Flow chart generation method and device, electronic equipment and computer readable storage medium

Abstract

The invention relates to the technical field of chart generation, and provides a flow chart generation method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining a flow chart natural description language text and intermediate language type information; performing first coding processing on the natural description language text of the flow chart to obtain natural description language coding information; carrying out second coding processing on the intermediate language type information to obtain intermediate language type coding information; performing joint processing on the natural description language coding information and the intermediate language type coding information to obtain joint language coding information; decoding the joint language coding information to obtain intermediate description language information of the flow chart; and performing picture drawing processing on the intermediate description language information of the flow chart to obtain a target flow chart. By means of the technical scheme, the flow chart generation process can be simpler, more convenient and faster, and then rapid development of financial services can be well promoted.

Description

Technical Field

[0001] The embodiments of the present application relate to but are not limited to the field of chart generation technology, and in particular, to a flowchart generation method, device, electronic device, and computer-readable storage medium. Background Art

[0002] In the financial industry, flowcharts are often used to represent the order of business processes and activities. Through the above settings, it can help people better understand the business process and improve the efficiency of communication and collaboration. In current work, drawing software is generally used to draw flowcharts; drawing flowcharts not only requires certain drawing skills, but also is very time-consuming, which makes the process of generating flowcharts inconvenient, thus affecting the rapid development of financial business. 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 flowchart generation method, device, electronic device and computer-readable storage medium, which can make the flowchart generation process simpler and faster, and thus can well promote the rapid development of financial business.

[0005] In a first aspect, an embodiment of the present application provides a flowchart generation method, which is applied to a flowchart generation model, wherein the flowchart generation model includes a conversion model and an image interpreter, wherein the conversion model includes a first encoder, a second encoder, and a decoder, and the flowchart generation method includes:

[0006] Obtain the flowchart natural description language text and intermediate language type information;

[0007] Performing a first encoding process on the natural description language text of the flowchart based on the first encoder to obtain natural description language encoding information; and performing a second encoding process on the intermediate language type information based on the second encoder to obtain intermediate language type encoding information;

[0008] Combine the natural description language encoding information and the intermediate language type encoding information to obtain combined language encoding information;

[0009] Decoding the combined language coding information based on the decoder to obtain flow chart intermediate description language information;

[0010] Based on the image interpreter, the intermediate description language information of the flowchart is subjected to image drawing processing to obtain a target flowchart.

[0011] According to some embodiments of the present application, performing a first encoding process on the natural description language text of the flowchart based on the first encoder to obtain natural description language encoding information includes:

[0012] Performing a first preprocessing on the natural description language text of the flowchart to obtain a preprocessed description language text;

[0013] Performing a first vectorization process on the preprocessed description language text to obtain a vectorized language text;

[0014] Formatting the vectorized language text to obtain language tensor information;

[0015] Context feature extraction is performed on the language tensor information to obtain the natural description language encoding information.

[0016] According to some embodiments of the present application, performing a second encoding process on the intermediate language type information based on the second encoder to obtain the intermediate language type encoding information includes:

[0017] Performing a second preprocessing on the intermediate language type information to obtain preprocessed language type information;

[0018] Performing vocabulary mapping processing on the preprocessed language type information to obtain a language type vocabulary text;

[0019] Performing a second vectorization process on the language type vocabulary text to obtain a language type vocabulary vector;

[0020] Serializing the language type vocabulary vector to obtain a serialized language type vector;

[0021] Context information is extracted from the sequence speech language type vector to obtain the intermediate language type encoding information.

[0022] According to some embodiments of the present application, the step of jointly processing the natural description language encoding information and the intermediate language type encoding information to obtain the joint language encoding information includes:

[0023] Performing format conversion processing on the natural description language encoding information and the intermediate language type encoding information to obtain conversion language encoding information corresponding to the natural description language encoding information and conversion language type encoding information corresponding to the intermediate language type encoding information;

[0024] The transformation language coding information and the transformation language type coding information are fused to obtain the combined language coding information.

[0025] According to some embodiments of the present application, the decoding process of the joint language encoding information based on the decoder to obtain the flow chart intermediate description language information includes:

[0026] generating a language encoding output sequence based on the combined language encoding information;

[0027] Performing element segmentation processing on the language coding output sequence to obtain a plurality of language coding elements;

[0028] The multiple language coding elements are decoded in sequence to obtain the intermediate description language information of the flow chart.

[0029] According to some embodiments of the present application, the image interpreter is used to perform image drawing processing on the intermediate description language information of the flowchart to obtain a target flowchart, including:

[0030] Analyze and process the intermediate description language information of the flowchart to obtain process node information, process turn information and node association attribute information;

[0031] Generate an initial flow chart according to the process node information, the process turn information and the node associated attribute information by using a preset layout algorithm;

[0032] The initial flowchart is rendered to obtain the target flowchart.

[0033] According to some embodiments of the present application, after the image interpreter performs image drawing processing on the intermediate description language information of the flowchart to obtain the target flowchart, the method further includes:

[0034] Get flowchart modification information;

[0035] The target flowchart is adjusted based on the flowchart modification information and the image interpreter.

[0036] In a second aspect, an embodiment of the present application further provides a flowchart generation device, including a flowchart generation model, wherein the flowchart generation model includes a conversion model and an image interpreter, wherein the conversion model includes a first encoder, a second encoder and a decoder, and the device further includes:

[0037] The first processing module is used to obtain the natural description language text and intermediate language type information of the flowchart;

[0038] A second processing module is configured to perform a first encoding process on the natural description language text of the flowchart based on the first encoder to obtain natural description language encoding information; and perform a second encoding process on the intermediate language type information based on the second encoder to obtain intermediate language type encoding information;

[0039] A third processing module, configured to jointly process the natural description language encoding information and the intermediate language type encoding information to obtain joint language encoding information;

[0040] A fourth processing module, configured to decode the joint language coding information based on the decoder to obtain flow chart intermediate description language information;

[0041] The fifth processing module is used to perform image drawing processing on the intermediate description language information of the flowchart based on the image interpreter to obtain a target flowchart.

[0042] 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 flowchart generation method described in the first aspect above is implemented.

[0043] 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 flowchart generation method described in the first aspect above.

[0044] According to the flowchart generation method of the embodiment provided by the present application, there are at least the following beneficial effects: in the process of generating a flowchart, it is realized by a flowchart generation model, wherein the flowchart generation model includes a conversion model and an image interpreter, and the conversion model includes a first encoder, a second encoder and a decoder; firstly, a natural description language text and an intermediate language type information of the flowchart are obtained; then, based on the first encoder, a first encoding process is performed on the natural description language text of the flowchart to obtain the natural description language encoding information; and, based on the second encoder, a second encoding process is performed on the intermediate language type information to obtain the intermediate language type encoding information; then, the natural description language encoding information and the intermediate language type encoding information are jointly processed to obtain the joint language encoding information; then, based on the decoder, the joint language encoding information is decoded to obtain the intermediate description language information of the flowchart; finally, based on the image interpreter, the intermediate description language information of the flowchart is subjected to image drawing processing to obtain the target flowchart; through the above technical scheme, a flowchart can be generated intelligently, which can make the flowchart generation process more simple and quick, and thus can well promote the rapid development of financial business. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide 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.

[0046] Figure 1 It is a flowchart of a flowchart generation method provided by an embodiment of the present application;

[0047] Figure 2 is a specific flow chart of step S200 provided in one embodiment of the present application;

[0048] Figure 3 is a specific flow chart of step S200 provided in another embodiment of the present application;

[0049] Figure 4 is a specific flow chart of step S300 provided in one embodiment of the present application;

[0050] Figure 5 is a specific flow chart of step S400 provided in one embodiment of the present application;

[0051] Figure 6 is a specific flow chart of step S500 provided in one embodiment of the present application;

[0052] Figure 7 is a specific flow chart after step S500 provided in one embodiment of the present application;

[0053] Figure 8 is a schematic diagram of a flowchart generating device provided by an embodiment of the present application;

[0054] Fig. 9 It is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] It should be noted that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification and claims and the above drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0057] It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as those 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.

[0058] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, 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 obtain the best results.

[0059] AI is a new technical science 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. Artificial intelligence attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robots, language recognition, image recognition, natural language processing and expert systems. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also a 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.

[0060] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0061] 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.

[0062] 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 (CDN), as well as big data and artificial intelligence platforms.

[0063] The present application provides a flowchart generation method, device, electronic device and computer-readable storage medium. In the process of generating a flowchart, it is implemented through a flowchart generation model, wherein the flowchart generation model includes a conversion model and an image interpreter, and the conversion model includes a first encoder, a second encoder and a decoder; first, a flowchart natural description language text and intermediate language type information are obtained; then, based on the first encoder, a first encoding process is performed on the flowchart natural description language text to obtain natural description language encoding information; and, based on the second encoder, a second encoding process is performed on the intermediate language type information to obtain the intermediate language type encoding information; then, the natural description language encoding information and the intermediate language type encoding information are jointly processed to obtain the joint language encoding information; then, based on the decoder, the joint language encoding information is decoded to obtain the flowchart intermediate description language information; finally, based on the image interpreter, the flowchart intermediate description language information is image-drawn to obtain the target flowchart; through the above technical solution, a flowchart can be generated intelligently, which can make the flowchart generation process more simple and quick, and thus can well promote the rapid development of financial business.

[0064] The flowchart generation method provided in the embodiment of the present application relates to the technical field of chart generation. The flowchart generation method 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 a server cluster or a distributed system composed of multiple physical servers, or 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 flowchart generation method, etc., but is not limited to the above forms.

[0065] 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, etc. 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, etc. 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 through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0066] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0067] The embodiments of the present application are further described below in conjunction with the accompanying drawings.

[0068] like Figure 1 As shown, Figure 1 It is a flowchart of a flowchart generation method provided by an embodiment of the present application, which is applied to a flowchart generation model. The flowchart generation model includes a conversion model and an image interpreter. The conversion model includes a first encoder, a second encoder and a decoder. The flowchart generation method includes but is not limited to steps S100 to S500.

[0069] Step S100, obtaining the natural description language text and intermediate language type information of the flowchart;

[0070] Step S200, performing a first encoding process on the natural description language text of the flowchart based on a first encoder to obtain natural description language encoding information; and performing a second encoding process on the intermediate language type information based on a second encoder to obtain intermediate language type encoding information;

[0071] Step S300, jointly processing the natural description language encoding information and the intermediate language type encoding information to obtain joint language encoding information;

[0072] Step S400, decoding the joint language coding information based on the decoder to obtain the flow chart intermediate description language information;

[0073] Step S500: performing image drawing processing on the intermediate description language information of the flowchart based on the image interpreter to obtain the target flowchart.

[0074] It should be noted that, in the process of generating a flowchart, it is realized through a flowchart generation model, wherein the flowchart generation model includes a conversion model and an image interpreter, and the conversion model includes a first encoder, a second encoder and a decoder; first, the natural description language text and the intermediate language type information of the flowchart are obtained; then, based on the first encoder, the natural description language text of the flowchart is subjected to a first encoding process to obtain the natural description language encoding information; and, based on the second encoder, the intermediate language type information is subjected to a second encoding process to obtain the intermediate language type encoding information; then, the natural description language encoding information and the intermediate language type encoding information are jointly processed to obtain the joint language encoding information; then, based on the decoder, the joint language encoding information is decoded to obtain the intermediate description language information of the flowchart; finally, based on the image interpreter, the intermediate description language information of the flowchart is subjected to image drawing process to obtain the target flowchart; through the above technical solution, the flowchart can be generated intelligently, which can make the flowchart generation process more simple and quick, and thus can well promote the rapid development of financial business.

[0075] It is worth noting that the embodiments of the present application will obtain the user's permission or consent before obtaining the flowchart natural description language text and intermediate language type information, and the collection, use and processing of these data will comply with relevant laws, regulations and standards. In addition, when the embodiments of the present application need to obtain the user's sensitive personal information, it will obtain the user's separate permission or consent through a pop-up window or jump to a confirmation page, and after clearly obtaining the user's separate permission or consent, it will obtain the necessary user-related data for the normal operation of the embodiments of the present application.

[0076] It is worth noting that in the financial industry, in the process of promoting various financial products to users, it is often necessary to draw flowcharts to explain the system to users; in the existing technology, financial salesmen are often required to draw flowcharts through drawing software, which requires financial salesmen to master some drawing skills, and the process of drawing flowcharts is relatively time-consuming, which makes the flowchart drawing process more complicated, thereby affecting the rapid development of financial business; through the flowchart generation method of the embodiment of the present application, it is possible to realize automatic and intelligent generation of flowcharts, making the flowchart generation process more simple and quick, which is conducive to financial salesmen to better carry out financial business.

[0077] It is worth noting that the embodiment of the present application generates a flowchart directly based on the flowchart natural description language text, and does not require the user to write configuration files, write codes and annotations, and draw flowcharts based on software, which greatly reduces the interaction requirements between users and drawing software. The embodiment of the present application can output different types of intermediate languages ​​to adapt to different image interpreters, and can generate flowcharts of different styles.

[0078] It should be noted that the flowchart generation method of the embodiment of the present application is applied to the flowchart generation model, and the flowchart generation model includes a conversion model and an image interpreter, and the conversion model includes a first encoder, a second encoder and a decoder; in the process of flowchart generation, the flowchart natural description language text and intermediate language type information are first obtained; then the first encoder based on the conversion model performs a first encoding process on the flowchart natural description language text to obtain the natural description language encoding information; and the second encoder based on the conversion model performs a second encoding process on the intermediate language type information to obtain the intermediate language type encoding information; then the natural description language encoding information and the intermediate language type encoding information can be jointly processed to obtain the joint language encoding information; then the decoder based on the conversion model can decode the joint language encoding information to obtain the flowchart intermediate description language information; then the image interpreter can draw the flowchart intermediate description language information to obtain the target flowchart; through the above technical solution, based on the conversion model and the image interpreter of the flowchart generation model, intelligent flowchart generation processing is realized, so that the flowchart generation process can be simpler and faster, which is conducive to the development of financial business.

[0079] It is worth noting that the conversion model in the embodiment of the present application is mainly responsible for converting the natural description language text of the flowchart into the intermediate description language information understandable by the image interpreter, and different intermediate description language information can be output according to different image interpreters. Intermediate language is a language understandable by the image interpreter. The image interpreter is an interpreter that can draw pictures based on language descriptions. For the above-mentioned conversion module and image interpreter, users can select suitable modules and then combine them to generate flowcharts based on natural language. The embodiment of the present application mainly constructs a conversion model and an image interpreter. Among them, the conversion model is a generative model, and the model input includes two parts, one part is natural language, which is used to describe the components of the flowchart, the relationship between the parts and the attribute requirements of the flowchart, etc., and the other part is the intermediate language type converted to, and the model is based on the input part converted into the intermediate language description of the corresponding intermediate language type.

[0080] In some embodiments, Figure 2 As shown, the above step S200 may include but is not limited to steps S210 to S240.

[0081] Step S210, performing a first preprocessing on the flowchart natural description language text to obtain a preprocessed description language text;

[0082] Step S220, performing a first vectorization process on the preprocessed description language text to obtain a vectorized language text;

[0083] Step S230, formatting the vectorized language text to obtain language tensor information;

[0084] Step S240, performing context feature extraction on the language tensor information to obtain natural description language encoding information.

[0085] It should be noted that in the process of performing a first encoding process on the natural description language text of the flowchart based on the first encoder to obtain the natural description language encoding information, the natural description language text of the flowchart is first preprocessed to obtain the preprocessed description language text; then the preprocessed description language text is first vectorized to obtain the vectorized language text; then the vectorized language text is formatted to obtain the language tensor information; then the language tensor information is subjected to context feature extraction to obtain the natural description language encoding information. Through the above technical solution, the generation of the natural description language encoding information can be simpler and faster, and is ready for the subsequent flowchart generation.

[0086] In some specific embodiments of the present application, the natural description language text of the flowchart can be expressed orally by the salesperson, and then the text is obtained by the intelligent voice recognition robot recognizing the received voice signal. For example, the salesperson verbally expresses "the first step is to list a variety of fund investment projects", and then the intelligent voice recognition robot will convert the language signal expressed orally by the salesperson into a natural description language text of the flowchart, so as to provide a content basis for the subsequent flowchart generation.

[0087] It is worth noting that by performing a first preprocessing on the natural description language text of the flowchart, a preprocessed description language text can be obtained; wherein, in the process of performing the first preprocessing on the natural description language text of the flowchart, operations such as word segmentation and removal of stop words can be included to prepare for the subsequent encoding process. And by subsequently performing a first vectorization process on the preprocessed description language text, a vectorized language text can be obtained to facilitate the subsequent encoding process. Then, by formatting the vectorized language text, language tensor information can be obtained, thereby preparing for the subsequent context feature extraction; subsequently, by performing context feature extraction on the language tensor information, natural description language encoding information can be obtained, so that the first encoding process can be more accurate.

[0088] In some embodiments, Figure 3 As shown, the above step S200 may include but is not limited to steps S250 to S290.

[0089] Step S250, performing a second preprocessing on the intermediate language type information to obtain preprocessed language type information;

[0090] Step S260, performing vocabulary mapping processing on the pre-processed language type information to obtain a language type vocabulary text;

[0091] Step S270, performing a second vectorization process on the language type vocabulary text to obtain a language type vocabulary vector;

[0092] Step S280, serializing the language type vocabulary vector to obtain a serialized language type vector;

[0093] Step S290, extracting context information from the sequence speech language type vector to obtain intermediate language type encoding information.

[0094] It should be noted that in the process of performing a second encoding process on the intermediate language type information based on the second encoder to obtain the intermediate language type encoding information, firstly, the intermediate language type information is subjected to a second preprocessing process to obtain the preprocessed language type information; then, the preprocessed language type information is subjected to a vocabulary mapping process to obtain the language type vocabulary text; then, the language type vocabulary text is subjected to a second vectorization process to obtain the language type vocabulary vector; then, the language type vocabulary vector is subjected to a serialization process to obtain the serialized language type vector; then, the serialized language type vector is subjected to context information extraction to obtain the intermediate language type encoding information. Through the above technical solution, the generation of the intermediate language type information can be simpler and faster, and is prepared for the subsequent flow chart generation.

[0095] In some specific embodiments of the present application, the intermediate language type information is a language type that can be recognized and understood by the image interpreter. A second preprocessing of the intermediate language type information can obtain preprocessed language type information; then, a vocabulary mapping process is performed on the preprocessed language type information to obtain a language type vocabulary text, in preparation for subsequent vectorization processing; a second vectorization process is performed on the language type vocabulary text to obtain a language type vocabulary vector; then, a subsequent serialization process is performed on the language type vocabulary vector to obtain a serialized language type vector, in preparation for subsequent context information extraction; finally, a context information extraction process is performed on the serialized language type vector to obtain the intermediate language type encoding information, making the second encoding process more accurate and quick.

[0096] In some embodiments, Figure 4 As shown, the above step S300 may include but is not limited to step S310 to step S320.

[0097] Step S310, performing format conversion processing on the natural description language coding information and the intermediate language type coding information to obtain transformed language coding information corresponding to the natural description language coding information and transformed language type coding information corresponding to the intermediate language type coding information;

[0098] Step S320: fusing the transformed language coding information and the transformed language type coding information to obtain joint language coding information.

[0099] It should be noted that in the process of jointly processing the natural description language coding information and the intermediate language type coding information to obtain the joint language coding information, the natural description language coding information and the intermediate language type coding information are first formatted to obtain the transformed language coding information corresponding to the natural description language coding information and the transformed language type coding information corresponding to the intermediate language type coding information; then the transformed language coding information and the transformed language type coding information are fused to obtain the joint language coding information, so as to prepare for the subsequent flow chart generation.

[0100] It is worth noting that the transformation language coding information can be obtained by format conversion processing of the natural description language coding information, which is a prerequisite for the subsequent information fusion processing; the transformation language type coding information can be obtained by format conversion processing of the intermediate language type coding information, which is also a prerequisite for the subsequent information fusion processing. The subsequent fusion processing of the transformation language coding information and the transformation language type coding information can obtain the joint language coding information, which is a prerequisite for the generation of the intermediate description language information of the subsequent flowchart.

[0101] In some embodiments, Figure 5 As shown, the above step S400 may include but is not limited to step S410 to step S430.

[0102] Step S410, generating a language coding output sequence based on the joint language coding information;

[0103] Step S420, performing element segmentation processing on the language code output sequence to obtain a plurality of language code elements;

[0104] Step S430, decoding the multiple language coding elements in sequence to obtain the intermediate description language information of the flowchart.

[0105] It should be noted that, in the process of decoding the joint language coding information based on the decoder to obtain the intermediate description language information of the flowchart, first, a language coding output sequence is generated based on the joint language coding information, and then the language coding output sequence is segmented to obtain multiple language coding elements; then, the multiple language coding elements are decoded in turn to obtain the intermediate description language information of the flowchart, so as to prepare for the subsequent flowchart generation.

[0106] It is worth noting that a language coding output sequence is generated based on the joint language coding information, and then the language coding output sequence is subjected to element segmentation processing to obtain multiple language coding elements, so that the multiple language coding elements can meet the decoding requirements, thereby obtaining the intermediate description language information of the flowchart based on the multiple language coding elements.

[0107] In some embodiments, Figure 6 As shown, the above step S500 may include but is not limited to step S510 to step S530.

[0108] Step S510, analyzing and processing the intermediate description language information of the flowchart to obtain process node information, process turn information and node associated attribute information;

[0109] Step S520, using a preset layout algorithm to generate an initial flow chart according to process node information, process turn information and node associated attribute information;

[0110] Step S530, rendering the initial flowchart to obtain a target flowchart.

[0111] It should be noted that in the process of obtaining the target flowchart by performing image drawing processing on the intermediate description language information of the flowchart based on the image interpreter, the intermediate description language information of the flowchart is first analyzed and processed to obtain the process node information, process turn information and node association attribute information; then, the preset layout algorithm is used to generate the initial flowchart according to the process node information, process turn information and node association attribute information; finally, the initial flowchart is rendered to obtain the target flowchart; by using the image interpreter to perform image drawing processing on the intermediate description language information of the flowchart, the corresponding flowchart can be obtained quickly and easily, making the generation of the flowchart easier and faster.

[0112] It is worth noting that image interpreters are tools used to explain the decision-making process of deep learning models, especially image classification models. They can help us understand why the model makes a specific prediction by identifying the areas in the image that have the greatest impact on the model's output. The following are the functions and applications of image interpreters: Local Interpretable Model-Independent Explanations, which is a popular image interpreter that can be used for image classification tasks; it explains the model's predictions by identifying key areas in the image, and can output a heat map showing the pixel areas in the image that have the greatest impact on the model's decision.

[0113] In some embodiments, Figure 7 As shown, after executing the above step S500, it may include but not be limited to steps S610 to S620.

[0114] Step S610, obtaining flowchart modification information;

[0115] Step S620: adjusting the target flowchart based on the flowchart modification information and the image interpreter.

[0116] It should be noted that after obtaining the target flowchart by drawing the intermediate description language information of the flowchart based on the image interpreter, the flowchart modification information can also be obtained. Then, the target flowchart can be adjusted based on the flowchart modification information and the image interpreter, so that the flowchart adjustment process can be simpler, faster and more flexible.

[0117] It is worth noting that, according to the user's modification requirements, flowchart modification information can be generated, and then the target flowchart can be adjusted and processed conveniently and quickly according to the flowchart modification information and the image interpreter, so that the adjustment of the flowchart can be more flexible and quick.

[0118] In addition, if Figure 8 As shown, an embodiment of the present application further provides a flowchart generation device 10, including a flowchart generation model, the flowchart generation model includes a conversion model and an image interpreter, the conversion model includes a first encoder, a second encoder and a decoder, and the device also includes:

[0119] The first processing module 100 is used to obtain the natural description language text and intermediate language type information of the flowchart;

[0120] The second processing module 200 is used to perform a first encoding process on the natural description language text of the flowchart based on the first encoder to obtain natural description language encoding information; and to perform a second encoding process on the intermediate language type information based on the second encoder to obtain intermediate language type encoding information;

[0121] The third processing module 300 is used to jointly process the natural description language encoding information and the intermediate language type encoding information to obtain joint language encoding information;

[0122] The fourth processing module 400 is used to decode the joint language coding information based on the decoder to obtain the flow chart intermediate description language information;

[0123] The fifth processing module 500 is used to perform image drawing processing on the intermediate description language information of the flowchart based on the image interpreter to obtain the target flowchart.

[0124] It should be noted that, in the process of generating a flowchart, it is realized through a flowchart generation model, wherein the flowchart generation model includes a conversion model and an image interpreter, and the conversion model includes a first encoder, a second encoder and a decoder; first, the natural description language text and the intermediate language type information of the flowchart are obtained; then, based on the first encoder, the natural description language text of the flowchart is subjected to a first encoding process to obtain the natural description language encoding information; and, based on the second encoder, the intermediate language type information is subjected to a second encoding process to obtain the intermediate language type encoding information; then, the natural description language encoding information and the intermediate language type encoding information are jointly processed to obtain the joint language encoding information; then, based on the decoder, the joint language encoding information is decoded to obtain the intermediate description language information of the flowchart; finally, based on the image interpreter, the intermediate description language information of the flowchart is subjected to image drawing process to obtain the target flowchart; through the above technical solution, the flowchart can be generated intelligently, which can make the flowchart generation process more simple and quick, and thus can well promote the rapid development of financial business.

[0125] The specific implementation of the flowchart generating device 10 is substantially the same as the specific implementation of the flowchart generating method described above, and will not be described in detail herein.

[0126] In addition, if Fig. 9 As shown, an embodiment of the present application further provides an electronic device 700 , which includes: a memory 720 , a processor 710 , and a computer program stored in the memory 720 and executable on the processor 710 .

[0127] The processor 710 and the memory 720 may be connected via a bus or in other ways.

[0128] The non-transitory software programs and instructions required to implement the flowchart generation methods of the above embodiments are stored in the memory 720 , and when executed by the processor 710 , the flowchart generation methods of the above embodiments are executed.

[0129] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0130] In addition, an embodiment of the present application also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are executed by a processor 710 or a controller, for example, by a processor 710 in the above-mentioned device embodiment, so that the above-mentioned processor 710 can execute the flowchart generation method in the above-mentioned embodiment.

[0131] The above embodiments may be used in combination, and modules with the same name in different embodiments may be the same or different.

[0132] The above 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 recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0133] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0134] 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 methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the corresponding apparatus, device and computer storage medium will not be repeated here.

[0135] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, 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 should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0136] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. 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 also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0137] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with 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 smart phone, 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.

[0138] For the convenience of description, the above devices are described in terms of functions and are divided into various units. 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.

[0139] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0140] 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes 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.

[0141] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0142] 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 instructions 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.

[0143] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0144] The 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. The memory is an example of a computer-readable medium.

[0145] Computer readable media include 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 disk read-only memory (CD-ROM), digital versatile disk (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 temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0146] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0147] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" 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 be represented by: 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.

[0148] 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, etc. 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 communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0149] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0150] The above is only 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 changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A flowchart generation method, characterized in that: Applied to a flowchart generation model, the flowchart generation model includes a conversion model and an image interpreter, the conversion model includes a first encoder, a second encoder and a decoder, and the flowchart generation method includes: Obtain the flowchart natural description language text and intermediate language type information; Performing a first encoding process on the natural description language text of the flowchart based on the first encoder to obtain natural description language encoding information; and performing a second encoding process on the intermediate language type information based on the second encoder to obtain intermediate language type encoding information; Combine the natural description language encoding information and the intermediate language type encoding information to obtain combined language encoding information; Decoding the combined language coding information based on the decoder to obtain flow chart intermediate description language information; Based on the image interpreter, the intermediate description language information of the flowchart is subjected to image drawing processing to obtain a target flowchart.

2. The flowchart generation method according to claim 1, characterized in that: The first encoding process is performed on the natural description language text of the flowchart based on the first encoder to obtain natural description language encoding information, including: Performing a first preprocessing on the natural description language text of the flowchart to obtain a preprocessed description language text; Performing a first vectorization process on the preprocessed description language text to obtain a vectorized language text; Formatting the vectorized language text to obtain language tensor information; Context feature extraction is performed on the language tensor information to obtain the natural description language encoding information.

3. The flowchart generation method according to claim 1, characterized in that: The performing a second encoding process on the intermediate language type information based on the second encoder to obtain the intermediate language type encoding information includes: Performing a second preprocessing on the intermediate language type information to obtain preprocessed language type information; Performing vocabulary mapping processing on the preprocessed language type information to obtain a language type vocabulary text; Performing a second vectorization process on the language type vocabulary text to obtain a language type vocabulary vector; Serializing the language type vocabulary vector to obtain a serialized language type vector; Context information is extracted from the sequence speech language type vector to obtain the intermediate language type encoding information.

4. The flowchart generation method according to claim 1, characterized in that: The step of jointly processing the natural description language encoding information and the intermediate language type encoding information to obtain the combined language encoding information includes: Performing format conversion processing on the natural description language encoding information and the intermediate language type encoding information to obtain conversion language encoding information corresponding to the natural description language encoding information and conversion language type encoding information corresponding to the intermediate language type encoding information; The transformation language coding information and the transformation language type coding information are fused to obtain the combined language coding information.

5. The flowchart generation method according to claim 1, characterized in that: The decoding process of the combined language coding information based on the decoder to obtain the flow chart intermediate description language information includes: generating a language encoding output sequence based on the combined language encoding information; Performing element segmentation processing on the language coding output sequence to obtain a plurality of language coding elements; The multiple language coding elements are decoded in sequence to obtain the intermediate description language information of the flow chart.

6. The flowchart generation method according to claim 1, characterized in that: The image interpreter is used to perform image drawing processing on the intermediate description language information of the flowchart to obtain a target flowchart, including: Analyze and process the intermediate description language information of the flowchart to obtain process node information, process turn information and node association attribute information; Generate an initial flow chart according to the process node information, the process turn information and the node associated attribute information by using a preset layout algorithm; The initial flowchart is rendered to obtain the target flowchart.

7. The flowchart generation method according to claim 1, characterized in that: After the image interpreter performs image drawing processing on the intermediate description language information of the flowchart to obtain the target flowchart, the method further includes: Get flowchart modification information; The target flowchart is adjusted based on the flowchart modification information and the image interpreter.

8. A flow chart generating device, characterized in that: The device comprises a flow chart generation model, wherein the flow chart generation model comprises a conversion model and an image interpreter, wherein the conversion model comprises a first encoder, a second encoder and a decoder, and the device further comprises: The first processing module is used to obtain the natural description language text and intermediate language type information of the flowchart; A second processing module is configured to perform a first encoding process on the natural description language text of the flowchart based on the first encoder to obtain natural description language encoding information; and perform a second encoding process on the intermediate language type information based on the second encoder to obtain intermediate language type encoding information; A third processing module, configured to jointly process the natural description language encoding information and the intermediate language type encoding information to obtain joint language encoding information; A fourth processing module, configured to decode the joint language coding information based on the decoder to obtain flow chart intermediate description language information; The fifth processing module is used to perform image drawing processing on the intermediate description language information of the flowchart based on the image interpreter to obtain a target flowchart.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the flowchart generation method as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that: The computer executable instructions are used to execute the flowchart generation method described in any one of claims 1 to 7.

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