Coal fusion text generation method and device based on deep learning and electronic equipment

Abstract

The invention provides a coal fusion text generation method and device based on deep learning and electronic equipment, and relates to the technical field of computers. The method comprises the following steps: responding to a login success operation of a target object, and entering a workbench page; on the workbench page, in response to a selection operation of the target object on the article mode, generating a basic article, in response to an editing operation on the basic article, entering a text editor, the text editor being used for editing the basic article; on a page of the text editor, in response to a selection operation on an editing button by the target object, displaying an editing page corresponding to the editing button, displaying an editing result corresponding to the editing button on the editing page, and replacing the basic article with the editing result to generate an initial coal fusion text; in response to a click operation of a target object on a text optimization button, optimizing the initial coal fusion text to obtain a target coal fusion text; and the target coal fusion text is published, so that the coal fusion text can be flexibly and automatically generated.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and specifically to a method, device and electronic device for generating coal melting text based on deep learning. Background Art

[0002] With the rapid development of the era of integrated media, the demand for text generation and optimization is growing. Traditional text generation and optimization methods rely on manual writing and revision. During the creation process, writers may encounter problems such as no content to write and inappropriate content expression, and writing efficiency is difficult to guarantee. In related technologies, the use of a rule-based automatic text generation system to generate coal-melting text can avoid the problem of low manual writing efficiency. However, the rule-based automatic text generation system mainly relies on preset rule templates and corpora. For complex and changeable natural languages, the number and complexity of rules are limited, and text generation cannot be flexibly performed. Summary of the invention

[0003] The present application provides a coal melting text generation method, device and electronic device based on deep learning, which can flexibly and automatically generate coal melting text and improve writing efficiency.

[0004] The technical solution of the embodiment of the present application is as follows: In a first aspect, an embodiment of the present application provides a method for generating coal melting text based on deep learning, the method comprising: In response to a successful login operation of the target object, entering the workbench page; On the workbench page, in response to the target object's selection operation of an article mode, a basic article is generated according to the selected article mode, and in response to the editing operation of the basic article, a text editor is entered, and the text editor is used to edit the basic article; On the page of the text editor, in response to the target object selecting an edit button, an edit page corresponding to the edit button is displayed, an edit result corresponding to the edit button is displayed on the edit page, and the basic article is replaced with the edit result to generate an initial coal melting text; In response to the target object clicking on a text optimization button, optimizing the initial coal melting text to obtain a target coal melting text; The target coal melting text is published.

[0005] In the above technical scheme, first, in response to the successful login operation of the target object, the workbench page is entered; on the workbench page, in response to the target object's selection operation of the article mode, a basic article is generated according to the selected article mode, and in response to the editing operation of the basic article, the text editor is entered. The text editor is used to edit the basic article. By selecting the article mode, article generation in different modes can be achieved, which has strong flexibility; on the page of the text editor, in response to the target object's selection operation of the edit button, the edit page corresponding to the edit button is displayed, the editing result corresponding to the edit button is displayed on the edit page, and the basic article is replaced with the editing result to generate an initial coal melting text. By editing the basic article in the text editor, the flexibility of writing can be further improved, and after clicking the edit button on the editing page, the editing result corresponding to the edit button is automatically displayed, and the text is automatically generated, thereby improving the writing efficiency; in response to the target object's clicking operation on the text optimization button, the initial coal melting text is optimized to obtain the target coal melting text. By optimizing the text, the accuracy of the generated text can be further improved; the generated target coal melting text is published for easy viewing.

[0006] In some embodiments of the present application, the article mode includes an intelligent recognition mode; In response to the target object's selection of an article mode, generating a basic article according to the selected article mode includes: In response to the target object's selection operation of the intelligent recognition mode, receiving uploaded files to be recognized according to the selected intelligent recognition mode, wherein the files to be recognized include voice files and picture files; Performing text conversion on the voice file and / or the picture file to obtain a converted edited text; The converted edited text is cleaned to generate the basic article.

[0007] In some embodiments of the present application, the article mode includes a real-time news mode; In response to the target object's selection of an article mode, generating a basic article according to the selected article mode includes: In response to the target object's selection operation of the real-time news mode, receiving crawled news articles according to the selected real-time news mode; The news articles are assigned news popularity according to preset indicators, and the news articles corresponding to the news popularity ranked first are used as the basic articles.

[0008] In some embodiments of the present application, the editing button includes an auxiliary writing button; The method comprises: displaying an edit page corresponding to the edit button in response to the target object selecting an edit button on the text editor page, displaying an edit result corresponding to the edit button on the edit page, and replacing the basic article with the edit result to generate an initial coal melting text, including: On the page of the text editor, in response to the target object selecting an auxiliary writing button, an auxiliary writing page corresponding to the auxiliary writing button is displayed, and auxiliary writing is performed on the basic article to obtain an auxiliary writing result; The auxiliary writing result is displayed on the auxiliary writing page, and the basic article is replaced by the auxiliary writing result to generate an initial coal melting text.

[0009] In some embodiments of the present application, the step of performing assisted writing on the basic article to obtain an assisted writing result includes: Performing text preprocessing on the basic article to obtain a preprocessed text, wherein the preprocessing process includes word segmentation, part-of-speech tagging, and removal of stop words; Performing feature extraction on the preprocessed text to obtain extracted features; A text title is generated according to the extracted features, and in response to a determination operation of the target object on the text title, the text title is added to the basic article to obtain the auxiliary writing result.

[0010] In some embodiments of the present application, the editing button includes a short sentence recommendation button; The method comprises: displaying an edit page corresponding to the edit button in response to the target object selecting an edit button on the text editor page, displaying an edit result corresponding to the edit button on the edit page, and replacing the basic article with the edit result to generate an initial coal melting text, including: On the page of the text editor, in response to the target object selecting a short sentence recommendation button, a recommendation page corresponding to the short sentence recommendation button is displayed, and short sentences are recommended for the basic article to obtain recommended short sentences; The recommended short sentences are displayed in the recommendation page in the form of a list, and in response to the target object selecting the recommended short sentence in the list, the recommended short sentence is inserted into the basic article to obtain the initial coal melting text.

[0011] In some embodiments of the present application, after entering the workbench page in response to the successful login operation of the target object, the method further includes: On the workbench page, in response to the target object's selection operation of a function button, displaying the function editing page; On the function editing page, in response to the input operation of the target object, functionally edit the input content to obtain an editing result; The editing result is imported into the text editing library for editing again.

[0012] In a second aspect, an embodiment of the present application provides a coal melting text generation device based on deep learning, the device comprising: A response module, used to respond to a successful login operation of the target object and enter the workbench page; A text editing module, for generating a basic article according to the selected article mode in response to the target object's selection operation on the workbench page, and for entering a text editor in response to an editing operation on the basic article, wherein the text editor is used to edit the basic article; A text generation module is used to display an edit page corresponding to the edit button on the page of the text editor in response to the target object selecting an edit button, display an edit result corresponding to the edit button on the edit page, and replace the basic article with the edit result to generate an initial coal melting text; A text optimization module, configured to optimize the initial coal melting text in response to the target object clicking the text optimization button to obtain a target coal melting text; A text publishing module is used to publish the target coal melting text.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a user interface, a communication bus and a network interface, wherein the processor, the memory, the user interface and the network interface are respectively connected to the communication bus, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods provided in the first aspect.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any one of the methods provided in the first aspect is executed.

[0015] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Due to the adoption of first responding to the successful login operation of the target object, entering the workbench page; on the workbench page, responding to the target object's selection operation of the article mode, generating a basic article according to the selected article mode, responding to the editing operation of the basic article, entering the text editor, the text editor is used to edit the basic article, by selecting the article mode, it can realize the generation of articles in different modes, with strong flexibility; on the page of the text editor, responding to the target object's selection operation of the edit button, displaying the edit page corresponding to the edit button, displaying the editing result corresponding to the edit button on the edit page, and using the editing result to replace the basic article, generating the initial coal melting text, by editing the basic article in the text editor, the flexibility of writing can be further improved, and after clicking the edit button on the edit page, the editing result corresponding to the edit button is automatically displayed, and the text is automatically generated, which improves the writing efficiency; in response to the target object's click operation on the text optimization button, the initial coal melting text is optimized to obtain the target coal melting text, and by optimizing the text, the accuracy of the generated text can be further improved; the generated target coal melting text is published for easy viewing. Therefore, the problem that text generation cannot be flexibly performed in the related technology is effectively solved.

[0016] 2. It can generate and edit different types of texts and automatically generate texts, which is not only flexible but also able to generate more accurate coal melting texts.

[0017] 3. It can also generate text based on the content input by the target object, realizing text generation in various ways with higher flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a method for generating coal melting text based on deep learning provided by an embodiment of the present application; Figure 2 yes Figure 1 A schematic flow chart of a sub-step of step S200; Figure 3 yes Figure 1 Another sub-step flow chart of step S200; Figure 4 is a flowchart of a method for generating coal melting text based on deep learning provided by another embodiment of the present application; Figure 5 It is a schematic diagram of the overall process of a method for generating coal melting text based on deep learning provided by an embodiment of the present application; Figure 6 It is a structural schematic diagram of a coal melting text generation device based on deep learning provided by an embodiment of the present application; Figure 7It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0020] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0021] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0022] The embodiment of the present application provides a method, device, electronic device and readable storage medium for generating coal-melting text based on deep learning. The method for generating coal-melting text based on deep learning first enters a workbench page in response to a successful login operation of a target object; on the workbench page, in response to an article mode selection operation of the target object, a basic article is generated according to the selected article mode; in response to an editing operation on the basic article, a text editor is entered, and the text editor is used to edit the basic article. By selecting an article mode, article generation in different modes can be achieved, and the method has strong flexibility; on the page of the text editor, in response to an edit button selection operation of the target object, a text editor is entered. The editing page corresponding to the edit button is displayed, the editing result corresponding to the edit button is displayed on the editing page, and the basic article is replaced by the editing result to generate an initial coal-melting text. By editing the basic article in the text editor, the flexibility of writing can be further improved. After clicking the edit button on the editing page, the editing result corresponding to the edit button is automatically displayed, and the text is automatically generated, thereby improving the writing efficiency. In response to the target object clicking the text optimization button, the initial coal-melting text is optimized to obtain the target coal-melting text. By optimizing the text, the accuracy of the generated text can be further improved. The generated target coal-melting text is published for easy viewing.

[0023] It should be noted that the deep learning-based method for generating coal-fired text is applied to the generation and publication of coal-fired text, including the writing of real-time news, public account articles, short videos, journals, etc. The text can also be translated to achieve the publication of texts in multiple languages.

[0024] The technical solution provided in the embodiments of the present application is further described below in conjunction with the accompanying drawings.

[0025] Reference Figure 1 and Figure 5 , Figure 1 It is a flowchart of a coal melting text generation method based on deep learning provided in an embodiment of the present application. The coal melting text generation method based on deep learning is applied to a coal melting text generation device based on deep learning, and the coal melting text generation method based on deep learning is executed by a processor in an electronic device or a readable storage medium. The coal melting text generation method based on deep learning includes step S100, step S200, step S300, step S400 and step S500.

[0026] Step S100, in response to a successful login operation of the target object, entering the workbench page.

[0027] In one embodiment, the target object can be a user or an automatic operation robot. The following embodiments are all described by taking the target object as a user. The workbench page is the general page for displaying various functional modules for text generation, including AI creation, document upload, keyword search, article rewriting, dictionary, etc. First, the coal-melting system displays the login page, and the user logs in on the login page. For example, there are two login methods, account password login and mobile phone number SMS login, and the user logs in using a registered account. If you are not registered, register an account first and then log in. Send a login request to the background. If the login is successful, jump to the workbench page so that the article generation operation can be performed on the workbench page later. If the login is unsuccessful, jump to the login interface, prompting the user that the login is unsuccessful and verification is required.

[0028] Step S200, on the workbench page, in response to the target object's selection operation on the article mode, a basic article is generated according to the selected article mode, and in response to the editing operation on the basic article, a text editor is entered, and the text editor is used to edit the basic article.

[0029] In one embodiment, since there are multiple functional modules on the workbench page, the target object, i.e., the user, can click and select the functional modules on the workbench page. The user selects the article generation mode, and in response to the user's selection operation on the article mode, a basic article is generated according to the selected article mode. By selecting the article mode, article generation in different modes can be achieved, which has strong flexibility. The basic article can also be edited. In response to the user's click operation on editing the basic article, the text editor is entered to achieve the corresponding editing operation on the basic article. Among them, the text editor includes a phrase recommendation button, a translation button, a rewrite button, etc., which are used to edit the basic article in different styles to improve the flexibility of text generation.

[0030] In one embodiment, the article mode includes an intelligent recognition mode. Figure 2 As shown, in response to the target object's selection operation of the article mode, a basic article is generated according to the selected article mode, including but not limited to the following steps: Step S210, in response to the target object's selection operation of the intelligent recognition mode, receiving uploaded files to be recognized according to the selected intelligent recognition mode, wherein the files to be recognized include voice files and picture files.

[0031] In some possible embodiments of the present application, when the article mode is the intelligent recognition mode, the user clicks to select the intelligent recognition mode, and in response to the target object's selection operation of the intelligent recognition mode, a file upload interface is provided according to the selected intelligent recognition mode, and the user can upload the file to be recognized. The system receives the uploaded file to be recognized so that the basic article can be generated based on the file to be recognized later. Among them, the file to be recognized includes voice files and picture files. The voice files support multiple formats such as MP3, MP4, AAC, etc., and the picture files support multiple picture formats such as jpg, git, etc.

[0032] Step S220, converting the voice file and / or the picture file into text to obtain a converted edited text.

[0033] In some possible embodiments of the present application, the file to be recognized is first recognized. In the case of a voice file, the voice file is converted into a text format using Automatic Speech Recognition (ASR) technology to obtain a converted edited text. In the case of a picture file, the optical character recognition (OCR) technology is used, combined with a convolutional neural network and a recurrent neural network to detect the text area in the picture, and convert it into editable text to obtain a converted edited text, which is conducive to the subsequent generation of a basic article based on the converted edited text.

[0034] It should be noted that the file to be recognized may also contain voice files and picture files at the same time. For example, the file to be recognized is a file with audio inserted in a picture. In this case, it can be processed by combining automatic speech recognition technology, optical character recognition technology, convolutional neural network and recurrent neural network to obtain converted edited text, which is conducive to the subsequent generation of basic articles based on the converted edited text.

[0035] Step S230, cleaning the converted edited text to generate a basic article.

[0036] In some possible embodiments of the present application, the converted edited text obtained according to step S220 may contain various noises, for example, unnecessary symbols or incorrect line breaks. Data cleaning can be performed by natural language processing technology. Specifically, a rule-based method or sequence model can be used to correct errors and format the text, and a basic article is generated based on the cleaned text so that the basic article can be subsequently imported into the article editor. After the above optimization by natural language processing technology, the coherence and logical structure of the text can be ensured, and the accuracy of the generated text can be improved.

[0037] In one embodiment, the article mode includes a real-time news mode. Figure 3As shown, in response to the target object's selection operation of the article mode, a basic article is generated according to the selected article mode, including but not limited to the following steps: Step S240, in response to the target object's selection operation of the real-time news mode, receiving crawled news articles according to the selected real-time news mode.

[0038] In some possible embodiments of the present application, when the article mode is the real-time news mode, the user clicks to select the real-time news mode, and in response to the target object's selection operation of the real-time news mode, the corresponding news articles are crawled from the web page using the preset crawler technology according to the selected real-time news mode, and the crawled news articles are sent to the system, and the crawled news articles are received so as to generate basic articles according to the crawled news articles later. Among them, there are multiple crawled news articles, including multiple news types, exemplarily including hot news and influential news.

[0039] Step S250, generating news popularity for the news articles according to preset indicators, and taking the news articles corresponding to the news popularity ranked first as basic articles.

[0040] In some possible embodiments of the present application, the preset indicators include likes, comments, collections, forwarding, and the time difference between the release time of the article and the current time. According to the news articles crawled in step S240, the news articles are calculated using various indicators according to the set coefficients to obtain the news popularity. The news popularity is sorted, and it can be sorted in descending order, and the news articles corresponding to the news popularity arranged in the front are selected; it can also be sorted in ascending order, and the news articles corresponding to the news popularity arranged in the back are selected. Click to view the news article, extract the full text, and obtain the basic article, so that the basic article can be edited and adjusted in the article editor later to improve the accuracy of the text.

[0041] In other possible embodiments of the present application, the article mode also includes an AI text creation mode. The user clicks to select the AI ​​text creation mode, and in response to the target object's selection operation of the AI ​​text creation mode, the user enters the text creation page according to the selected AI text creation mode. The user can write the created text by himself and use the created text as the basic article. This not only provides an interface for the user's own creation, but also facilitates the subsequent editing and adjustment of the basic article in the article editor to improve the accuracy of the text.

[0042] It should be noted that the article modes include intelligent recognition mode, real-time news mode, and AI text creation mode. By selecting different modes, different creation formats can be achieved, which improves the flexibility of text creation.

[0043] Step S300, on the page of the text editor, in response to the target object selecting an edit button, displaying the edit page corresponding to the edit button, displaying the edit result corresponding to the edit button on the edit page, and using the edit result to replace the basic article to generate an initial coal melting text.

[0044] In one embodiment, the text editor includes multiple functional editing buttons, which may include: dictionary, short sentence recommendation, title generation, intelligent rewriting, writing assistance, etc. On the page of the text editor, the user can select and click the editing button. In response to the target object selecting the editing button, the editing page corresponding to the editing button is displayed, and the editing result corresponding to the editing button is displayed on the editing page. After the editing button is triggered, the basic article can be automatically edited, thereby improving the writing efficiency. The editing result is used to replace the basic article to generate the initial coal-melting text, which is conducive to the subsequent optimization of the initial coal-melting text and improves the accuracy of text generation.

[0045] In one embodiment, the edit button includes an auxiliary writing button. On the page of the text editor, in response to the target object selecting the edit button, an edit page corresponding to the edit button is displayed, the edit result corresponding to the edit button is displayed on the edit page, and the basic article is replaced with the edit result to generate the initial coal-melting text, including but not limited to the following steps: Step S310: On the page of the text editor, in response to the target object selecting an auxiliary writing button, an auxiliary writing page corresponding to the auxiliary writing button is displayed, and auxiliary writing is performed on the basic article to obtain an auxiliary writing result.

[0046] In one embodiment, on a page of a text editor, a user can select multiple functional editing buttons displayed on the page, and the user clicks on an auxiliary writing button to select the auxiliary writing button. In response to the target object's selection of the auxiliary writing button, an auxiliary writing page corresponding to the auxiliary writing button is displayed, and the background performs automated assisted writing on the basic article to obtain assisted writing results, so as to improve writing efficiency and the accuracy of subsequently generated coal-melting texts.

[0047] like Figure 4 As shown, assisting writing of the basic article to obtain the assisting writing result includes but is not limited to the following steps: Step S311, performing text preprocessing on the basic article to obtain a preprocessed text, wherein the preprocessing process includes word segmentation, part-of-speech tagging, and removal of stop words.

[0048] In some possible embodiments of the present application, the preprocessing process includes word segmentation, part-of-speech tagging, and stop word removal, and the data processing part of the natural language processing technology is used to perform word segmentation, part-of-speech tagging, and stop word removal on the basic article to obtain the preprocessed text. Through the above processing, the input text content can be converted into a computer-recognizable data structure for subsequent data processing to achieve automatic text generation.

[0049] Step S312, extracting features from the preprocessed text to obtain extracted features.

[0050] In some possible embodiments of the present application, feature extraction is performed on the preprocessed text through the feature extraction part in the natural language processing technology. The feature extraction part mainly performs semantic understanding. Specifically, the words and sentences in the text can be converted into vector representations in a high-dimensional space through technologies such as word embedding and sentence vectors, thereby capturing their semantic information. The extracted features are obtained by performing feature extraction, so as to analyze the sentence structure and emotional color, thereby improving the accuracy of text generation.

[0051] Step S313, generating a text title according to the extracted features, and in response to the target object's confirmation operation on the text title, adding the text title to the basic article to obtain an auxiliary writing result.

[0052] In some possible embodiments of the present application, according to the extracted features, the extracted features are input into the pre-trained model to generate a text title, which conforms to grammatical rules, is clearly expressed and attractive. The text title is displayed on the editing page, and the user can view the generated text title. If the title conditions of the text are met, the user clicks to confirm the adoption of the title. In response to the user's determination of the text title, the text title is added to the first line of the basic article and centered to generate an auxiliary writing result. Among them, the pre-trained model can be a machine learning model, such as a decision tree, naive Bayes, support vector machine, etc.; it can also be a recurrent neural network and its variants. In the process of title generation, the pre-trained model will also comprehensively consider keywords and sentence structure to better capture long-term dependencies and contextual information in the text, thereby generating a more accurate and natural title, and can also adjust the word order of the text to improve the writing accuracy.

[0053] Step S320, displaying the auxiliary writing result on the auxiliary writing page, and using the auxiliary writing result to replace the basic article to generate an initial coal melting text.

[0054] In one embodiment, according to the auxiliary writing result obtained in step S310, the auxiliary writing result is displayed on the auxiliary writing page, and the user can click the Replace or Confirm button to replace the auxiliary writing result with the basic article, thereby generating an initial coal-melting text, so that the initial coal-melting text can be processed later to improve the accuracy of the generated text.

[0055] In one embodiment, the edit button includes a short sentence recommendation button. On the page of the text editor, in response to the target object selecting the edit button, an edit page corresponding to the edit button is displayed, the edit result corresponding to the edit button is displayed on the edit page, and the basic article is replaced with the edit result to generate the initial coal-melting text, including but not limited to the following steps: Step S330, on the page of the text editor, in response to the target object selecting the short sentence recommendation button, a recommendation page corresponding to the short sentence recommendation button is displayed, short sentences are recommended for the basic article, and recommended short sentences are obtained.

[0056] In some possible embodiments of the present application, on a page of a text editor, a user can select a plurality of functional editing buttons displayed on the page, and the user clicks and selects a short sentence recommendation button. In response to the target object selecting the short sentence recommendation button, a recommendation page corresponding to the short sentence recommendation button is displayed, and the background recommends short sentences for the basic article through natural language processing technology to obtain recommended short sentences. Natural language processing technology can learn the context of the text, and the recommended short sentences are more reasonable and accurate.

[0057] Step S340, the recommended short sentences are displayed in the recommendation page in the form of a list, and in response to the target object's selection operation of the recommended short sentence in the list, the recommended short sentence is inserted into the basic article to obtain the initial coal melting text.

[0058] In some possible embodiments of the present application, the recommended short sentences may have multiple records, and each recommended short sentence is displayed in the form of a list on the recommendation page. Only one recommended short sentence can also be displayed in the form of a list. The user can select the recommended short sentence by viewing the displayed recommendation list. In response to the user's selection operation on the recommended short sentence in the list, the recommended short sentence is inserted into the basic article. The article can also continue to be edited in the article editor to obtain the initial coal melting text, so that the initial coal melting text can be processed subsequently to improve the accuracy of the generated text.

[0059] like Figure 5As shown, the text editor also includes translation tools, intelligent rewriting, dictionaries, Tang poetry and Song lyrics, etc. Exemplarily, for a basic article, the user can click the translation tool button, and in response to the user's click operation on the translation tool button, the basic article is translated using a pre-set translation software interface to obtain an initial coal-melting text. Among them, the pre-set translation software can be a translation software formed by a Transformer architecture, and a corresponding interface is set. The user can also click the intelligent rewrite button, and in response to the user's click operation on the intelligent rewrite button, the text is deeply parsed using a rewriting algorithm to understand its semantics and logical structure, and the vocabulary, grammar, syntax and semantic relationships in the text are analyzed, and operations such as adjusting the structure of the article, replacing vocabulary, and changing sentence patterns are performed to generate a rewriting result, and the rewriting result replaces the basic article to obtain an initial coal-melting text. Among them, the rewriting algorithm is a pre-trained Transformer architecture model. The user can also click on the dictionary button, and the user selects a character or word in the basic article for input. In response to the user's click search operation, a preset search algorithm is used to find matching items in a preset dictionary or dictionary database, and the search matching character or word meaning is displayed; wherein the search algorithm can be an inverted index or hash map for fast retrieval. The collected relevant results can also be processed for information retrieval, and the search results are ranked using a machine learning algorithm so that the most relevant character or word meaning is displayed first. The ranking algorithm considers factors such as the context of the query, the user's historical query records, and universality. If a polysemous word is entered, context analysis technology will be used to determine the most appropriate meaning. Context analysis technology uses word embedding models, such as BERT or its variants, to understand the specific meaning of words in different contexts, so as to recommend more appropriate character or word meanings. The user can also click on the Tang Poetry and Song Lyrics button. In response to the user's click operation on Tang Poetry and Song Lyrics, natural language technology is used to extract keywords in the basic article, and Tang Poetry and Song Lyrics formats are generated to obtain coal-melting text.

[0060] Step S400, in response to the target object clicking the text optimization button, optimizing the initial coal melting text to obtain the target coal melting text.

[0061] In one embodiment, if the generated initial coal-melting text is not satisfactory, it can be optimized. The system is provided with a text optimization button. The user clicks the optimization button. In response to the user's click operation on the text optimization button, the initial coal-melting text is optimized to obtain the target coal-melting text. By optimizing the text, the accuracy of the generated text can be further improved. Among them, the optimization includes typo detection, sensitive word detection, automatic typesetting and originality detection. Exemplarily, the generated initial coal-melting text is displayed in the article editor. The user can click on typo detection and click to start review. The typo can be identified and the correction results can be displayed for the user to view. The user can click on sensitive word detection. Through the preset sensitive word library about news, each segmentation is compared through the word segmentation technology, and sensitive word detection is performed. The user can view the detection results and click to adopt or ignore. The user can also click on originality detection. The system uses the crawler content and the text comparison algorithm to compare the generated text with the text crawled on the Internet to obtain the originality percentage. The higher the originality percentage, the lower the repetition of the generated target coal-melting text, and it can be published. By optimizing, the accuracy of generating coal melting text can be further improved.

[0062] Step S500, publishing the target coal melting text.

[0063] In one embodiment, according to the generated target coal-refining text, the user can use Rong Xiaopang AIGC, select the AI ​​assistant, and click publish to generate the published content. Rong Xiaopang AIGC integrates the interface required for publishing content and can automatically publish it with one click. The publishing channels can be Weibo, WeChat, Bilibili, Toutiao, Xiaohongshu, Douyin, etc.

[0064] In one embodiment, after entering the workbench page in response to the target object's successful login operation, the deep learning-based coal-melting text generation method also includes but is not limited to: on the workbench page, in response to the target object's selection operation of a function button, displaying a function editing page; on the function editing page, in response to the target object's input operation, performing function editing on the input content to obtain an editing result; and importing the editing result into the text editing library for editing again.

[0065] In some possible embodiments of the present application, since there are multiple function modules on the workbench page, on the workbench page, in response to the target object's selection operation of the function button, a function editing page is displayed. The function buttons include translation tools, short sentence recommendations, dictionaries, title generation, typo detection, originality detection, etc. That is, the above functions can be implemented in a text editor, and an interface is also provided on the workbench page, so that the corresponding editing page can be directly entered from the workbench page. It should be noted that the difference between the function editing page and the text editor is that the text editor has an automatically generated basic article, while the user needs to enter the corresponding content as the basic article in the function editing page. By setting a variety of function modules, different text generation modes are provided, which improves the flexibility of text generation.

[0066] Specifically, on the function editing page, in response to the input operation of the target object, the input content is functionally edited to obtain the editing result. Exemplarily, the user enters a professional hot word and clicks search. The background crawler technology is used to crawl the data of the professional hot word, sort it according to the popularity, generate the search results, and display it. In the case where the input content is a web link, the text in the link can also be automatically processed, including removing web page tags, punctuation marks and stop words, and then jumping to the corresponding web link page, extracting and identifying the content in the web link page, generating corresponding text, and displaying it. Among them, the process of extracting and identifying the content in the web link page is similar to step S220, which will not be repeated here.

[0067] Specifically, the user can also click on keyword mining, enter keywords, click search, and display a list of related keywords automatically mined using natural language processing technology. The natural language processing technology can be a word analysis model TF-IDF or BERT model. The BERT model is used to identify and extract vocabulary related to the input keywords from the search engine search information. Then, an association rule learning algorithm, such as Apriori or FP-Growth algorithm, is used to analyze the relationship between keywords. Frequently co-occurring keyword combinations can be mined to provide users with a wider range of related keyword choices. Random forests or support vector machines are then used to analyze and predict the relevance and effectiveness of keywords, making the connection between keywords closer. The above can integrate multi-channel data and use cross-domain knowledge to enhance the accuracy and breadth of keyword mining.

[0068] In some other possible embodiments of the present application, the editing results can also be imported into a text editing library for further editing to improve the accuracy of the generated text.

[0069] like Figure 6As shown, the embodiment of the present application provides a coal-melting text generation device 100 based on deep learning. The coal-melting text generation device 100 based on deep learning first enters the workbench page in response to the successful login operation of the target object through the response module 110; then, through the text editing module 120, on the workbench page, in response to the target object's selection operation of the article mode, a basic article is generated according to the selected article mode, and in response to the editing operation of the basic article, a text editor is entered. The text editor is used to edit the basic article. By selecting the article mode, article generation in different modes can be achieved, which has strong flexibility; then, the text generation module 130 is used on the page of the text editor to respond to the target object's editing operation. The button selection operation is performed to display the editing page corresponding to the edit button, and the editing result corresponding to the edit button is displayed on the editing page. The basic article is replaced by the editing result to generate the initial coal-melting text. By editing the basic article in the text editor, the flexibility of writing can be further improved. After clicking the edit button on the editing page, the editing result corresponding to the edit button is automatically displayed, and the text is automatically generated, thereby improving the writing efficiency. The text optimization module 140 responds to the target object's click operation on the text optimization button to optimize the initial coal-melting text and obtain the target coal-melting text. By optimizing the text, the accuracy of the generated text can be further improved. Finally, the text publishing module 150 is used to publish the target coal-melting text for easy viewing.

[0070] It should be noted that the response module 110 is connected to the text editing module 120, the text editing module 120 is connected to the text generation module 130, the text generation module 130 is connected to the text optimization module 140, and the text optimization module 140 is connected to the text publishing module 150. The above-mentioned coal melting text generation method based on deep learning is applied to the coal melting text generation device 100 based on deep learning. The coal melting text generation device 100 based on deep learning first enters the workbench page in response to the successful login operation of the target object; on the workbench page, in response to the target object's selection operation of the article mode, a basic article is generated according to the selected article mode, and in response to the editing operation of the basic article, a text editor is entered. The text editor is used to edit the basic article. By selecting the article mode, article generation in different modes can be achieved, which has strong flexibility; on the page of the text editor, in response to the target object's selection operation of the edit button The editing page corresponding to the edit button is displayed, the editing result corresponding to the edit button is displayed on the editing page, and the basic article is replaced by the editing result to generate an initial coal-melting text. By editing the basic article in the text editor, the flexibility of writing can be further improved. After clicking the edit button on the editing page, the editing result corresponding to the edit button is automatically displayed, and the text is automatically generated, thereby improving the writing efficiency. In response to the target object clicking the text optimization button, the initial coal-melting text is optimized to obtain the target coal-melting text. By optimizing the text, the accuracy of the generated text can be further improved. The generated target coal-melting text is published for easy viewing.

[0071] It should also be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0072] The present application also discloses an electronic device. Figure 7 , Figure 7 The electronic device 500 may include: at least one processor 501 , at least one network interface 504 , a user interface 503 , a memory 505 , and at least one communication bus 502 .

[0073] The communication bus 502 is used to realize the connection and communication between these components.

[0074] The user interface 503 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0075] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0076] Among them, the processor 501 may include one or more processing cores. The processor 501 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 505, and calling data stored in the memory 505. Optionally, the processor 501 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 501 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 501, and it can be implemented separately through a chip.

[0077] Among them, the memory 505 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 505 may also be optionally at least one storage device located away from the aforementioned processor 501. Refer to Figure 7, as a computer storage medium, the memory 505 may include an operating system, a network communication module, a user interface module, and an application program for a method for generating coal melting text based on deep learning.

[0078] exist Figure 7 In the electronic device 500 shown, the user interface 503 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 501 can be used to call the memory 505 to store an application program for a method for generating coal melting text based on deep learning. When executed by one or more processors 501, the electronic device 500 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0079] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0081] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0083] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0084] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0085] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A coal melting text generation method based on deep learning, characterized in that: The method comprises: In response to a successful login operation of the target object, entering the workbench page; On the workbench page, in response to the target object's selection operation of an article mode, a basic article is generated according to the selected article mode, and in response to the editing operation of the basic article, a text editor is entered, and the text editor is used to edit the basic article; On the page of the text editor, in response to the target object selecting an edit button, an edit page corresponding to the edit button is displayed, an edit result corresponding to the edit button is displayed on the edit page, and the basic article is replaced with the edit result to generate an initial coal melting text; In response to the target object clicking on a text optimization button, optimizing the initial coal melting text to obtain a target coal melting text; The target coal melting text is published.

2. The method according to claim 1, characterized in that The article mode includes an intelligent recognition mode; In response to the target object's selection of an article mode, generating a basic article according to the selected article mode includes: In response to the target object's selection operation of the intelligent recognition mode, receiving uploaded files to be recognized according to the selected intelligent recognition mode, wherein the files to be recognized include voice files and picture files; Performing text conversion on the voice file and / or the picture file to obtain a converted edited text; The converted edited text is cleaned to generate the basic article.

3. The method according to claim 1, characterized in that The article mode includes a real-time news mode; In response to the target object's selection of an article mode, generating a basic article according to the selected article mode includes: In response to the target object's selection operation of the real-time news mode, receiving crawled news articles according to the selected real-time news mode; The news articles are assigned news popularity according to preset indicators, and the news articles corresponding to the news popularity ranked first are used as the basic articles.

4. The method according to claim 1, characterized in that: The editing button includes an auxiliary writing button; On the page of the text editor, in response to the target object selecting an edit button, an edit page corresponding to the edit button is displayed, an edit result corresponding to the edit button is displayed on the edit page, and the basic article is replaced with the edit result to generate an initial coal melting text, including: On the page of the text editor, in response to the target object selecting an auxiliary writing button, an auxiliary writing page corresponding to the auxiliary writing button is displayed, and auxiliary writing is performed on the basic article to obtain an auxiliary writing result; The auxiliary writing result is displayed on the auxiliary writing page, and the basic article is replaced by the auxiliary writing result to generate an initial coal melting text.

5. The method according to claim 4, characterized in that The assisting writing of the basic article to obtain the assisting writing result includes: Performing text preprocessing on the basic article to obtain a preprocessed text, wherein the preprocessing process includes word segmentation, part-of-speech tagging, and removal of stop words; Performing feature extraction on the preprocessed text to obtain extracted features; A text title is generated according to the extracted features, and in response to a determination operation of the target object on the text title, the text title is added to the basic article to obtain the auxiliary writing result.

6. The method according to claim 1, characterized in that The editing button includes a short sentence recommendation button; On the page of the text editor, in response to the target object selecting an edit button, an edit page corresponding to the edit button is displayed, an edit result corresponding to the edit button is displayed on the edit page, and the basic article is replaced with the edit result to generate an initial coal melting text, including: On the page of the text editor, in response to the target object selecting a short sentence recommendation button, a recommendation page corresponding to the short sentence recommendation button is displayed, and short sentences are recommended for the basic article to obtain recommended short sentences; The recommended short sentences are displayed in the recommendation page in the form of a list, and in response to the target object selecting the recommended short sentence in the list, the recommended short sentence is inserted into the basic article to obtain the initial coal melting text.

7. The method according to claim 1, characterized in that After entering the workbench page in response to the successful login operation of the target object, the method further includes: On the workbench page, in response to the target object's selection operation of a function button, displaying the function editing page; On the function editing page, in response to the input operation of the target object, functionally edit the input content to obtain an editing result; The editing result is imported into the text editing library for editing again.

8. A coal melting text generation device based on deep learning, characterized in that: The device comprises: A response module (110), configured to respond to a successful login operation of the target object and enter a workbench page; A text editing module (120) is used for generating a basic article according to the selected article mode in response to the target object's selection operation on the article mode on the workbench page, and entering a text editor in response to the editing operation on the basic article, wherein the text editor is used to edit the basic article; A text generation module (130) is used to display an editing page corresponding to the editing button on the page of the text editor in response to the target object selecting an editing button, display an editing result corresponding to the editing button on the editing page, and replace the basic article with the editing result to generate an initial coal melting text; A text optimization module (140) is used to optimize the initial coal melting text in response to the target object clicking the text optimization button to obtain a target coal melting text; The text publishing module (150) is used to publish the target coal melting text.

9. An electronic device, characterized in that: The electronic device (500) comprises a processor (501), a memory (505), a user interface (503), a communication bus (502) and a network interface (504), wherein the processor (501), the memory (505), the user interface (503) and the network interface (504) are respectively connected to the communication bus (502), the memory (505) is used to store instructions, the user interface (503) and the network interface (504) are used to communicate with other devices, and the processor (501) is used to execute the instructions stored in the memory (505) so that the electronic device (500) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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