CN113127574B - Business data display method, system, device and medium based on knowledge graph - Google Patents

Abstract

The application provides a business data display method, a system, equipment and a medium based on a knowledge graph, wherein the method comprises the following steps: capturing pages and business files associated with a target business; extracting each data associated with each preset label from the page and the business file; determining a knowledge graph formed by the data according to the relation among the preset labels; and displaying the knowledge graph. The target service data are uniformly displayed by the knowledge graph, so that the readability of the target service data is improved, the working efficiency of staff is improved, and the time for users to understand and transact the target service is saved.

Description

Business data display method, system, device and medium based on knowledge graph

Technical Field

The present application relates to the field of data processing technology, and in particular to a method, system, device and medium for displaying business data based on a knowledge graph.

Background Art

With the development of the financial technology field, more and more financial products are appearing in the financial market. Users can invest in financial products of interest through financial institutions to gain returns.

Currently, the financial products of financial institutions are usually provided by multiple institutions. The display styles of the financial products provided by each institution are different, resulting in complex and diverse display styles of financial products in financial institutions, which not only affects the work efficiency of financial staff, but also affects user understanding and consumption.

Summary of the invention

The embodiments of the present application propose a business data display method based on a knowledge graph, a business data processing system based on a knowledge graph, an electronic device and a computer-readable storage medium, which are used to solve the technical problem in related technologies that the display styles of financial products in financial institutions are complex and diverse, which not only affects the work efficiency of financial staff, but also affects user understanding and consumption.

To this end, an embodiment of the present application proposes a business data display method based on a knowledge graph, including capturing pages and business files associated with the target business; extracting each data associated with each preset tag from the pages and business files; determining a knowledge graph composed of the data based on the relationship between the preset tags; and displaying the knowledge graph.

As a first possible implementation method of the first aspect of the present invention, the crawling of pages and business files associated with the target business includes: determining a data crawling frequency according to the attributes of the target business; and crawling pages and business files associated with the target business at the crawling frequency.

As a second possible implementation method of the first aspect of the present invention, after crawling the pages and business files associated with the target business, it also includes: adding the pages and business files associated with the target business to a preset message queue; extracting the data respectively associated with each preset tag from the pages and business files includes: reading the pages and business files associated with the target business from the preset message queue to extract the data respectively associated with each preset tag from the pages and business files.

As a third possible implementation manner of the first aspect of the present invention, the target business data is financial product data; the preset label includes: at least one of: a financial product code, a financial product name, a financial product abbreviation, a financial product type, a financial product validity period, the company to which the financial product belongs, the person in charge of the product, and the subscription amount corresponding to the financial product.

As a fourth possible implementation method of the first aspect of the present invention, the display of the knowledge graph includes: when it is determined that the target business in the business list is in a selected state, displaying the knowledge graph at a preset position on the business list page; or, when it is determined that the target business in the business list is in a selected state, displaying the knowledge graph in a preset graph display layer, wherein the display priority of the graph display layer is higher than the layer where the business list is located.

As a fifth possible implementation method of the first aspect of the present invention, after extracting the data associated with each preset tag from the page and the business file, it also includes: when it is determined that the target business in the business list is in a selected state, displaying the data associated with each preset tag at a preset position on the business list page; or, when it is determined that the target business in the business list is in a selected state, displaying the data associated with each preset tag in a preset data display layer, wherein the display priority of the data display layer is higher than the layer where the business list is located.

As a sixth possible implementation manner of the first aspect of the present invention, the method also includes: obtaining a recommended setting instruction, wherein the recommended setting instruction includes a service identifier and a corresponding priority order; and determining a display position of each service in the service list according to the recommended setting instruction.

As the seventh possible implementation method of the first aspect of the present invention, after extracting the data associated with each preset tag from the page and the business file, it also includes: using the preset tags as key values, writing the data associated with each preset tag into the retrieval library.

As an eighth possible implementation manner of the first aspect of the present invention, the method also includes: obtaining a permission setting instruction, the setting instruction including a label and a corresponding role identifier; determining each data in the knowledge graph and the corresponding role identifier based on the association relationship between each preset label and each data; and setting the visibility of each node in the knowledge graph in the display interface corresponding to different role identifiers based on the each data and the corresponding role identifier.

On the other hand, an embodiment of the present application proposes a business data processing system based on a knowledge graph, including: a crawling module, used to crawl pages and business files associated with the target business; a data extraction module, used to extract each data associated with each preset tag from the pages and business files; a knowledge graph generation module, used to determine a knowledge graph for display composed of the data according to the relationship between the preset tags.

As a first possible implementation manner of the second aspect of the present invention, the capture module is specifically used to: determine a data capture frequency according to the attributes of the target business; and capture pages and business files associated with the target business at the capture frequency.

As a second possible implementation method of the second aspect of the present invention, the processing system also includes: a processing module, used to add the pages and business files associated with the target business to a preset message queue; the data extraction module is specifically used to: read the pages and business files associated with the target business from the preset message queue to extract the data associated with each preset tag from the pages and business files.

As a third possible implementation manner of the second aspect of the present invention, the target business data is financial product data; the preset label includes: at least one of: a financial product code, a financial product name, a financial product abbreviation, a financial product type, a financial product validity period, the company to which the financial product belongs, the person in charge corresponding to the product, and the subscription amount corresponding to the financial product.

On the other hand, an embodiment of the present application proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the business data display method based on the knowledge graph as described in the embodiment of the first aspect is implemented.

On the other hand, an embodiment of the present application proposes a computer-readable storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the business data display method based on the knowledge graph as described in the embodiment of the first aspect is implemented.

The technical solution disclosed in this application has the following beneficial effects:

By uniformly displaying the target business data using a knowledge graph, staff and users can understand the target business in a very intuitive knowledge graph form, which improves the readability of the target business data, improves the work efficiency of staff, and saves users time in understanding and handling the target business.

Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

FIG1 is a schematic diagram of a process of a business data display method based on a knowledge graph according to an embodiment of the present application;

FIG2 is an example diagram of a knowledge graph according to an embodiment of the present application;

FIG3 is a schematic diagram of the display effect of a knowledge graph according to an embodiment of the present application;

FIG4 is a schematic diagram of display effects of various data associated with various preset tags according to an embodiment of the present application;

FIG5 is a schematic diagram of a process of a business data display method based on a knowledge graph according to another embodiment of the present application;

6-9 are example diagrams of a business data display method based on a knowledge graph according to an embodiment of the present application;

FIG10 is a schematic diagram of the structure of a business data processing system based on a knowledge graph according to an embodiment of the present application;

FIG11 is a schematic diagram of the structure of an electronic device according to an embodiment of the present application;

FIG. 12 is a schematic diagram of the structure of an electronic device according to another embodiment of the present application.

DETAILED DESCRIPTION

Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

It is understandable that the various financial products of current financial institutions are usually provided by multiple institutions, and the display styles of the financial products provided by each institution are different, resulting in complex and diverse display styles of financial products in financial institutions, which not only affects the work efficiency of financial staff, but also affects user understanding and consumption.

To solve this technical problem, this application first captures the pages and business files associated with the target business, and extracts the data associated with each preset tag from the pages and business files, and then determines the knowledge graph composed of each data based on the relationship between the preset tags, and then displays the knowledge graph. By uniformly displaying the target business data with a knowledge graph, the readability of the target business data is improved, the work efficiency of the staff is improved, and the time for users to understand and handle the target business is saved.

The following describes, with reference to the accompanying drawings, a business data display method based on a knowledge graph, a business data processing system based on a knowledge graph, an electronic device, and a computer-readable storage medium in an embodiment of the present application.

First, with reference to FIG1 , the business data display method based on the knowledge graph provided in the embodiment of the present application is specifically described.

FIG1 is a flow chart of a method for displaying business data based on a knowledge graph according to an embodiment of the present application.

As shown in FIG1 , the business data display method based on the knowledge graph of the present application may include:

Step 101, crawling pages and business files associated with the target business.

Specifically, the business data display method based on the knowledge graph provided in the embodiment of the present application can be performed by the business data processing system based on the knowledge graph provided in the embodiment of the present application, hereinafter referred to as the processing system, which can be configured in an electronic device to generate a knowledge graph for display. Among them, the electronic device can be any terminal device capable of data processing, such as a smart phone, a laptop computer, a wearable device, etc., or a server, and this application does not limit this.

Among them, the target business can be a target financial product or other business, and this application does not limit this. This application takes the target business as a financial product as an example for explanation. Correspondingly, the pages associated with the financial product can include all pages related to the target financial product, such as the bank fund list page, the fund details page, the fund manager page, and the fund company page. The target business data is the financial product data.

In an exemplary embodiment, when crawling pages and business files associated with the target business, the crawling may be performed according to a preset crawling frequency, that is, step 101 may be implemented in the following manner:

Determine the frequency of data capture based on the attributes of the target business;

Crawl pages and business files related to the target business at the crawling frequency.

The attributes of the target business may include the category of the target business, the update frequency of the corresponding data, and other attributes.

For example, assuming that the data corresponding to the target business is updated at 12 noon every day, the data crawling frequency can be determined to be once a day. The specific crawling time can be set as needed, such as crawling the pages and business files associated with the target business at 5 or 6 pm every day.

By crawling pages and business files associated with the target business at a certain crawling frequency after the data corresponding to the target business is updated, the data processing volume of the processing system is reduced.

Step 102: extracting data associated with each preset tag from the page and the business file.

Specifically, labels corresponding to different businesses can be pre-set, so that after crawling the pages and business files associated with the target business, the pages and business files can be parsed to extract the data associated with the preset labels corresponding to the preset target business from the pages and business files.

In an exemplary embodiment, for financial products, the preset labels may include: financial product code, financial product name, financial product abbreviation, financial product type, financial product validity period, the company to which the financial product belongs, the person in charge of the product and the subscription amount corresponding to the financial product, financial product details, financial product assets, bonds held, financial product yield, financial product investment, financial product rate, and at least one of investment stocks.

For example, assuming that the target business data is financial product data and the preset tags are the above tags, then by parsing the extracted pages and business files, the data associated with the preset tags extracted from the pages and business files may include: financial product code "501007", financial product name "Huatianfu", financial product type "public offering", financial product validity period "20 years", the company to which the financial product belongs "Huatianfu Fund", and the person in charge of the product, namely the fund managers "Sun Lin", "Tang Junjie", "Ren Lina", "Gong Lili", etc.

It is understandable that the data structures and data formats of the data extracted from the pages and business files and associated with the preset tags may be diverse, which is not convenient for unified application and management. Therefore, after obtaining the data associated with the preset tags, the present application can also perform structured processing on the data associated with the preset tags to obtain structured data, so as to facilitate the unified application and management of the data and improve the availability of the data.

In an exemplary embodiment, the data corresponding to each label can be processed as follows: regularize the syntax of each data, remove spaces and other non-standard characters, replace nicknames or abbreviations with formal names, filter out unnecessary pictures, styles, files, etc., and the present application does not impose any restrictions on this.

Step 103, determining the knowledge graph composed of various data according to the relationship between each preset label.

It is understandable that the knowledge graph may include multiple nodes. In the embodiment of the present application, the node may correspond to data associated with a label, such as the name of the financial product "Huatianfu", the effective time of the financial product "20 years", etc., wherein each node corresponds to data associated with a label.

Specifically, after pre-setting labels corresponding to different businesses and extracting data associated with each preset label corresponding to the target business from pages and business files associated with the target business, the knowledge graph composed of each data can be determined based on the relationship between each preset label.

Among them, the relationship between the preset tags can be a semantic relationship between the preset tags, such as a classification relationship, a member relationship, an attribute relationship, an aggregation relationship, a time relationship, a position relationship, etc., and this application does not impose any restrictions on this.

It should be noted that the knowledge graph may also include edges connecting nodes. In the present application, edges may correspond to the relationship between data corresponding to different nodes. For example, "20 years" is the effective period of a financial product named "Huatianfu", then the node corresponding to "20" is connected to the node corresponding to "Huatianfu" through an edge, which corresponds to the relationship between "20" and "Huatianfu". Thus, while the nodes of the knowledge graph are composed of data associated with preset labels, edges connecting the nodes are generated according to the relationship between the labels.

For example, taking the target business as financial products, assuming that the preset labels corresponding to the financial products include: financial product code, financial product name, financial product abbreviation, financial product type, financial product validity period, the company to which the financial product belongs, the person in charge of the product and the subscription amount corresponding to the financial product, the financial product name "Huatianfu", the financial product type "public offering", the financial product code "501007", the financial product validity period "20 years", the company to which the financial product belongs "Huatianfu Fund", the person in charge of the financial product, namely the fund manager "Sun Lin", "Tang Junjie", "Ren Lina", "Gong Lili", and the financial product "Huatian Wealth" related to the financial product are extracted from the captured pages and business files associated with the financial products. Since the financial product type, financial product code, financial product validity period, financial product company, financial product fund manager, and financial products related to financial products are all related to the financial product name, it can be determined that the knowledge graph composed of "Huatianfu", "public offering", "501007", "20 years", "Huatianfu Fund", "Sun Lin", "Tang Junjie", "Ren Lina", "Gong Lili", and "Huatian Wealth" is as shown in Figure 2. In addition, as shown in Figure 2, the edges connecting the nodes in the knowledge graph can correspond to the semantic relationships between different nodes.

It should be noted that in the knowledge graph, the nodes connected to the nodes where the data associated with the names of financial products are located can also correspond to the detailed information of the financial products. When the data associated with the preset labels of different financial products are the same, the nodes can also be shared. For example, in Figure 2, fund managers "Gong Lili" and "Ren Lina" are both responsible for the financial products named "Huatianfu" and "Huatian Wealth", then the nodes where "Huatianfu" and "Huatian Wealth" are located are connected to the nodes where "Gong Lili" and "Ren Lina" are located.

In addition, when the knowledge graph is composed of data associated with each preset tag, the relationship between the data corresponding to each node in the knowledge graph can also be represented by any node in the related nodes. For example, "public offering" is the type of financial product named "Huatianfu", and the data in the node corresponding to "public offering" can include not only "public offering" but also "financial product type", such as "financial product type: public offering".

It should be noted that the method of determining the knowledge graph composed of various data based on the relationship between various preset labels can refer to the knowledge graph generation method in the relevant technology, and this application will not go into details.

Step 104, display the knowledge graph.

Specifically, after the knowledge graph is generated, it can be displayed in the following ways.

Method 1

When it is determined that the target business is selected in the business list, the knowledge graph is displayed at a preset position on the business list page.

Among them, the preset position can be set as needed, and this application does not limit the display position of the knowledge graph.

Specifically, the target service may be touched by a touch operation such as clicking, sliding, etc., so that the target service is in a selected state.

For example, as shown in Figure 3, a business list can be displayed in the display interface (as shown on the left side of Figure 3), and the business list includes multiple businesses. When a target business, such as a business with a serial number 1, is selected, the knowledge graph corresponding to the business with a fund name of "Huatianfu" can be displayed on the right side of the business list page (i.e., the page currently displaying the business list).

Method 2

When it is determined that the target business in the business list is in a selected state, the knowledge graph is displayed in a preset graph display layer, wherein the display priority of the graph display layer is higher than the layer where the business list is located.

Specifically, the display interface can be set to include multiple display layers, different display layers display different data, different display layers correspond to different display priorities, and the higher the display priority, the higher the display layer is displayed on the display interface, and the knowledge graph is pre-set to be displayed in the graph display layer with a display priority higher than the layer where the business list is located. Therefore, when the target business is selected, the knowledge graph can be displayed in the preset graph display layer.

It is understandable that in the present application, after extracting each data associated with each preset tag from the page and the business file, each data associated with each preset tag can also be displayed in the display interface.

The display method of each data associated with each preset tag may be similar to the display method of the knowledge graph. That is, after step 102, each data associated with each preset tag may be displayed in the following manner.

Method 3

When it is determined that the target service in the service list is in a selected state, various data associated with various preset tags are displayed at preset positions on the service list page.

Among them, the preset position can be set as needed, and this application does not limit the display position of each data associated with each preset tag.

Method 4

When it is determined that the target service in the service list is in a selected state, each data associated with each preset tag is displayed in a preset data display layer, wherein the display priority of the data display layer is higher than the layer where the service list is located.

Specifically, the display interface may be configured to include multiple display layers, with different display layers displaying different data, and different display layers corresponding to different display priorities. A layer with a higher display priority is displayed at an upper layer of the display interface, and it is preset to display each data associated with each preset tag in a data display layer with a display priority higher than the layer where the service list is located. Thus, when the target service is in the selected state, each data associated with each preset tag may be displayed in the preset data display layer.

For example, as shown in FIG. 4 , each data associated with each preset tag may be displayed in an upper layer of the layer where the service list is located.

In addition, in addition to displaying the business list, the display interface can also only display the data or knowledge graphs associated with each preset tag, or as shown in Figure 4, display the data and knowledge graphs associated with each preset tag at the same time, and this application does not limit this. It should be noted that the display priority of the data display layer and the graph display layer can be set as needed, for example, the display priority of the data display layer is higher than the display priority of the graph display layer.

The business data display method based on knowledge graph provided in the embodiment of the present application can extract each data associated with each preset tag from the page and business file after capturing the page and business file associated with the target business, and then determine the knowledge graph composed of each data according to the relationship between the preset tags, and then display the knowledge graph. By uniformly displaying the target business data with the knowledge graph, the readability of the target business data is improved, the work efficiency of the staff is improved, and the time for users to understand and handle the target business is saved.

The business data display method based on the knowledge graph provided in the embodiment of the present application is further explained below in conjunction with Figure 5.

FIG5 is a flow chart of a business data display method based on a knowledge graph according to another embodiment of the present application.

As shown in FIG5 , the business data display method based on the knowledge graph provided in the embodiment of the present application may include the following steps:

Step 201, determine the data capture frequency according to the attributes of the target business.

Step 202: crawling pages and business files associated with the target business at a crawling frequency.

The specific implementation process and principle of the above steps 201 to 202 can refer to the description of the above embodiment and will not be repeated here.

Step 203: Add the pages and business files associated with the target business to a preset message queue.

Step 204: Read the pages and business files associated with the target business from the preset message queue.

Step 205: extracting data associated with each preset tag from the page and the business file.

Specifically, after grabbing the pages and business files associated with the target business, the pages and business files can be queued and sorted in chronological order through the shell program, with the target business identifier as the primary key, and the URL (Uniform Resource Locator) address or storage location of the pages and business files pushed to the message queue, which is responsible for the distribution and sorting of the link addresses or storage locations of the pages and business files. Then, the processing system can subscribe to the pages and business files in the message queue, obtain the addresses or storage locations of the pages and business files, obtain the pages and business files through network requests or file access, and extract the data associated with each preset tag from the business page and business files.

Among them, the identifier of the target service can uniquely identify the target service, which can be a code corresponding to the target service, a name of the target service, etc., and this application does not impose any limitation on this.

It is understandable that the data structures and data formats of the data extracted from the pages and business files and associated with the preset tags may be diverse, which is not convenient for unified application and management. Therefore, after obtaining the data associated with the preset tags, the present application can also perform structured processing on the data associated with the preset tags to obtain structured data, so as to facilitate the unified application and management of the data and improve the availability of the data.

In addition, after extracting each data associated with each preset tag, each data associated with each preset tag can also be stored in a MySQL database, thereby obtaining each data associated with each preset tag from the MySQL database to generate a knowledge graph corresponding to the target business. Each data obtained from the MySQL database can also be written into a high-performance Nosql graph database for generating a knowledge graph.

In addition, each data associated with each preset tag can also be written into the search library, so that the user can quickly search for the required business. Specifically, each data associated with each preset tag can be written into the search library in the form of preset key-value pairs. That is, after step 205, it can also include: using each preset tag as a key value, writing each data associated with each preset tag into the search library.

The retrieval library may be a database for storing data for implementing the retrieval function.

The process of writing data into the retrieval library by using key-value pairs can refer to the process of storing data in a database by using key-value pairs in the related art, and this application will not go into details.

Step 206, determining the knowledge graph composed of each data according to the relationship between each preset label.

Step 207, when it is determined that the target business in the business list is in a selected state, the knowledge graph is displayed in a preset graph display layer, wherein the display priority of the graph display layer is higher than the layer where the business list is located.

The specific implementation process and principle of the above steps 206 and 207 can refer to the description of the above embodiment and will not be repeated here.

It is understandable that the service list includes multiple services. In the embodiment of the present application, the display positions of various services in the service list can also be adjusted according to instructions.

That is, the business data display method based on knowledge graph provided by this application may also include:

Obtaining a recommended setting instruction, wherein the recommended setting instruction includes a service identifier and a corresponding priority order;

According to the recommended setting instructions, determine the display position of each service in the service list.

The recommended setting instruction can be triggered by a staff member or user touching a preset position of the service list page through a touch operation such as clicking or dragging, or dragging a service in the service list page. Of course, it can also be triggered by other methods, which is not limited by this application. The recommended setting instruction is used to set the display position of each service in the service list.

Specifically, after obtaining the recommended setting instruction, the services corresponding to the service identifiers can be displayed in the service list in order of priority according to the service identifiers and the corresponding priority order.

By adjusting the display position of each service in the service list according to the recommended setting instruction, it is more convenient for the staff to adjust the recommendation order of each service according to the business needs.

In addition, in the embodiment of the present application, the visibility of the data corresponding to each node in the knowledge graph can be set differently for different roles. That is, the business data display method based on the knowledge graph provided by the present application can also include:

Get permission setting instructions, which include tags and corresponding role identifiers;

According to the association between each preset label and each data, determine each data in the knowledge graph and the corresponding role identifier;

According to each data and the corresponding role identifier, set the visibility of each data in the knowledge graph in the display interface corresponding to different role identifiers.

Among them, the permission setting instruction can be triggered by the staff through touch operations such as clicking and dragging a button with a permission setting function, or it can be triggered by other methods, and this application does not limit this. The permission setting instruction is used to set whether the data associated with the tag is visible to the role corresponding to the role identifier. For example, the permission setting instruction can include "the financial product type is visible to role M".

Specifically, after obtaining the labels and corresponding role identifiers in the permission setting instructions, the data and corresponding role identifiers in the knowledge graph can be determined based on the association between each preset label and each data, as well as the label and the corresponding role identifier. Then, based on each data and the corresponding role identifier, and in accordance with the visibility in the permission setting instructions, the visibility of each data in the knowledge graph in the display interface corresponding to different role identifiers can be set.

For example, suppose the permission setting instruction includes "Financial product type is visible to role M", "Financial product company is not visible to role M", "Financial product type is not visible to role N", "Financial product company is visible to role M", and the data corresponding to the financial product type is "public offering", and the data corresponding to the financial product company is "Huatianfu Fund". Then it can be determined that the data "public offering" in the knowledge graph is visible to role M, but not to role N, and the data "Huatianfu Fund" in the knowledge graph is not visible to role M, but visible to role N. Therefore, in the display interface corresponding to role M, the knowledge graph can be set to display "public offering", and in the display interface corresponding to role N, the knowledge graph can display "Huatianfu Fund".

By setting different knowledge graph visibility permissions for different roles, it is more convenient for staff and users to obtain effective information through the knowledge graph, saving their time.

In specific implementation, the processing system can be configured in the processing system of the bank. The following is combined with the example diagrams shown in Figures 6-9, taking the target business as the target financial product as an example, to illustrate the processing process of business data.

As shown in Figures 6 and 7, according to the preset crawling frequency, the bank fund manager page, fund company page and other fund-related pages can be crawled from the web data source, and then the pages are queued and sorted in chronological order through the shell program, with the fund code as the primary key, and the URL address of the page is pushed to the message queue. The message queue is responsible for the distribution and sorting of the URL address of the page until the URL addresses of all pages are pushed to the message queue. By processing the bank fund-related pages in the form of a message queue, the business data processing system based on the financial knowledge graph can be decoupled from the bank front-end system, ensuring the stability of the business data processing system based on the knowledge graph.

Then, as shown in Figures 6 and 8, the data extraction module of the processing system can subscribe to the pages in the message queue, obtain the data address from the URL list of the message queue, obtain the page data through http requests, and extract the data associated with tags such as fund managers and fund companies from the page data. Then, these data are structured to generate structured data corresponding to the fund, and persisted to the local MySQL database through Java technology using the orm model (object-relational mapping model). After completing the writing of a piece of data, the message queue will remove this piece of data in the message queue, and then process the next piece of data until all data is processed.

For data in the MySQL database, you can use scheduled tasks to synchronize the latest data to the graph database and retrieval library, respectively implementing the tasks of transferring the data source to the high-performance Nosql graph database and retrieval library, and updating the data through the interface. The specific data synchronization process can be shown in Figure 9.

Specifically, the program first clears the data in the graph database and retrieval library by calling the interface, then connects to the local MySQL database through jdbc (Java Data Base Connectivity), and reads the structured data associated with tags such as fund managers and fund companies in the MySQL database, and then writes the acquired structured data into the graph database for generating a knowledge graph, using each tag as the key value, and writes the structured data corresponding to the fund into the retrieval library, and exits the program after completion.

Graph databases can efficiently receive banking and financial data and generate knowledge graphs, as well as store knowledge graphs and provide interfaces for banking front-end systems to display knowledge graphs.

The search library can provide efficient search functions. Through the search engine and inverted index, you can quickly search and locate data that meets certain conditions. Specifically, you can quickly search for data by fund code, fund manager, fund company, financial management code, etc. When implementing it, enter the fund code, fund manager, fund company, and financial management code information in the search box, click the search button, and the search engine will query the search library, return the data that meets the conditions, sort it by relevance, and display it on the page.

The business system can access the local database to obtain fund and financial data to provide fund lists and details display functions for the front-end page. The web backend can provide functions such as user management, system management, and personalized page configuration. Among them, system management mainly includes management of system styles and themes, and user management includes management of users, roles, permissions, etc., to achieve secure management of data. Page configuration is mainly used by operation and maintenance personnel to customize the personalized needs of banks or outlets, such as promoting funds, financial products, data display, etc. After the outlet user logs in to the system, he can select a fund in the fund list or search for a fund by keyword, select a fund, and set it as a recommended fund by clicking the button. After setting it as a recommended fund, these funds will be displayed at the front when the user views the fund list on the front end. By viewing statistical reports, outlet users can view the click-through rate of funds, the ranking of fund stay time, etc.

The business data display method based on knowledge graph provided in the embodiment of the present application can add the pages and business files associated with the target business to the preset message queue after crawling the pages and business files associated with the target business at the crawling frequency, and read the pages and business files associated with the target business from the preset message queue, and then extract the data associated with each preset tag from the pages and business files, and then determine the relationship between each preset tag to determine the knowledge graph composed of each data, and then when it is determined that the target business is in the selected state in the business list, display the knowledge graph in the preset graph display layer, wherein the display priority of the graph display layer is higher than the layer where the business list is located. By uniformly displaying the target business data with the knowledge graph, the readability of the target business data is improved, the work efficiency of the staff is improved, and the time for users to understand and handle the target business is saved.

The following is an explanation of the business data processing system based on knowledge graph provided in an embodiment of the present application in conjunction with Figure 10.

It should be noted that the business data processing system based on the knowledge graph provided in the embodiment of the present application can execute the business data display method based on the knowledge graph described in the above embodiment. When implemented specifically, the business data processing system based on the knowledge graph can be configured in an electronic device to generate a knowledge graph for display. Among them, the electronic device can be any terminal device capable of data processing, such as a smart phone, a laptop computer, a wearable device, etc., or a server, and this application does not limit this.

FIG10 is a schematic diagram of the structure of a business data processing system based on a knowledge graph according to an embodiment of the present application.

As shown in FIG10 , the business data processing system based on the knowledge graph provided in the embodiment of the present application includes:

The capture module 11 is used to capture pages and business files associated with the target business;

The data extraction module 12 is used to extract data associated with each preset tag from the page and the business file;

The knowledge graph generation module 13 is used to determine the knowledge graph for display composed of the data according to the relationship between the preset tags.

In a possible implementation form, the capture module 11 is specifically configured to:

Determine the frequency of data capture based on the attributes of the target business;

Crawl pages and business files related to the target business at the crawling frequency.

In another possible implementation form, the target business data is financial product data;

The preset labels include: at least one of: financial product code, financial product name, financial product abbreviation, financial product type, financial product validity period, company to which the financial product belongs, person in charge corresponding to the product, and subscription amount corresponding to the financial product.

In another possible implementation form, the business data processing system based on the knowledge graph provided in the embodiment of the present application may also include:

The processing module is used to add the pages and business files associated with the target business to the preset message queue.

Accordingly, the data extraction module 12 is specifically used for:

The pages and business files associated with the target business are read from the preset message queue to extract the data respectively associated with the preset tags from the pages and business files.

In another possible implementation form, the business data processing system based on the knowledge graph provided in the embodiment of the present application may also include:

The recommended setting instruction acquisition module is used to acquire the recommended setting instruction, wherein the recommended setting instruction includes a service identifier and a corresponding priority order.

The display position determination module is used to determine the display position of each service in the service list according to the recommended setting instruction.

The writing module is used to write each data associated with each preset tag into the retrieval library by using each preset tag as a key value.

The permission setting instruction acquisition module is used to obtain the permission setting instruction, and the setting instruction includes a label and a corresponding role identifier.

The role identification determination module is used to determine the corresponding role identification of each data in the knowledge graph according to the association relationship between each preset label and each data.

The setting module is used to set the visibility of each data in the knowledge graph in the display interface corresponding to different role identifiers according to each data and the corresponding role identifier.

It should be noted that the implementation process and technical principles of the business data processing system based on the knowledge graph of this embodiment refer to the aforementioned explanation of the business data display method based on the knowledge graph of the first aspect of the embodiment, and will not be repeated here.

The business data processing system based on knowledge graph provided by the embodiment of the present application can extract each data associated with each preset tag from the page and business file after capturing the page and business file associated with the target business, and then determine the knowledge graph for display composed of each data according to the relationship between the preset tags. By determining the indication graph for display according to the target business data, the target business data can be uniformly displayed using the knowledge graph, which improves the readability of the target business data, improves the work efficiency of the staff, and saves the time for users to understand and handle the target business.

In order to implement the above embodiments, the present application also proposes an electronic device.

Figure 11 is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. The electronic device shown in Figure 11 is only an example and should not bring any limitation to the functions and scope of use of the embodiment of the present application.

Figure 11 is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. The electronic device shown in Figure 11 is only an example and should not bring any limitation to the functions and scope of use of the embodiment of the present application.

As shown in Figure 11, the above-mentioned electronic device 200 includes: a memory 210, a processor 220, and a computer program stored in the memory 210 and executable on the processor 220. When the processor 220 executes the program, the business data display method based on the knowledge graph described in the first aspect embodiment is implemented.

Specifically, the electronic device can be any hardware device capable of data processing, such as a smart phone, a laptop computer, a wearable device, etc., or a server, which is not limited in this application.

In an optional implementation form, as shown in Figure 12, the electronic device 200 may also include: a memory 210 and a processor 220, a bus 230 connecting different components (including the memory 210 and the processor 220), the memory 210 stores a computer program, and when the processor 220 executes the program, the business data display method based on the knowledge graph described in the embodiment of the present application is implemented.

Bus 230 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

The electronic device 200 typically includes a variety of computer device readable media. These media can be any available media that can be accessed by the electronic device 200, including volatile and non-volatile media, removable and non-removable media.

The memory 210 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 240 and/or cache memory 250. The electronic device 200 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, the storage system 260 may be used to read and write non-removable, non-volatile magnetic media (not shown in FIG. 12, commonly referred to as "hard disk drives"). Although not shown in FIG. 12, a disk drive for reading and writing removable non-volatile disks (such as "floppy disks"), and an optical disk drive for reading and writing removable non-volatile optical disks (such as CD-ROM, DVD-ROM or other optical media) may be provided. In these cases, each drive may be connected to the bus 230 via one or more data medium interfaces. The memory 210 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

A program/utility 280 having a set (at least one) of program modules 270 may be stored, for example, in the memory 210, such program modules 270 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 270 generally perform the functions and/or methods of the embodiments described herein.

The electronic device 200 may also communicate with one or more external devices 290 (e.g., keyboard, pointing device, display 291, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 200, and/or communicate with any device that enables the electronic device 200 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed via an input/output (I/O) interface 292. Furthermore, the electronic device 200 may also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN) and/or public network, such as the Internet) via a network adapter 293. As shown in FIG. 12 , the network adapter 293 communicates with other modules of the electronic device 200 via a bus 230. It should be understood that, although not shown in FIG. 12 , other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

It should be noted that the implementation process and technical principles of the electronic device of this embodiment refer to the aforementioned explanation of the business data display method based on the knowledge graph of the first aspect embodiment, and will not be repeated here.

The electronic device provided in the embodiment of the present application can extract each data associated with each preset tag from the page and the business file after grabbing the page and the business file associated with the target business, and then determine the knowledge graph composed of each data according to the relationship between the preset tags, and then display the knowledge graph. By uniformly displaying the target business data with the knowledge graph, the readability of the target business data is improved, the work efficiency of the staff is improved, and the time for users to understand and handle the target business is saved.

To implement the above embodiments, the present application also proposes a computer-readable storage medium.

The computer-readable storage medium stores a computer program thereon, and when the program is executed by the processor, it implements the business data display method based on the knowledge graph described in the first aspect of the embodiment.

To implement the above-mentioned embodiments, the present application also proposes a computer program product. When the instructions in the computer program product are executed by a processor, the business data display method based on the knowledge graph as described in the above-mentioned embodiment is executed.

In the description of this specification, reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application.

In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims (11)

1. A business data display method based on knowledge graph, characterized by comprising: Crawl pages and business files associated with the target business; Extracting data respectively associated with each preset tag from the page and the business file; Determine a knowledge graph consisting of the data according to the relationship between the preset tags; Displaying the knowledge graph; After extracting the data respectively associated with the preset tags from the pages and business files, the method further includes: When it is determined that the target service in the service list is in a selected state, displaying each data associated with each preset tag at a preset position on the service list page; or, When it is determined that the target service in the service list is in a selected state, displaying each data associated with each preset tag in a preset data display layer, wherein the display priority of the data display layer is higher than the layer where the service list is located; Obtaining a recommended setting instruction, wherein the recommended setting instruction includes a service identifier and a corresponding priority order; Determining the display position of each service in the service list according to the recommended setting instruction; After extracting the data respectively associated with the preset tags from the pages and business files, the method further includes: Using the preset tags as key values, writing the data associated with the preset tags into the search library; Also includes: Obtaining permission setting instructions, wherein the setting instructions include a tag and a corresponding role identifier; According to the association between each preset label and each data, determine each data in the knowledge graph and the corresponding role identifier; According to the data and the corresponding role identifiers, the visibility of the data in the knowledge graph in the display interface corresponding to the different role identifiers is set. 2. The method according to claim 1, wherein the crawling of pages and business files associated with the target business comprises: Determine the frequency of data capture according to the attributes of the target business; The pages and business files associated with the target business are crawled at the crawling frequency. 3. The method according to claim 1, characterized in that after crawling the pages and business files associated with the target business, it also includes: Adding pages and business files associated with the target business to a preset message queue; The extracting of data respectively associated with each preset tag from the page and the business file includes: The pages and business files associated with the target business are read from the preset message queue to extract the data respectively associated with the preset tags from the pages and business files. 4. The method according to claim 1, wherein the target business data is financial product data; The preset label includes at least one of: a financial product code, a financial product name, a financial product abbreviation, a financial product type, a financial product validity period, a company to which the financial product belongs, a person in charge corresponding to the product, and a subscription amount corresponding to the financial product. 5. The method according to claim 1, wherein displaying the knowledge graph comprises: When it is determined that the target business in the business list is in a selected state, displaying the knowledge graph at a preset position on the business list page; or, When it is determined that the target business in the business list is in a selected state, the knowledge graph is displayed in a preset graph display layer, wherein the display priority of the graph display layer is higher than the layer where the business list is located. 6. A business data processing system based on knowledge graph, which is used to execute the method according to any one of claims 1 to 5, characterized in that it includes: A crawling module is used to crawl pages and business files associated with the target business; A data extraction module, used to extract data associated with each preset tag from the page and the business file; The knowledge graph generation module is used to determine the knowledge graph for display composed of the data according to the relationship between the preset tags. 7. The processing system according to claim 6, wherein the capture module is specifically used for: Determine the frequency of data capture according to the attributes of the target business; The pages and business files associated with the target business are crawled at the crawling frequency. 8. The processing system of claim 6, further comprising: A processing module, used for adding pages and business files associated with the target business to a preset message queue; The data extraction module is specifically used for: The pages and business files associated with the target business are read from the preset message queue to extract the data respectively associated with the preset tags from the pages and business files. 9. The processing system according to claim 6, wherein the target business data is financial product data; The preset label includes at least one of: a financial product code, a financial product name, a financial product abbreviation, a financial product type, a financial product validity period, a company to which the financial product belongs, a person in charge corresponding to the product, and a subscription amount corresponding to the financial product. 10. An electronic device, characterized in that it comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the business data display method based on the knowledge graph as described in any one of claims 1 to 5. 11. A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the business data display method based on the knowledge graph as described in any one of claims 1 to 5 is implemented.