Artificial intelligence-based knowledge anchor point generation method, device and storage medium

Abstract

The present application relates to artificial intelligence technology and provides a method, device, electronic device and computer-readable storage medium for generating knowledge anchor points based on artificial intelligence. The method includes: obtaining a solution image from a web page; performing recognition processing on the solution image based on a preset optical character recognition network model to obtain content text; extracting the content text to obtain original keywords; determining whether there is a knowledge text matching the original keyword in a preset knowledge text set, and if so, determining the original keyword as a valid keyword; generating a knowledge anchor point at a position corresponding to the valid keyword in the web page; triggering a first operation instruction of the user for the knowledge anchor point, displaying the knowledge text corresponding to the knowledge anchor point on the web page. The above technical solution can improve the work efficiency of salesmen and bring convenience to their work.

Description

Technical Field

[0001] The embodiments of the present application relate to, but are not limited to, the field of artificial intelligence technology, and in particular to a method, device, electronic device, and computer-readable storage medium for generating knowledge anchor points based on artificial intelligence. Background Art

[0002] When visiting clients, insurance practitioners often need to explain some professional plans. When clients are confused about certain professional terms or knowledge points, insurance practitioners need to look up relevant information to explain these professional terms or knowledge points. This will make the entire plan introduction process more time-consuming and affect the work efficiency of insurance practitioners. Summary of the Invention

[0003] 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.

[0004] In order to solve the problems mentioned in the above background technology, the embodiments of the present application provide a knowledge anchor point generation method, device, electronic device and computer-readable storage medium based on artificial intelligence, which can improve the work efficiency of salesmen and bring convenience to the work of salesmen.

[0005] In a first aspect, an embodiment of the present application provides a method for generating knowledge anchor points based on artificial intelligence, the method comprising:

[0006] Get the solution image from the web page;

[0007] Recognize and process the solution image based on a preset optical character recognition network model to obtain content text;

[0008] Extracting the content text to obtain original keywords;

[0009] Determine whether there is a knowledge text matching the original keyword in a preset knowledge text set, and if so, determine the original keyword as a valid keyword;

[0010] Generating a knowledge anchor point at a position corresponding to the effective keyword in the web page;

[0011] Triggered by a first operation instruction from a user for the knowledge anchor point, the knowledge text corresponding to the knowledge anchor point is displayed on the web page.

[0012] According to the embodiment provided by the present application, the method for generating knowledge anchors based on artificial intelligence has at least the following beneficial effects: first, a solution image is obtained from a web page; then, the solution image is recognized and processed based on a preset optical character recognition network model to obtain a content text; then, the content text is extracted to obtain original keywords; then, it is determined whether there is a knowledge text matching the original keyword in the preset knowledge text set, and if so, the original keyword is determined as a valid keyword; then, a knowledge anchor is generated at the position corresponding to the valid keyword in the web page; finally, the first operation instruction of the user for the knowledge anchor is triggered, and the knowledge text corresponding to the knowledge anchor is displayed on the web page. This embodiment displays the relevant explanations of the valid keywords in the form of knowledge anchors, so that the salesperson can directly trigger the knowledge anchor to explain the relevant content during the explanation process, without having to spend time searching for the relevant content as in the past, which can improve the work efficiency of the salesperson and bring great convenience to the salesperson's work.

[0013] 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 a recognition module. The preset optical character recognition network model is used to perform recognition processing on the solution image to obtain the content text, including:

[0014] Preprocessing the scheme image based on the image preprocessing module to obtain first image information;

[0015] Performing layout processing on the first image information based on the layout processing module to obtain second image information;

[0016] performing image segmentation processing on the second image information based on the image segmentation module to obtain the third image information;

[0017] Performing feature extraction on the third image information based on the feature extraction module to obtain image feature information;

[0018] The image feature information is subjected to character recognition processing based on the recognition module to obtain the content text.

[0019] According to some embodiments of the present application, extracting the content text to obtain original keywords includes:

[0020] Performing matching processing on the initial words in the content text to obtain a matching score corresponding to the initial words;

[0021] When the matching score is not lower than a preset matching threshold, the corresponding initial word is determined as the original keyword.

[0022] According to some embodiments of the present application, generating a knowledge anchor point at a position corresponding to the valid keyword in the web page includes:

[0023] Based on the preset layout display priority, the effective keywords are screened to obtain final keywords;

[0024] A knowledge anchor point is generated at a position corresponding to the final keyword in the web page.

[0025] According to some embodiments of the present application, the layout display priority includes an anchor classification priority and a display position priority. The effective keywords are screened based on the preset layout display priority to obtain the final keywords, including:

[0026] performing a first selection on the valid keywords according to the anchor point classification priority to obtain a first keyword;

[0027] According to the display position priority, a second selection is performed on the first keyword to obtain the final keyword.

[0028] According to some embodiments of the present application, the triggering of the first operation instruction of the user on the knowledge anchor point to display the knowledge text corresponding to the knowledge anchor point on the web page includes:

[0029] Triggered by a first operation instruction of a user for the knowledge anchor point, extracting the knowledge text corresponding to the knowledge anchor point from the knowledge text set;

[0030] The knowledge text is displayed on the web page.

[0031] According to some embodiments of the present application, after the first operation instruction of the user for the knowledge anchor point is triggered and the knowledge text corresponding to the knowledge anchor point is displayed on the web page, the method further includes:

[0032] Triggered by a second operation instruction of the user for the knowledge anchor point, the knowledge text corresponding to the knowledge anchor point displayed at a previous moment on the web page is hidden.

[0033] In a second aspect, an embodiment of the present application further provides an artificial intelligence-based knowledge anchor point generation device, the device comprising:

[0034] The first processing module is used to obtain a solution image from a web page;

[0035] A second processing module is used to perform recognition processing on the solution image based on a preset optical character recognition network model to obtain content text;

[0036] A third processing module is used to extract the content text to obtain original keywords;

[0037] A fourth processing module is used to determine whether there is a knowledge text matching the original keyword in a preset knowledge text set, and if so, determine the original keyword as a valid keyword;

[0038] A fifth processing module, configured to generate a knowledge anchor point at a position corresponding to the valid keyword in the web page;

[0039] The sixth processing module is used to trigger the user's first operation instruction for the knowledge anchor point, and display the knowledge text corresponding to the knowledge anchor point on the network page.

[0040] In a third aspect, an embodiment of the present application further provides an electronic device comprising: 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, the method for generating knowledge anchor points based on artificial intelligence as described in the first aspect above is implemented.

[0041] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the artificial intelligence-based knowledge anchor point generation method as described in the first aspect above.

[0042] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purposes and other advantages of the present application can be realized and obtained through the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are used to provide a further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0044] Figure 1 This is a flowchart of a method for generating knowledge anchor points based on artificial intelligence provided by one embodiment of the present application;

[0045] Figure 2 This is a flowchart of obtaining content text in the artificial intelligence-based knowledge anchor point generation method provided by one embodiment of the present application;

[0046] Figure 3 This is a flowchart for determining original keywords in the method for generating knowledge anchor points based on artificial intelligence provided by one embodiment of the present application;

[0047] Figure 4This is a flowchart of generating a knowledge anchor point in a method for generating a knowledge anchor point based on artificial intelligence provided by one embodiment of the present application;

[0048] Figure 5 This is a flowchart for selecting final keywords in the method for generating knowledge anchor points based on artificial intelligence provided by one embodiment of the present application;

[0049] Figure 6 This is a flowchart showing knowledge text in a method for generating knowledge anchor points based on artificial intelligence provided by one embodiment of the present application;

[0050] Figure 7 is a flowchart of a method for generating knowledge anchor points based on artificial intelligence provided by another embodiment of the present application;

[0051] Figure 8 This is a schematic diagram of a knowledge anchor point generation device based on artificial intelligence provided by one embodiment of the present application;

[0052] Figure 9 This is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0054] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, used in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0055] It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0056] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0057] AI is a new technical discipline that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. Artificial intelligence is a branch of computer science that seeks to understand the essence of intelligence and produce new intelligent machines that can respond in a manner similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. Artificial intelligence can simulate the information processes of human consciousness and thinking. It also refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0058] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0059] The servers involved in artificial intelligence technology can be independent servers or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), as well as big data and artificial intelligence platforms.

[0060] The present application provides a method, device, electronic device and computer-readable storage medium for generating knowledge anchor points based on artificial intelligence, which first obtains a solution image from a web page; then identifies and processes the solution image based on a preset optical character recognition network model to obtain a content text; then extracts the content text to obtain original keywords; then determines whether there is a knowledge text matching the original keyword in the preset knowledge text set, and if so, determines the original keyword as a valid keyword; then generates a knowledge anchor point at the position corresponding to the valid keyword in the web page; finally, triggers the user's first operation instruction for the knowledge anchor point, and displays the knowledge text corresponding to the knowledge anchor point on the web page. This embodiment displays the relevant explanations of the valid keywords in the form of knowledge anchor points, so that the salesperson can directly trigger the knowledge anchor point to explain the relevant content during the explanation process, without having to spend time searching for the relevant content as in the past, which can improve the salesperson's work efficiency and bring great convenience to the salesperson's work.

[0061] The method for generating knowledge anchor points based on artificial intelligence provided in the embodiment of the present application relates to the field of artificial intelligence technology. The method for generating knowledge anchor points based on artificial intelligence provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the method for generating knowledge anchor points based on artificial intelligence, etc., but is not limited to the above forms.

[0062] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0063] The embodiments of the present application are further described below with reference to the accompanying drawings.

[0064] like Figure 1 As shown, Figure 1 This is a flowchart of an artificial intelligence-based knowledge anchor point generation method provided by an embodiment of the present application. The artificial intelligence-based knowledge anchor point generation method includes but is not limited to steps S100 to S600.

[0065] Step S100, obtaining a solution image from a web page;

[0066] Step S200: performing recognition processing on the solution image based on a preset optical character recognition network model to obtain content text;

[0067] Step S300: extract the content text to obtain original keywords;

[0068] Step S400: determining whether there is a knowledge text matching the original keyword in the preset knowledge text set; if so, determining the original keyword as a valid keyword;

[0069] Step S500, generating a knowledge anchor point at a location corresponding to a valid keyword in a web page;

[0070] Step S600 is triggered by the user's first operation instruction for the knowledge anchor point, and the knowledge text corresponding to the knowledge anchor point is displayed on the web page.

[0071] It should be noted that, first, a solution image is obtained from a web page; then, the solution image is recognized and processed based on a preset optical character recognition network model to obtain a content text; then, the content text is extracted to obtain original keywords; then, it is determined whether there is a knowledge text matching the original keyword in the preset knowledge text set. If so, the original keyword is determined as a valid keyword; then, a knowledge anchor point is generated at the position corresponding to the valid keyword in the web page; finally, the first operation instruction of the user for the knowledge anchor point is triggered, and the knowledge text corresponding to the knowledge anchor point is displayed on the web page. This embodiment displays the relevant explanations of the valid keywords in the form of knowledge anchor points, so that the salesperson can directly trigger the knowledge anchor point to explain the relevant content during the explanation process, without having to spend time searching for the relevant content as in the past. This can improve the work efficiency of the salesperson and bring great convenience to the salesperson's work.

[0072] It is worth noting that artificial intelligence is AI. AI is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0073] It is worth noting that the Optical Character Recognition (OCR) network model uses optical technology and computer technology to read text printed or written on paper and convert it into a format that can be accepted by computers and understood by humans.

[0074] It should be noted that the web page can be the display interface of the terminal, and the terminal can be a computer, mobile phone or tablet, etc.; in order to obtain the solution picture from the web page, it is only necessary to use the screenshot software that comes with the terminal or the external screenshot software to take a screenshot of the corresponding web page during the display of the web page to be identified and processed, and the solution picture can be obtained; you can even use an external device to capture the image of the terminal's display interface, and you can also get the corresponding solution picture, and the external device is a device with image acquisition function, such as a mobile phone, digital camera, etc.

[0075] It should be noted that the optical character recognition network model can process the acquired solution image and the text on the solution image to obtain the content text. The content text is then extracted to obtain the original keywords. It then determines whether there is any knowledge text in the preset knowledge text set that matches the original keywords. If so, the original keywords are determined to be valid keywords.

[0076] It can be understood that a knowledge anchor is an online link that explains effective keywords.

[0077] It should be noted that the user's first operation instruction for the knowledge anchor point can be generated by the user clicking or sliding on the knowledge anchor point in the web page. For example, when the user clicks on the corresponding knowledge anchor point in the web page, the web page will display the knowledge text related to the knowledge anchor point.

[0078] In some embodiments, the optical character recognition network model includes an image preprocessing module, a layout processing module, an image segmentation module, a feature extraction module and a recognition module. Figure 2 In the example, step S200 includes but is not limited to steps S210 to S250.

[0079] Step S210, preprocessing the solution image based on the image preprocessing module to obtain first image information;

[0080] Step S220, performing layout processing on the first image information based on the layout processing module to obtain second image information;

[0081] Step S230, performing image segmentation processing on the second image information based on the image segmentation module to obtain third image information;

[0082] Step S240, performing feature extraction on the third image information based on the feature extraction module to obtain image feature information;

[0083] Step S250: performing character recognition processing on the image feature information based on the recognition module to obtain content text.

[0084] It should be noted that 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; when using the optical character recognition network model to process the solution image, the solution image is first preprocessed based on the image preprocessing module to obtain the first image information; then the first image information is subjected to layout processing based on the layout processing module to obtain the second image information; then the second image information is subjected to image segmentation processing based on the image segmentation module to obtain the third image information; then the third image information is subjected to feature extraction based on the feature extraction module to obtain image feature information; finally, the image feature information is subjected to character recognition processing based on the recognition module to obtain the content text.

[0085] It can be understood that the content text is the text content in the solution picture; for example, the solution picture is an insurance product introduction page, and the content text obtained after recognition and processing by the optical character recognition network model is the product introduction text in the solution picture.

[0086] It should be noted that the main functions of the image preprocessing module include image binarization and noise removal; the layout processing module mainly divides the document image into paragraphs and lines; the image segmentation module mainly deals with 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 mainly performs coarse template classification and fine template matching on the feature vector extracted from the current character and the feature template library to identify the character.

[0087] exist Figure 3 In the example, step S300 includes but is not limited to steps S310 to S320.

[0088] Step S310, performing matching processing on the initial words in the content text to obtain a matching score corresponding to the initial words;

[0089] Step S320: If the matching score is not lower than a preset matching threshold, the corresponding initial word is determined as the original keyword.

[0090] It should be noted that the initial words in the content text are first matched to obtain a matching score corresponding to the initial words; if the matching score is not lower than the preset matching threshold, the corresponding initial words are determined as original keywords; if the matching score is lower than the preset matching threshold, the corresponding initial words are discarded.

[0091] It can be understood that the content text obtained by the optical character recognition network model contains multiple words, and these words are matched with the preset word set to obtain multiple matching scores corresponding to the words; only when the matching score is not lower than the matching threshold will the corresponding word be determined as the original keyword, making prerequisite preparations for the subsequent knowledge anchor point generation.

[0092] It should be noted that after obtaining multiple original keywords through the above steps, it is still necessary to determine whether there is a knowledge text matching the original keyword in the preset knowledge text set. If so, the corresponding original keyword will be determined as a valid keyword.

[0093] exist Figure 4 In the example, step S500 includes but is not limited to steps S510 to S520.

[0094] Step S510: Filter the valid keywords based on the preset layout display priority to obtain the final keywords;

[0095] Step S520: Generate a knowledge anchor point at a position corresponding to the final keyword in the web page.

[0096] It should be noted that, based on the preset layout display priority, the effective keywords are screened to obtain the final keywords; then, a knowledge anchor point is generated at the position corresponding to the final keyword in the web page.

[0097] It is understandable that when multiple valid keywords are obtained and multiple initial knowledge anchor points overlap due to their close positions, in order to solve this problem, it is necessary to filter and process such multiple valid keywords so that the final knowledge anchor point will not have such a situation, and select the knowledge anchor point with the highest priority for display.

[0098] In some embodiments, the layout display priority includes an anchor classification priority and a display position priority; Figure 5 In the example, step S510 includes but is not limited to steps S511 to S512.

[0099] Step S511, performing a first selection on valid keywords according to the anchor point classification priority to obtain a first keyword;

[0100] Step S512: performing a second selection on the first keyword according to the display position priority to obtain a final keyword.

[0101] It should be noted that first, according to the anchor classification priority, the effective keywords are selected to obtain the first keyword; then according to the display position priority, the first keyword is selected to obtain the final keyword; finally, the corresponding knowledge anchor point is generated according to the final keyword.

[0102] Exemplarily, the anchor classification priority can be set so that product knowledge is higher than disease knowledge, and disease knowledge is higher than insurance knowledge; therefore, when a valid keyword corresponds to the relevant content of product knowledge, its priority will be higher than the priority of another valid keyword corresponding to the relevant content of disease knowledge, and the valid keyword with higher priority will be selected first to generate the corresponding knowledge anchor point; the display position priority can be set so that the priority of the middle position is higher than the priority of the edge position. When the priorities of two valid keywords are the same, the priority of the valid keyword in the middle position will be higher than the priority of the edge position, and the valid keyword in the middle position will be selected first to generate the corresponding knowledge anchor point.

[0103] exist Figure 6 In the example, step S600 includes but is not limited to steps S610 to S620.

[0104] Step S610, triggered by a user's first operation instruction for a knowledge anchor point, extracting knowledge text corresponding to the knowledge anchor point from the knowledge text set;

[0105] Step S620: display the knowledge text on the web page.

[0106] It should be noted that, triggered by the user's first operation instruction on the knowledge anchor point, the knowledge text corresponding to the knowledge anchor point is extracted from the knowledge text set; and then the knowledge text is displayed on the web page.

[0107] It should be noted that the user's first operation instruction for the knowledge anchor point can be generated by the user clicking or sliding on the knowledge anchor point in the web page. For example, when the user clicks on the corresponding knowledge anchor point in the web page, the web page will display the knowledge text related to the knowledge anchor point.

[0108] exist Figure 7 In the example, step S600 also includes but is not limited to step S700.

[0109] Step S700 is triggered by the user's second operation instruction for the knowledge anchor point, and the knowledge text corresponding to the knowledge anchor point displayed at the previous moment is hidden on the web page.

[0110] It should be noted that, when triggered by the user's second operation instruction for the knowledge anchor point, the knowledge text corresponding to the knowledge anchor point displayed at the previous moment on the web page is hidden, so that the previously displayed knowledge text is hidden, that is, it returns to the state before receiving the first operation instruction.

[0111] It is worth noting that the user's second operation instruction for the knowledge anchor point can be generated by the user clicking or sliding on the knowledge anchor point on the web page. For example, when the user clicks on the corresponding knowledge anchor point on the web page, the knowledge text already displayed on the web page will be hidden, allowing the user to trigger and click on other knowledge anchor points to facilitate the user to view other knowledge anchor points.

[0112] In addition, if Figure 8 As shown, an embodiment of the present application further provides an artificial intelligence-based knowledge anchor point generation device 10, comprising:

[0113] The first processing module 100 is used to obtain a solution image from a web page;

[0114] The second processing module 200 is used to perform recognition processing on the solution image based on a preset optical character recognition network model to obtain content text;

[0115] The third processing module 300 is used to extract the content text to obtain original keywords;

[0116] The fourth processing module 400 is used to determine whether there is a knowledge text matching the original keyword in the preset knowledge text set, and if so, determine the original keyword as a valid keyword;

[0117] The fifth processing module 500 is used to generate a knowledge anchor point at a position corresponding to a valid keyword in the web page;

[0118] The sixth processing module 600 is used to trigger the user's first operation instruction for the knowledge anchor point, and display the knowledge text corresponding to the knowledge anchor point on the web page.

[0119] In one embodiment, a solution image is first obtained from a web page; then, the solution image is recognized and processed based on a preset optical character recognition network model to obtain a content text; then, the content text is extracted to obtain original keywords; then, it is determined whether there is a knowledge text matching the original keyword in the preset knowledge text set. If so, the original keyword is determined as a valid keyword; then, a knowledge anchor point is generated at the position corresponding to the valid keyword in the web page; finally, the first operation instruction of the user for the knowledge anchor point is triggered, and the knowledge text corresponding to the knowledge anchor point is displayed on the web page. This embodiment displays the relevant explanations of the valid keywords in the form of knowledge anchor points, so that the salesperson can directly trigger the knowledge anchor point to explain the relevant content during the explanation process, without having to spend time searching for the relevant content as in the past. This can improve the work efficiency of the salesperson and bring great convenience to the salesperson's work.

[0120] In addition, if Figure 9 As shown, an embodiment of the present application further provides an electronic device 700 , which includes: a memory 710 , a processor 720 , and a computer program stored in the memory 710 and executable on the processor 720 .

[0121] The processor 720 and the memory 710 may be connected via a bus or other means.

[0122] The non-transient software program and instructions required to implement the above-mentioned artificial intelligence-based knowledge anchor point generation method are stored in the memory 710. When executed by the processor 720, the above-mentioned artificial intelligence-based knowledge anchor point generation method is executed. For example, the above-mentioned method is executed. Figure 1 Method steps S100 to S600, Figure 2 Steps S210 to S250 of the method, Figure 3 Method steps S310 to S320, Figure 4 Method steps S510 to S520, Figure 5 Steps S511 to S512 of the method, Figure 6 Steps S610 to S620 of the method, Figure 7 Method steps S100 to S700 in .

[0123] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0124] In addition, an embodiment of the present application further provides a computer-readable storage medium, which stores computer-executable instructions. The computer-executable instructions are executed by a processor 720 or a controller, for example, by a processor 720 in the above-mentioned device embodiment, so that the processor 720 can execute the artificial intelligence-based knowledge anchor point generation method in the above-mentioned embodiment, for example, execute the above-described Figure 1 Method steps S100 to S600, Figure 2 Steps S210 to S250 of the method, Figure 3 Method steps S310 to S320, Figure 4 Method steps S510 to S520, Figure 5 Steps S511 to S512 of the method, Figure 6 Steps S610 to S620 of the method, Figure 7 Method steps S100 to S700 in .

[0125] The above embodiments may be used in combination, and modules with the same name in different embodiments may be the same or different.

[0126] The foregoing description describes specific embodiments of the present application, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0127] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, equipment, and computer-readable storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0128] The apparatus, device, computer-readable storage medium and method provided in the embodiments of the present application correspond to each other. Therefore, the apparatus, device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, device and computer storage medium will not be repeated here.

[0129] In the 1990s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements today can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD through their own programming, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly done using "logic compiler" software. This is similar to the software compiler used when developing programs. Before compilation, the original code must also be written in a specific programming language, called a hardware description language (HDL). There is not just one HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages ​​and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0130] The controller can be implemented in any suitable manner. For example, the controller can take the form of 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, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that in addition to implementing the controller in a purely computer-readable program code format, the controller can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules that implement the method and structures within the hardware component.

[0131] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, 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.

[0132] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing the embodiments of the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0133] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0134] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0135] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0136] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0137] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0138] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0139] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules, 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 disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that 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 (transitory media), such as modulated data signals and carrier waves.

[0140] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0141] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next 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 single or plural items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c or a and b and c, where a, b, c can be single or multiple.

[0142] Embodiments of the present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. Embodiments of the present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0143] The various embodiments in this application are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiment is generally similar to the method embodiment, so the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment.

[0144] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for generating knowledge anchor points based on artificial intelligence, characterized in that: The method comprises: Get the solution image from the web page; Recognize and process the solution image based on a preset optical character recognition network model to obtain content text; Extracting the content text to obtain original keywords; Determine whether there is a knowledge text matching the original keyword in a preset knowledge text set, and if so, determine the original keyword as a valid keyword; Generating a knowledge anchor point at a position corresponding to the effective keyword in the web page; Triggered by a first operation instruction of a user for the knowledge anchor point, the knowledge text corresponding to the knowledge anchor point is displayed on the web page; The step of generating a knowledge anchor point at a position corresponding to the valid keyword in the web page includes: Based on the preset layout display priority, the effective keywords are screened to obtain final keywords; Generating a knowledge anchor point at a position corresponding to the final keyword in the web page; The layout display priority includes an anchor classification priority and a display position priority. The effective keywords are screened based on the preset layout display priority to obtain final keywords, including: performing a first selection on the valid keywords according to the anchor point classification priority to obtain a first keyword; According to the display position priority, a second selection is performed on the first keyword to obtain the final keyword.

2. The method for generating knowledge anchor points based on artificial intelligence according to claim 1, characterized in that: The optical character recognition network model includes an image preprocessing module, a layout processing module, an image segmentation module, a feature extraction module, and a recognition module. The preset optical character recognition network model is used to perform recognition processing on the solution image to obtain the content text, including: Preprocessing the scheme image based on the image preprocessing module to obtain first image information; Performing layout processing on the first image information based on the layout processing module to obtain second image information; performing image segmentation processing on the second image information based on the image segmentation module to obtain third image information; Performing feature extraction on the third image information based on the feature extraction module to obtain image feature information; The image feature information is subjected to character recognition processing based on the recognition module to obtain the content text.

3. The method for generating knowledge anchor points based on artificial intelligence according to claim 1, wherein extracting the content text to obtain original keywords comprises: Performing matching processing on the initial words in the content text to obtain a matching score corresponding to the initial words; When the matching score is not lower than a preset matching threshold, the corresponding initial word is determined as the original keyword.

4. The method for generating knowledge anchor points based on artificial intelligence according to claim 1, characterized in that: The triggering of the first operation instruction of the user on the knowledge anchor point to display the knowledge text corresponding to the knowledge anchor point on the web page includes: Triggered by a first operation instruction of a user for the knowledge anchor point, extracting the knowledge text corresponding to the knowledge anchor point from the knowledge text set; The knowledge text is displayed on the web page.

5. The method for generating knowledge anchor points based on artificial intelligence according to claim 1, characterized in that: After the first operation instruction triggered by the user for the knowledge anchor point is displayed on the web page, the method further includes: Triggered by a second operation instruction of the user for the knowledge anchor point, the knowledge text corresponding to the knowledge anchor point displayed at a previous moment on the web page is hidden.

6. A knowledge anchor point generation device based on artificial intelligence, characterized in that: The device comprises: The first processing module is used to obtain a solution image from a web page; A second processing module is used to perform recognition processing on the solution image based on a preset optical character recognition network model to obtain content text; A third processing module is used to extract the content text to obtain original keywords; A fourth processing module is used to determine whether there is a knowledge text matching the original keyword in a preset knowledge text set, and if so, determine the original keyword as a valid keyword; A fifth processing module, configured to generate a knowledge anchor point at a position corresponding to the valid keyword in the web page; A sixth processing module, configured to trigger, upon a user's first operation instruction for the knowledge anchor point, display the knowledge text corresponding to the knowledge anchor point on the web page; The step of generating a knowledge anchor point at a position corresponding to the valid keyword in the web page includes: Based on the preset layout display priority, the effective keywords are screened to obtain final keywords; Generating a knowledge anchor point at a position corresponding to the final keyword in the web page; The layout display priority includes an anchor classification priority and a display position priority. The effective keywords are screened based on the preset layout display priority to obtain final keywords, including: performing a first selection on the valid keywords according to the anchor point classification priority to obtain a first keyword; According to the display position priority, a second selection is performed on the first keyword to obtain the final keyword.

7. An electronic device comprising: 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, the method for generating knowledge anchor points based on artificial intelligence as described in any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the artificial intelligence-based knowledge anchor point generation method according to any one of claims 1 to 5.

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