(4) The operation is repeated, a model thought can be built according to actual combat business requirements, and standard input and output of model nodes and edges are planned. The method solves the problems that multi-source heterogeneous data cannot be utilized and the data structure form is single, exerts the model creativity of users, builds the model thought first, and does not interrupt the modeling process because of the data result of each step.
2. Model operation:
after the model logic is built, the last step is that the model is operated. And resolving the input, output and display of each step by analyzing the knowledge model DAG, analyzing the dependency relationship and logic condition of each part of nodes, and executing according to the dependency sequence.
The model operation can be executed once, or the time period can be set for timing execution.
In order to support a general big data modeling design through abstract and normative definition of points and edges in terms of hardware level and simultaneously realize integration with external interface capability and algorithm capability in modeling, the application provides an embodiment of an electronic device for realizing all or part of contents in the visual knowledge model construction method, wherein the electronic device specifically comprises the following contents:
a processor (processor), a memory (memory), a communication interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the communication interface is used for realizing information transmission between the visual knowledge model construction device and related equipment such as a core service system, a user terminal, a related database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, etc., and the embodiment is not limited thereto. In this embodiment, the logic controller may refer to an embodiment of the method for constructing a visual knowledge model in the embodiment and an embodiment of the apparatus for constructing a visual knowledge model, and the contents thereof are incorporated herein, and are not repeated here.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Wherein, intelligent wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical application, part of the visual knowledge model construction method can be executed on the electronic device side as described in the above description, or all operations can be completed in the client device. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The present application is not limited in this regard. If all operations are performed in the client device, the client device may further include a processor.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Fig. 4 is a schematic block diagram of a system configuration of an electronic device 9600 of an embodiment of the present application. As shown in fig. 4, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 4 is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.
In one embodiment, the visual knowledge model building method functionality may be integrated into the central processor 9100. The central processor 9100 may be configured to perform the following control:
step S101: obtaining a result set node according to the calculation of at least one data source node and a calculation edge, wherein the data source node is a node formed by dragging original data on a user interface, the calculation edge is a local preset algorithm, and the result set node is a result node generated after the calculation of the calculation edge;
step S102: and according to the calculation of at least one result set node and the operator edge, obtaining the calculated result set node, wherein the operator edge is an external algorithm interface.
From the above description, the electronic device provided in the embodiment of the present application supports a general big data modeling design through abstract and normative definitions of points and edges, and simultaneously integrates with external interface capability and algorithm capability in model modeling.
In another embodiment, the visual knowledge model construction device may be configured separately from the central processor 9100, for example, the visual knowledge model construction device may be configured as a chip connected to the central processor 9100, and the visual knowledge model construction method functions are implemented by control of the central processor.
As shown in fig. 4, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 need not include all of the components shown in fig. 4; in addition, the electronic device 9600 may further include components not shown in fig. 4, and reference may be made to the related art.
As shown in fig. 4, the central processor 9100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 9100 receives inputs and controls the operation of the various components of the electronic device 9600.
The memory 9140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 9100 can execute the program stored in the memory 9140 to realize information storage or processing, and the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.
The memory 9140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, etc. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 storing application programs and function programs or a flow for executing operations of the electronic device 9600 by the central processor 9100.
The memory 9140 may also include a data store 9143, the data store 9143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. A communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, as in the case of conventional mobile communication terminals.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132 to implement usual telecommunications functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100 so that sound can be recorded locally through the microphone 9132 and sound stored locally can be played through the speaker 9131.
The embodiment of the present application also provides a computer-readable storage medium capable of implementing all the steps in the method for constructing a visual knowledge model in which an execution subject is a server or a client in the above embodiment, the computer-readable storage medium storing a computer program thereon, the computer program, when executed by a processor, implements all the steps of the visual knowledge model construction method in which the execution subject in the above embodiment is a server or a client, for example, the processor implements the following steps when executing the computer program:
step S101: obtaining a result set node according to the calculation of at least one data source node and a calculation edge, wherein the data source node is a node formed by dragging original data on a user interface, the calculation edge is a local preset algorithm, and the result set node is a result node generated after the calculation of the calculation edge;
step S102: and according to the calculation of at least one result set node and the operator edge, obtaining the calculated result set node, wherein the operator edge is an external algorithm interface.
As can be seen from the above description, the computer readable storage medium provided in the embodiments of the present application supports a general big data modeling design through abstract and normative definition of points and edges, and simultaneously implements integration with external interface capability and algorithm capability in model modeling.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (4)
1. A method for constructing a visual knowledge model, the method comprising:
obtaining a result set node according to the calculation of at least one data source node and a calculation edge, wherein the data source node is a node formed by dragging original data on a user interface, the calculation edge is a local preset algorithm, and the result set node is a result node generated after the calculation of the calculation edge;
dragging the data source node and the result set node, selecting a local data source from the data source node, establishing an association relationship between the data source node and local data, and selecting a data source column to be set as a display field and a condition field of the data source node;
according to the calculation of at least one result set node and an operator edge, obtaining a calculated result set node, wherein the operator edge is an external algorithm interface;
selecting a local data source as an input parameter of the operator edge by dragging the operator edge, and outputting result generation structured data to the result set node after the operator edge is subjected to background batch operation;
wherein calculating the edge comprises: edges supporting filtering, grouping, intersection, union and difference operations on nodes;
the operator edge includes: introducing an interface or algorithm, outputting a two-dimensional table with a row-column structure, and generating a result node;
the computational paradigm input of operators includes: monobasic, dibasic and tribasic;
the computational paradigm output of operators includes: unifying;
the edge type is selected according to actual service requirements:
if the calculation edge is selected, taking the condition field of the input node as the condition field of the calculation edge, taking the display field of the input node as the display field of the output node, and manually changing the display fields, wherein the display fields of the input node and the output node are taken as the display fields of the result set node;
if the operator edge is selected, a condition column in the data source node is selected as an input parameter of an operator interface, and an output result is configured as a display field of a result set node;
the default data source node of the edge between the data source node and the result set node is an input node, the result set node is an output node, and the input node and the output node can be determined according to the direction of the edge between the result set node and the result set node.
2. A visual knowledge model construction apparatus, comprising:
the computing side computing module is used for obtaining a result set node according to the computation of at least one data source node and a computing side, wherein the data source node is a node formed by dragging original data at a user interface, the computing side is a local preset algorithm, the result set node is a result node generated after the computation of the computing side, and the computing side comprises: edges supporting filtering, grouping, intersection, union and difference operations on nodes;
the node dragging unit is used for dragging the data source node and the result set node, selecting a local data source from the data source node, establishing an association relationship between the data source node and the local data, and selecting a data source column to be set as a display field and a condition field of the data source node;
the operator edge calculation module is used for obtaining a result set node after calculation according to at least one result set node and the operator edge calculation, wherein the operator edge is an external algorithm interface, and comprises: introducing an interface or algorithm, outputting a two-dimensional table with a row-column structure, and generating a result node; the computational paradigm input of operators includes: monobasic, dibasic and tribasic; the computational paradigm output of operators includes: unifying;
the edge dragging calculation unit is used for selecting a local data source as an input parameter of the operator edge by dragging the operator edge, and outputting result generation structured data to the result set node after the operator edge is subjected to background batch operation;
the edge type selection module comprises a calculation edge input and output unit and an operator edge input and output unit, and is used for selecting the type of the selected edge according to the actual service requirement, calling the calculation edge input and output unit if the selected edge is the calculation edge, and calling the operator edge input and output unit if the selected edge is the operator edge;
the computing edge input and output unit is used for taking the condition field of the input node as the condition field of the computing edge, taking the display field of the input node as the display field of the output node, and manually changing the display field, wherein the display fields of the input node and the output node are taken as the display fields of the result set node;
the operator edge input and output unit is used for selecting a condition column in the data source node as an input parameter of an operator interface and configuring an output result as a display field of the result set node;
and the input-output confirmation module is used for defaulting the data source node to be an input node according to the edge between the data source node and the result set node, wherein the result set node is an output node, and the input node and the output node can be determined according to the direction of the edge between the result set node and the result set node.
3. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the visual knowledge model construction method of claim 1 when the program is executed by the processor.
4. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor realizes the steps of the visual knowledge model construction method of claim 1.
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