CN114821591A

CN114821591A - Knowledge anchor point generation method and device based on artificial intelligence and storage medium

Info

Publication number: CN114821591A
Application number: CN202210441787.XA
Authority: CN
Inventors: 满园园; 陈闽; 梁亚妮; 刘喜声
Original assignee: Ping An Life Insurance Company of China Ltd
Current assignee: Ping An Life Insurance Company of China Ltd
Priority date: 2022-04-25
Filing date: 2022-04-25
Publication date: 2022-07-29

Abstract

The application relates to an artificial intelligence technology, and provides a knowledge anchor point generation method and device based on artificial intelligence, an electronic device and a computer readable storage medium, wherein the method comprises the following steps: acquiring a scheme picture from a network page; identifying the scheme picture based on a preset optical character identification network model to obtain a content text; extracting the content text to obtain an original keyword; judging whether a knowledge text matched with the original keyword exists in a preset knowledge text set, and if so, determining the original keyword as an effective keyword; generating a knowledge anchor point in the web page corresponding to the position of the effective keyword; and triggering a first operation instruction of a user for the knowledge anchor point, and displaying the knowledge text corresponding to the knowledge anchor point on the network page. By the technical scheme, the working efficiency of the salesman can be improved, and convenience is brought to the work of the salesman.

Description

Knowledge anchor point generation method and device based on artificial intelligence and storage medium

Technical Field

The embodiment of the application relates to, but not limited to, the technical field of artificial intelligence, and in particular relates to a knowledge anchor point generation method and device based on artificial intelligence, an electronic device and a computer-readable storage medium.

Background

In the process of visiting a client, an insurance practitioner often needs to explain some professional schemes; when a client feels confused about some terms or knowledge points, an insurance practitioner needs to search relevant data to explain the terms or knowledge points, so that the whole scheme introduction process is time-consuming, and the working efficiency of the insurance practitioner is affected.

Disclosure of Invention

The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

In order to solve the problems mentioned in the background art, embodiments of the present application provide a method and an apparatus for generating a knowledge anchor point based on artificial intelligence, an electronic device, and a computer-readable storage medium, which can improve the work efficiency of an employee and bring convenience to the work of the employee.

In a first aspect, an embodiment of the present application provides a knowledge anchor point generation method based on artificial intelligence, where the method includes:

acquiring a scheme picture from a network page;

identifying the scheme picture based on a preset optical character identification network model to obtain a content text;

extracting the content text to obtain an original keyword;

judging whether a knowledge text matched with the original keyword exists in a preset knowledge text set, and if so, determining the original keyword as an effective keyword;

generating a knowledge anchor point in the web page corresponding to the position of the effective keyword;

and triggering a first operation instruction of a user for the knowledge anchor point, and displaying the knowledge text corresponding to the knowledge anchor point on the network page.

The artificial intelligence-based knowledge anchor point generation method provided by the embodiment of the application at least has the following beneficial effects: firstly, acquiring a scheme picture from a network page; then, identifying the scheme picture based on a preset optical character identification network model to obtain a content text; extracting the content text to obtain an original keyword; then judging whether a knowledge text matched with the original keywords exists in a preset knowledge text set, and if so, determining the original keywords as effective keywords; then generating a knowledge anchor point at a position corresponding to the effective keyword in the network page; and finally, triggering a first operation instruction of the user for the knowledge anchor point, and displaying the knowledge text corresponding to the knowledge anchor point on a network page. The embodiment shows the relevant explanation of the effective keyword in a knowledge anchor point form, so that the salesman can directly trigger the knowledge anchor point to explain the relevant content in the explanation process without needing to spend time to search the relevant content like the past, the working efficiency of the salesman can be improved, and great convenience is brought to the work of the salesman.

According to some embodiments of the present application, the optical character recognition network model includes an image preprocessing module, a layout processing module, an image segmentation module, a feature extraction module, and an identification module, and the scheme picture is identified based on a preset optical character recognition network model to obtain a content text, including:

preprocessing the scheme picture based on the image preprocessing module to obtain first picture information;

performing layout processing on the first picture information based on the layout processing module to obtain second picture information;

performing image segmentation processing on the second picture information based on the image segmentation module to obtain third picture information;

extracting the features of the third picture information based on the feature extraction module to obtain image feature information;

and performing character recognition processing on the image characteristic information based on the recognition module to obtain the content text.

According to some embodiments of the present application, the extracting the content text to obtain an original keyword includes:

matching initial words in the content text to obtain matching scores corresponding to the initial words;

and under the condition that the matching score is not lower than a preset matching threshold value, determining the corresponding initial word as the original keyword.

According to some embodiments of the present application, the generating a knowledge anchor at a position in the web page corresponding to the valid keyword includes:

based on a preset layout display priority, screening the effective keywords to obtain final keywords;

and generating a knowledge anchor point in the network page corresponding to the position of the final keyword.

According to some embodiments of the present application, the layout display priority includes an anchor classification priority and a display position priority, and the effective keywords are screened based on a preset layout display priority to obtain final keywords, including:

according to the anchor point classification priority, performing first selection on the effective keywords to obtain first keywords;

and performing second selection on the first keyword according to the display position priority to obtain the final keyword.

According to some embodiments of the application, the triggering a first operation instruction of a user for the knowledge anchor point, and displaying the knowledge text corresponding to the knowledge anchor point on the network page, includes:

triggering a first operation instruction of a user for the knowledge anchor point, and extracting the knowledge text corresponding to the knowledge anchor point from the knowledge text set;

and displaying the knowledge text in the network page.

According to some embodiments of the application, after the triggering the first operation instruction of the user for the knowledge anchor point and displaying the knowledge text corresponding to the knowledge anchor point on the web page, the method further includes:

triggering a second operation instruction of the user for the knowledge anchor point, and hiding the knowledge text corresponding to the knowledge anchor point displayed at the previous moment on the network page.

In a second aspect, an embodiment of the present application further provides an artificial intelligence-based knowledge anchor point generating apparatus, where the apparatus includes:

the first processing module is used for acquiring a scheme picture from a network page;

the second processing module is used for identifying the scheme picture based on a preset optical character identification network model to obtain a content text;

the third processing module is used for extracting the content text to obtain an original keyword;

the fourth processing module is used for judging whether a knowledge text matched with the original keyword exists in a preset knowledge text set or not, and if yes, determining the original keyword as an effective keyword;

the fifth processing module is used for generating a knowledge anchor point in the network page corresponding to the position of the effective keyword;

and the sixth processing module is used for triggering a first operation instruction of a user for the knowledge anchor point and displaying the knowledge text corresponding to the knowledge anchor point on the network page.

In a third aspect, an embodiment of the present application further provides an electronic device, including: the artificial intelligence based knowledge anchor point generating method comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the artificial intelligence based knowledge anchor point generating method according to the first aspect.

In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium storing computer-executable instructions for performing the artificial intelligence-based knowledge anchor point generation method according to the first aspect.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

The accompanying drawings are included to provide a further understanding of the claimed subject matter and are incorporated in and constitute a part of this specification, illustrate embodiments of the subject matter and together with the description serve to explain the principles of the subject matter and not to limit the subject matter.

FIG. 1 is a flow chart of a method for artificial intelligence based knowledge anchor generation according to an embodiment of the present application;

FIG. 2 is a flowchart illustrating content text acquisition in a knowledge anchor point generation method based on artificial intelligence according to an embodiment of the present application;

FIG. 3 is a flowchart of determining original keywords in a method for generating artificial intelligence-based knowledge anchor points according to an embodiment of the present application;

FIG. 4 is a flowchart illustrating the generation of a knowledge anchor in a method for generating a knowledge anchor based on artificial intelligence according to an embodiment of the present application;

FIG. 5 is a flowchart illustrating a final keyword selection in a method for generating artificial intelligence-based anchor points according to an embodiment of the present application;

FIG. 6 is a flowchart illustrating a knowledge text in a method for generating knowledge anchor points based on artificial intelligence according to an embodiment of the present application;

FIG. 7 is a flowchart of a method for artificial intelligence based knowledge anchor generation according to another embodiment of the present application;

FIG. 8 is a diagram illustrating an artificial intelligence-based knowledge anchor point generating device according to an embodiment of the present application;

fig. 9 is a schematic diagram of an electronic device provided in an embodiment of the present application.

Detailed Description

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

It should be noted that although functional block divisions are performed in apparatus diagrams and logical orders are illustrated in flowcharts, in some cases, steps shown or described may be performed in orders different from block divisions in apparatus diagrams or flowcharts. The terms first, second and the like in the description and in the claims, as well as in the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.

It is to be noted that, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.

The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

AI is a new technical science to study and develop theories, methods, techniques and application systems for simulating, extending and expanding human intelligence; artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produces a new intelligent machine that can react in a manner similar to human intelligence, and research in this field includes robotics, language recognition, image recognition, natural language processing, and expert systems, among others. The artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

The server related to the artificial intelligence technology can be an independent server, and can also be a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN), big data and artificial intelligence platform and the like.

The application provides a knowledge anchor point generation method, a knowledge anchor point generation device, electronic equipment and a computer readable storage medium based on artificial intelligence, wherein scheme pictures are obtained from a network page; then, identifying the scheme picture based on a preset optical character identification network model to obtain a content text; extracting the content text to obtain an original keyword; then judging whether a knowledge text matched with the original keywords exists in a preset knowledge text set, and if so, determining the original keywords as effective keywords; then generating a knowledge anchor point at a position corresponding to the effective keyword in the network page; and finally, triggering a first operation instruction of the user for the knowledge anchor point, and displaying the knowledge text corresponding to the knowledge anchor point on a network page. The embodiment shows the relevant explanation of the effective keyword in a knowledge anchor point form, so that the salesman can directly trigger the knowledge anchor point to explain the relevant content in the explanation process without needing to spend time to search the relevant content like the past, the working efficiency of the salesman can be improved, and great convenience is brought to the work of the salesman.

The embodiment of the application provides a knowledge anchor point generation method based on artificial intelligence, and relates to the technical field of artificial intelligence. The knowledge anchor point generation method based on artificial intelligence provided by the embodiment of the application can be applied to a terminal, a server side and software running in the terminal or the server side. In some embodiments, the terminal may be a smartphone, tablet, laptop, desktop computer, or the like; the server side can be configured into an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and cloud servers for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (content delivery network) and big data and artificial intelligence platforms; the software may be an application or the like that implements the artificial intelligence based knowledge anchor point generation method, but is not limited to the above form.

The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments of the present application will be further explained with reference to the drawings.

As shown in fig. 1, fig. 1 is a flowchart of an artificial intelligence based knowledge anchor generation method provided by an embodiment of the present application, which includes, but is not limited to, steps S100 to S600.

Step S100, acquiring a scheme picture from a network page;

s200, identifying scheme pictures based on a preset optical character recognition network model to obtain a content text;

step S300, extracting the content text to obtain an original keyword;

step S400, judging whether a preset knowledge text set has a knowledge text matched with the original keywords, and if so, determining the original keywords as effective keywords;

step S500, generating a knowledge anchor point at a position corresponding to the effective keyword in the network page;

step S600, a first operation instruction of the user for the knowledge anchor point is triggered, and a knowledge text corresponding to the knowledge anchor point is displayed on a network page.

It should be noted that, first, a scheme picture is obtained from a network page; then, identifying the scheme picture based on a preset optical character identification network model to obtain a content text; extracting the content text to obtain an original keyword; then judging whether a knowledge text matched with the original keywords exists in a preset knowledge text set, and if so, determining the original keywords as effective keywords; then generating a knowledge anchor point at a position corresponding to the effective keyword in the network page; and finally, triggering a first operation instruction of the user for the knowledge anchor point, and displaying the knowledge text corresponding to the knowledge anchor point on a network page. The embodiment shows the relevant explanation of the effective keyword in a knowledge anchor point form, so that the salesman can directly trigger the knowledge anchor point to explain the relevant content in the explanation process without needing to spend time to search the relevant content like the past, the working efficiency of the salesman can be improved, and great convenience is brought to the work of the salesman.

It is worth noting that the artificial intelligence is AI, which is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results.

It should be noted that an Optical Character Recognition (OCR) network model is a network model that uses Optical technology and computer technology to read out characters printed or written on paper and convert the characters into a computer-acceptable and human-understandable format.

It should be noted that the web page may be a display interface of the terminal, and the terminal may be a computer, a mobile phone, or a tablet, etc.; in order to obtain a scheme picture from a network page, screenshot processing is performed on the corresponding network page by using screenshot software carried by the terminal or external screenshot software carried by the terminal in the process of displaying the network page to be identified and processed, so that the scheme picture can be obtained; even the external device can be used for shooting images on the display interface of the terminal, and corresponding scheme pictures can be obtained, wherein the external device is a device with an image acquisition function, such as a mobile phone, a digital camera and the like.

It should be noted that, the optical character recognition network model may perform recognition processing on the acquired scheme picture, and perform recognition processing on characters on the scheme picture to obtain a content text. Extracting the content text to obtain an original keyword; and then judging whether a knowledge text matched with the original keyword exists in the preset knowledge text set, and if so, determining the original keyword as the effective keyword.

It is understood that the knowledge anchor is an online link for explaining valid keywords.

It should be noted that the first operation instruction of the user for the knowledge anchor point may be generated by the user performing a click process or a sliding process on the knowledge anchor point in the web page. Illustratively, when a user clicks a corresponding knowledge anchor point in a web page, a knowledge text related to the knowledge anchor point is displayed in the web page.

In some embodiments, the optical character recognition network model includes an image preprocessing module, a layout processing module and an image segmentation module, a feature extraction module and a recognition module, and in the example of fig. 2, step S200 includes, but is not limited to, steps S210 to S250.

Step S210, preprocessing the scheme picture based on an image preprocessing module to obtain first picture information;

step S220, performing layout processing on the first picture information based on the layout processing module to obtain second picture information;

step S230, carrying out image segmentation processing on the second picture information based on the image segmentation module to obtain third picture information;

step S240, extracting the characteristics of the third picture information based on the characteristic extraction module to obtain image characteristic information;

and step S250, carrying out character recognition processing on the image characteristic information based on the recognition module to obtain a content text.

The optical character recognition network model comprises an image preprocessing module, a layout processing module, an image segmentation module, a feature extraction module and a recognition module; when a scheme picture is processed by using an optical character recognition network model, firstly, the scheme picture is preprocessed based on an image preprocessing module to obtain first picture information; then, performing layout processing on the first picture information based on a layout processing module to obtain second picture information; then, image segmentation processing is carried out on the second picture information based on an image segmentation module to obtain third picture information; then, feature extraction is carried out on the third picture information based on a feature extraction module to obtain image feature information; and finally, character recognition processing is carried out on the image characteristic information based on a recognition module, and a content text can be obtained.

It can be understood that the content text is the text content in the scheme picture; illustratively, the scheme picture is an insurance product introduction page, and the content text obtained after the identification processing is performed by the optical character recognition network model is the product introduction words in the scheme picture.

It should be noted that the image preprocessing module mainly has functions of image binarization and noise removal; the layout processing module is mainly used for segmenting and dividing the document pictures into lines; the image segmentation module mainly solves the problem that characters are difficult to simply cut due to character adhesion and broken strokes; the feature extraction module mainly extracts multi-dimensional features from the character image for subsequent feature matching; the recognition module is mainly used for carrying out template rough classification and template fine matching on the feature vectors extracted from the current characters and the feature template library to recognize the characters.

In the example of fig. 3, step S300 includes, but is not limited to, steps S310 to S320.

Step S310, carrying out matching processing on initial words in the content text to obtain matching scores corresponding to the initial words;

and step S320, under the condition that the matching score is not lower than a preset matching threshold, determining the corresponding initial word as an original keyword.

It is noted that, first, the initial words in the content text are matched to obtain matching scores corresponding to the initial words; under the condition that the matching score is not lower than a preset matching threshold value, determining the corresponding initial word as an original keyword; in the case that the matching score is lower than the preset matching threshold, the corresponding initial word is dropped.

It can be understood that the content text obtained through the recognition of the optical character recognition network model contains a plurality of words, and the words are matched with a preset word set to obtain a plurality of matching scores corresponding to the words; and under the condition that the matching score is not lower than the matching threshold, determining the corresponding word as the original keyword, and making a precondition for the subsequent generation of the knowledge anchor point.

It should be noted that after the plurality of original keywords are obtained through the above steps, it is further required to determine whether a knowledge text matching the original keywords exists in the preset knowledge text set, and if so, the corresponding original keywords are determined as valid keywords.

In the example of fig. 4, step S500 includes, but is not limited to, steps S510 to S520.

Step S510, based on a preset layout display priority, screening effective keywords to obtain final keywords;

step S520, generating a knowledge anchor point in the position corresponding to the final keyword in the network page.

It should be noted that, based on the preset layout display priority, the effective keywords are screened to obtain the final keywords; and then generating a knowledge anchor point corresponding to the position of the final keyword in the network page.

It can be understood that, in order to solve the problem, when the obtained multiple effective keywords are close in position, so that multiple initial knowledge anchors overlap, the multiple effective keywords need to be screened, so that the knowledge anchor obtained finally cannot be found, and the knowledge anchor with the highest priority is selected for display.

In some embodiments, the layout presentation priority comprises an anchor classification priority and a presentation location priority; in the example of fig. 5, step S510 includes, but is not limited to, steps S511 to S512.

Step S511, according to the anchor point classification priority, performing first selection on the effective keywords to obtain first keywords;

and S512, performing second selection on the first keywords according to the display position priority to obtain final keywords.

It is noted that, first, according to the anchor point classification priority, the effective keywords are first selected to obtain first keywords; secondly, performing second selection on the first keywords according to the display position priority to obtain final keywords; and finally, generating a corresponding knowledge anchor point according to the final key word.

For example, the anchor classification priority may be set to product knowledge over disease knowledge, disease knowledge over insurance knowledge; therefore, when one effective keyword corresponds to the related content of the product knowledge, the priority of the effective keyword is higher than that of the related content of the disease knowledge corresponding to the other effective keyword, and the effective keyword with higher priority is preferentially selected to generate a corresponding knowledge anchor point; the priority of the display position can be set to be higher than that of the edge position in the middle position, when the priorities of the two effective keywords are the same, the priority of the effective keyword in the middle position is higher than that of the edge position, and the effective keyword in the middle position is preferentially selected to generate the corresponding knowledge anchor point.

In the example of fig. 6, step S600 includes, but is not limited to, steps S610 to S620.

Step S610, triggering a first operation instruction of a user for the knowledge anchor point, and extracting a knowledge text corresponding to the knowledge anchor point from a knowledge text set;

and step S620, displaying the knowledge text in the network page.

It should be noted that, a first operation instruction of the user for the knowledge anchor point is triggered, and the knowledge text corresponding to the knowledge anchor point is extracted from the knowledge text set; the knowledge text is then presented in a web page.

In the example of fig. 7, step S600 is followed by step S700.

Step S700, triggering a second operation instruction of the user for the knowledge anchor point, and hiding the knowledge text corresponding to the knowledge anchor point displayed at the previous time on the web page.

It should be noted that, the second operation instruction for the knowledge anchor point by the user is triggered, and the knowledge text corresponding to the knowledge anchor point, which is displayed at the previous time, is hidden on the web page, so that the previously displayed knowledge text is hidden, that is, returns to the state before the first operation instruction is received.

It is noted that the second operation instruction of the user for the knowledge anchor point may be generated for the user to click or slide the knowledge anchor point in the web page. Illustratively, the user clicks on the corresponding knowledge anchor point in the web page, and the knowledge text displayed in the web page is hidden, so that the user can trigger clicking on other knowledge anchor points, and the user can conveniently view the other knowledge anchor points.

In addition, as shown in fig. 8, an embodiment of the present application further provides an artificial intelligence-based knowledge anchor point generating apparatus 10, including:

a first processing module 100, configured to obtain a scheme picture from a web page;

the second processing module 200 is configured to perform recognition processing on the scheme picture based on a preset optical character recognition network model to obtain a content text;

the third processing module 300 is configured to extract the content text to obtain an original keyword;

the fourth processing module 400 is configured to determine whether a knowledge text matching the original keyword exists in the preset knowledge text set, and if so, determine the original keyword as an effective keyword;

a fifth processing module 500, configured to generate a knowledge anchor at a position in the web page corresponding to the valid keyword;

the sixth processing module 600 is configured to trigger a first operation instruction of the user for the knowledge anchor point, and display a knowledge text corresponding to the knowledge anchor point on a web page.

In one embodiment, a scheme picture is firstly obtained from a network page; then, identifying the scheme picture based on a preset optical character identification network model to obtain a content text; extracting the content text to obtain an original keyword; then judging whether a knowledge text matched with the original keywords exists in a preset knowledge text set, and if so, determining the original keywords as effective keywords; then generating a knowledge anchor point at a position corresponding to the effective keyword in the network page; and finally, triggering a first operation instruction of the user for the knowledge anchor point, and displaying the knowledge text corresponding to the knowledge anchor point on a network page. The embodiment shows the relevant explanation of the effective keyword in a knowledge anchor point form, so that the salesman can directly trigger the knowledge anchor point to explain the relevant content in the explanation process, the time is not required to be spent to search the relevant content like the past, the working efficiency of the salesman can be improved, and great convenience is brought to the work of the salesman.

In addition, as shown in fig. 9, an embodiment of the present application also provides an electronic device 700, including: memory 710, processor 720, and computer programs stored on memory 710 and operable on processor 720.

The processor 720 and the memory 710 may be connected by a bus or other means.

Non-transitory software programs and instructions required to implement the artificial intelligence based knowledge anchor generation method of the above embodiments are stored in the memory 710, and when executed by the processor 720, perform the artificial intelligence based knowledge anchor generation method of the above embodiments, for example, perform the above-described method steps S100 to S600 in fig. 1, method steps S210 to S250 in fig. 2, method steps S310 to S320 in fig. 3, method steps S510 to S520 in fig. 4, method steps S511 to S512 in fig. 5, method steps S610 to S620 in fig. 6, and method steps S100 to S700 in fig. 7.

The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

Furthermore, an embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, which are executed by a processor 720 or a controller, for example, by a processor 720 in the above device embodiment, and can cause the processor 720 to perform the artificial intelligence-based knowledge anchor point generating method in the above embodiment, for example, the method steps S100 to S600 in fig. 1, the method steps S210 to S250 in fig. 2, the method steps S310 to S320 in fig. 3, the method steps S510 to S520 in fig. 4, the method steps S511 to S512 in fig. 5, the method steps S610 to S620 in fig. 6, and the method steps S100 to S700 in fig. 7, which are described above.

The above embodiments may be combined, and the modules with the same name may be the same or different between different embodiments.

While certain embodiments of the present application have been described above, other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily have to be in the particular order shown or in sequential order to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus, device, and computer-readable storage medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and in relation to the description, reference may be made to some portions of the description of the method embodiments.

The apparatus, the device, the computer-readable storage medium, and the method provided in the embodiments of the present application correspond to each other, and therefore, the apparatus, the device, and the nonvolatile computer storage medium also have advantageous technical effects similar to those of the corresponding method.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the units can be implemented in the same software and/or hardware or in a plurality of software and/or hardware when implementing the embodiment of the present application.

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

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

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.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or Flash memory (Flash RAM). Memory is an example of a computer-readable medium.

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

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

In the embodiments of the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, and means that there may be three relationships, for example, a and/or B, and may mean that a exists alone, a and B exist simultaneously, and B exists alone. Wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c may represent: a, b, c, a and b, a and c, b and c or a and b and c, wherein a, b and c can be single or multiple.

Embodiments of the application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Embodiments of the application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

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

Claims

1. A knowledge anchor point generation method based on artificial intelligence is characterized by comprising the following steps:

acquiring a scheme picture from a network page;

extracting the content text to obtain an original keyword;

2. The artificial intelligence-based knowledge anchor point generating method of claim 1, wherein the optical character recognition network model comprises an image preprocessing module, a layout processing module, an image segmentation module, a feature extraction module and a recognition module, and the method for recognizing the scheme picture based on the preset optical character recognition network model to obtain a content text comprises the following steps:

3. The artificial intelligence based knowledge anchor generation method of claim 1, wherein said extracting the content text to obtain original keywords comprises:

4. The artificial intelligence based knowledge anchor generation method of claim 1, wherein generating knowledge anchors in the web page at locations corresponding to the valid keywords comprises:

5. The artificial intelligence-based knowledge anchor point generation method according to claim 4, wherein the layout display priority includes an anchor point classification priority and a display position priority, and the screening of the effective keywords based on the preset layout display priority to obtain final keywords comprises:

6. The artificial intelligence based knowledge anchor generation method of claim 1, wherein the triggering of the first operation instruction of the user for the knowledge anchor displays the knowledge text corresponding to the knowledge anchor on the web page, and the method comprises:

and displaying the knowledge text in the network page.

7. The artificial intelligence based knowledge anchor generation method of claim 1, wherein after the triggering of the first operation instruction of the user for the knowledge anchor and the displaying of the knowledge text corresponding to the knowledge anchor on the web page, further comprising:

8. An artificial intelligence-based knowledge anchor point generation apparatus, the apparatus comprising:

9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the artificial intelligence based knowledge anchor point generating method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing computer-executable instructions for performing the artificial intelligence-based knowledge anchor generation method of any one of claims 1 to 7.