Data generation and dynamic rendering method

Abstract

The invention belongs to the technical field of interface display, and relates to a data generation and dynamic rendering method, which comprises the following steps: when a target instruction is detected, triggering according to a target data set to generate target text content; identifying key fields and semantic node positions contained in the target text content; and controlling the target text content to be rendered to the target display position by adopting a text rendering component capable of performing rendering state control, and performing content rendering at the key fields and the semantic node positions in a differentiated manner. The method is applied to a scene for generating and dynamically rendering display data to a target interface according to an interface interaction instruction, and real-time generation and controllable rendering display of target data are achieved. Specifically, the method is applied to the field of financial services, service evaluation or service recommendation data can be intelligently generated and automatically displayed according to historical financial service evaluation data, real-time generation and controllable rendering display of interface display data are achieved, and target personnel can browse and view the data conveniently.

Description

Technical Field

[0001] This application relates to the field of interface display technology, and is applied to scenarios where data is generated and dynamically rendered to a target interface based on interface interaction instructions. It relates to a data generation and dynamic rendering method. Background Technology

[0002] Traditional technologies require pushing text-based data such as news, information, and discount messages to terminals to facilitate users' timely access to the latest news or business information. However, servers store a large amount of news or business information, necessitating the selection of appropriate data to recommend to users.

[0003] In recent years, with the increasing investment in online processing of financial services, it has become necessary to recommend business-related information or comprehensive data to users, such as business evaluation data and business recommendation data. Current improvement methods often take into account the large number of users involved in financial business data itself, and no longer obtain relevant data from other channels. Instead, they analyze and process relevant business data from historical users to generate financial business recommendation data. Although this method reduces the complexity of obtaining reference data, it still lacks certain output improvements for the generated target data. Summary of the Invention

[0004] The purpose of this application is to propose a data generation and dynamic rendering method to improve the output of target data.

[0005] In a first aspect, embodiments of this application provide a data generation and dynamic rendering method, which adopts the following technical solution: A data generation and dynamic rendering method includes the following steps: When a target instruction is detected, target text content generation is triggered based on the target dataset; Identify key fields and semantic node locations contained in the target text content; A text rendering component capable of rendering state control is used to control the rendering of the target text content to the target display position. During the rendering of the target text content, the rendering of key fields and semantic nodes is differentiated.

[0006] Secondly, embodiments of this application also provide a data generation and dynamic rendering apparatus, which adopts the following technical solution: A data generation and dynamic rendering apparatus, comprising: The text content generation module is used to generate target text content based on the target dataset when a target instruction is detected. The text content recognition module is used to identify key fields and semantic node positions contained in the target text content; The text content rendering module is used to control the rendering of the target text content to the target display position using a text rendering component that allows for rendering state control. During the rendering of the target text content, the rendering of key fields and semantic nodes is performed differently.

[0007] Thirdly, embodiments of this application also provide a computer device that adopts the technical solution described below: A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the data generation and dynamic rendering method described above.

[0008] Fourthly, embodiments of this application also provide a computer-readable storage medium, which adopts the technical solutions described below: A computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of the data generation and dynamic rendering method described above.

[0009] Compared with the prior art, the embodiments of this application have the following main advantages: The data generation and dynamic rendering method described in this application, upon detecting a target instruction, triggers the generation of target text content based on the target dataset; identifies key fields and semantic node positions contained in the target text content; and employs a text rendering component capable of rendering state control to control the rendering of the target text content to the target display position. Specifically, during the rendering of the target text content, the rendering of key fields and semantic node positions is differentiated. Applied to scenarios where data is generated and dynamically rendered to a target interface based on interface interaction instructions, this method achieves real-time generation and controllable rendering of target data. Specifically, in the financial business field, it can intelligently generate and automatically display business evaluation or recommendation data based on historical financial business evaluation data, achieving real-time generation and controllable rendering of interface display data, facilitating browsing and viewing by target personnel. Attached Figure Description

[0010] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is an exemplary system architecture diagram to which this application can be applied; Figure 2 This is a flowchart of an embodiment of a data generation and dynamic rendering method according to this application; Figure 3 This is a flowchart of a specific embodiment of the latest instruction detection in the data generation and dynamic rendering method described in this application; Figure 4 yes Figure 2 A flowchart of a specific embodiment of step 201 shown; Figure 5 yes Figure 4 A flowchart of a specific embodiment of step 402 shown; Figure 6 yes Figure 5 A flowchart of a specific embodiment of step 501 shown; Figure 7 yes Figure 2 A flowchart of a specific embodiment of step 202 shown; Figure 8 yes Figure 2 A flowchart of a specific embodiment of step 203 shown; Figure 9 This is a schematic diagram of a structure of an embodiment of a data generation and dynamic rendering apparatus according to this application; Figure 10 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0012] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0013] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0014] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0015] like Figure 1 As shown, system architecture 100 may include terminal device 101, network 102, and server 103. Terminal device 101 may be a laptop 1011, tablet 1012, or mobile phone 1013. Network 102 is used as a medium to provide a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables.

[0016] Users can use terminal device 101 to interact with server 103 via network 102 to receive or send messages, etc. Various communication client applications can be installed on terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0017] Terminal device 101 can be various electronic devices with a display screen and support web browsing. In addition to laptops 1011, tablets 1012, or mobile phones 1013, terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer, and a desktop computer, etc.

[0018] Server 103 can be a server that provides various services, such as a backend server that provides support for the pages displayed on terminal device 101.

[0019] It should be noted that the data generation and dynamic rendering method provided in this application embodiment is generally executed by a server, and correspondingly, a data generation and dynamic rendering device is generally set in the server.

[0020] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0021] Continue to refer to Figure 2The diagram illustrates a flowchart of an embodiment of a data generation and dynamic rendering method according to this application. The data generation and dynamic rendering method includes the following steps: Step 201: When the target instruction is detected, the target text content is generated based on the target dataset.

[0022] In this embodiment, the target instruction includes a data rendering instruction, such as a data rendering instruction sent to the backend through a click button in the front-end business interface.

[0023] In this embodiment, the detection of the target instruction can be achieved by performing human-computer interaction detection on the front-end interface.

[0024] In this embodiment, the target dataset includes pre-collected historical display data, while the target text content includes the latest display data intelligently generated based on the historical display data.

[0025] Specifically, for example, when the target dataset is pre-collected user historical evaluation data, the target text content includes comprehensive evaluation data intelligently generated based on the user historical evaluation data.

[0026] In this embodiment, a specific application scenario of step 201 is, for example, when a target data rendering instruction is detected from the front-end interface, comprehensive evaluation data generation is triggered based on pre-collected historical user evaluation data. Another example is, when a recommendation data rendering instruction is detected from the front-end interface, service recommendation data generation is triggered based on pre-collected historical user service selection data.

[0027] Specifically, the data generation and dynamic rendering method provided in this embodiment can be applied to financial business scenarios. For example, based on a user's historical evaluation of a certain financial business, a comprehensive evaluation opinion can be generated and displayed to new users, or based on a user's historical selection of financial businesses, financial business recommendations can be made to new users.

[0028] Step 202: Identify the key fields and semantic node positions contained in the target text content.

[0029] In this embodiment, the identification of key fields and semantic node positions contained in the target text content can be achieved using keyword filtering and semantic parsing methods.

[0030] By identifying key fields and semantic node positions contained in the target text content, the text content at the key fields and semantic node positions can be rendered differently when the target text content is rendered subsequently.

[0031] Step 203: Use a text rendering component that can control the rendering state to control the rendering of the target text content to the target display position.

[0032] Specifically, during the rendering of the target text content, the rendering of the key fields and the semantic node positions is differentiated.

[0033] Specifically, the rendering method of the text content at the key field and the semantic node position is set in advance in the text rendering component. The rendering method includes text font style and text display style, such as color, font size, font format, etc.

[0034] By employing a text rendering component that allows for rendering state control, the target text content is rendered to the target display position. This enables differentiated rendering of key fields and semantic nodes during the rendering of the target text content, allowing users of the target display interface to more intuitively and quickly distinguish the key content within the target display position.

[0035] In this embodiment, upon detecting a target instruction, target text content is generated based on the target dataset. Key fields and semantic node positions within the target text content are identified. A text rendering component with rendering state control is used to control the rendering of the target text content to the target display position. During the rendering of the target text content, the rendering of key fields and semantic node positions is differentiated. This data generation and dynamic rendering method is applied to scenarios where data is generated and dynamically rendered to a target interface based on interface interaction instructions, achieving real-time generation and controllable rendering of target data. Specifically, in the financial business field, for example, intelligently generating and automatically displaying business evaluation or recommendation data based on historical financial business evaluation data enables real-time generation and controllable rendering of interface display data, facilitating browsing and viewing by target personnel.

[0036] Continue to refer to Figure 3 In some specific implementations, a step of detecting the latest instruction is included before step 201. Figure 3 This is a flowchart of a specific embodiment of the data generation and dynamic rendering method described in this application, which includes: Step 301: Use a preset instruction detection component to detect the latest instruction of the target operation terminal, wherein the preset instruction detection component includes an instruction trigger detection component based on the front-end interface; Step 302: If the latest instruction is not the target instruction, then continue to detect the latest instruction of the target operation terminal, wherein the target instruction includes a data rendering instruction.

[0037] Specifically, a command trigger detection component based on the front-end interface is used to detect the latest human-computer interaction processing command issued by the front-end interface. If the latest processing command is not the expected target command, the detection of the latest command from the target operation terminal continues. This ensures that when the front-end interface issues a target command through human-computer interaction, the target command can be detected in a timely manner, and subsequent processing steps can be executed promptly according to the target command.

[0038] Continue to refer to Figure 4 , Figure 4 yes Figure 2 A flowchart of a specific embodiment of step 201 shown includes: Step 401: If the latest instruction is the target instruction, then obtain the full target data given by the historical users, wherein the full target data includes full evaluation data or full service selection data. Step 402: Generate optimal target data based on the full target data to obtain the target text content, wherein the optimal target data corresponds to the optimal evaluation data or the optimal business recommendation data.

[0039] Specifically, step 201 implements the generation of optimal evaluation data by combining the full set of evaluation data provided by historical users when the latest instruction is detected as the target instruction, or it implements the generation of optimal business recommendation data by combining the full set of business selection data provided by historical users when the latest instruction is detected as the target instruction. The full set of target data provided by historical users can be obtained from the historical business cache platform.

[0040] In this embodiment, before performing the step of generating optimal target data based on the full target data to obtain the target text content, the method further includes: extracting field keywords for each target data in the full target data according to a preset keyword dictionary, and obtaining the field keyword extraction results corresponding to each target data.

[0041] Specifically, based on a preset keyword dictionary, keyword extraction is performed on each piece of target data in the full target data. The keyword dictionary can be deployed in the keyword recognition component as a basis for keyword recognition. The keyword recognition component is then trained by combining the full target data to achieve the preset recognition requirements.

[0042] Continue to refer to Figure 5 , Figure 5 yes Figure 4 A flowchart of a specific embodiment of step 402 shown includes: Step 501: Trigger the input of the full target data into a preset semantic understanding agent to identify the text semantics corresponding to each target data, wherein the preset semantic understanding agent includes a semantic understanding agent based on text serialization encoding and spatiotemporal attention mechanism; In this embodiment, the semantic understanding agent can adopt the transformer architecture encoding and decoding mode to realize text serialization encoding. At the same time, the introduction of the temporal feature extraction network LSTM can optimize the self-attention mechanism in the transformer architecture encoding and decoding mode into a spatiotemporal attention mechanism. This allows the semantic understanding agent to pay more attention to the contextual semantic information when encoding and decoding the input text content, thereby improving the accuracy of text semantic recognition.

[0043] Step 502: Based on the text semantics corresponding to each piece of target data, summarize the overall text semantics of the full set of target data to obtain the overall semantic tendency of the full set of target data; Specifically, when summarizing the overall text semantics of the full target data to obtain the overall semantic tendency of the full target data, the text semantic categories corresponding to each target data and the semantic measurement values ​​of each type of text semantics can be accumulated and statistically analyzed. Finally, the text semantics corresponding to the maximum value of the accumulated statistical values ​​are selected to obtain the overall semantic tendency of the full target data.

[0044] Step 503: Generate optimal target data based on the overall semantic tendency and the preset keyword dictionary; Step 504: Output the optimal target data as the target text content.

[0045] Specifically, optimal evaluation data or optimal business recommendation data are generated based on the overall semantic tendency and the preset keyword dictionary.

[0046] Continue to refer to Figure 6 , Figure 6 yes Figure 5 A flowchart of a specific embodiment of step 501 shown includes: Step 601: Use the text serialization encoding component of the semantic understanding agent to perform text serialization encoding on each piece of target data to obtain at least one text-encoded statement contained in each piece of target data. Step 602: Perform text semantic parsing on all text-encoded statements, and use the preset semantic relevance algorithm in the semantic understanding agent to remove text-encoded statements whose semantic understanding contribution does not exceed the preset contribution threshold, and only retain text-encoded statements whose semantic understanding contribution exceeds the preset contribution threshold. Specifically, the input full target data can be used as corpus data. Then, using the GloVe word co-occurrence algorithm, word vectors of all keywords are generated statistically from the perspective of global word co-occurrence. Based on the co-occurrence of all keywords in each data point, keyword combinations with greater semantic understanding contribution and keyword combinations with smaller semantic understanding contribution are identified. Finally, based on different keyword combinations, text-encoded sentences with semantic understanding contribution not exceeding a preset contribution threshold and text-encoded sentences with semantic understanding contribution exceeding the preset contribution threshold are selected.

[0047] Step 603: Using the spatiotemporal attention mechanism of the semantic understanding agent, determine the relationship of the retained text-encoded statements as the same target data, and organize the sequential relationship of the retained text-encoded statements in the same target data. Specifically, the spatiotemporal attention mechanism of the semantic understanding agent focuses more on the contextual and semantic information of the input data. Therefore, by using this mechanism, even after deleting content with low semantic contribution to a target data, the sequential positional relationship of the remaining content in the target data can still be identified.

[0048] Step 604: Based on the semantic understanding contribution of different text encoding statements in the same target data, perform classification and accumulation processing to obtain the multiple text semantics contained in the same target data and the semantic metric value of each text semantic.

[0049] Continue to refer to Figure 7 , Figure 7 yes Figure 2 A flowchart of a specific embodiment of step 202 shown includes: Step 701: Based on the keyword dictionary, identify all keywords contained in the target text content as the key fields, and mark the key fields with a first distinguishing marker; Specifically, in identifying key fields, the keyword dictionary is used directly to identify all keywords contained in the target text content as key fields.

[0050] In this embodiment, if the keyword dictionary is deployed as the basis for keyword recognition in the keyword recognition component as described above, the target text content can be input into the keyword recognition component to identify the key fields.

[0051] Step 702: Input the target text content into the preset semantic understanding agent; Step 703: Using the semantic understanding agent, identify the multiple types of text semantics contained in the target text content and the semantic metric value of each type of text semantics. Step 704: Based on the multiple types of text semantics contained in the target text content and the text content position corresponding to each type of text semantics in the target text content, determine the semantic node positions corresponding to the multiple types of text semantics respectively, and use the second distinguishing marker to mark the semantic node positions.

[0052] Specifically, the entire target data has already been input into the semantic understanding agent for semantic understanding processing, which is equivalent to training the semantic understanding agent. Therefore, in steps 702 to 704, the input target text content can be subjected to corresponding semantic understanding parsing to finally determine the semantic node positions corresponding to the various text semantics in the target text content.

[0053] In this embodiment, the text rendering component capable of rendering state control includes a text rendering component based on the Svelte framework. The text rendering component is constructed using front-end rendering and display tags in the Svelte framework. The ability to control the rendering state includes performing differentiated rendering and display processing on the text content at key fields and semantic node positions compared to the ordinary text content in the target text content.

[0054] Continue to refer to Figure 8 , Figure 8 yes Figure 2 A flowchart of a specific embodiment of step 203 shown includes: Step 801: Import the target text content as data to be rendered and displayed into the text rendering component that can control the rendering state; Step 802: Based on the first and second distinguishing markers, determine the key fields and semantic node positions contained in the target text content; Step 803: For the text content in the target text content that is not important and is located at non-semantic node positions, dynamically display it to the target display position according to the first rendering display frequency; Step 804: For the key fields and semantic node positions contained in the target text content, dynamically display them to the target display position according to other rendering display frequencies that are different from the first rendering display frequency. The different rendering display frequencies include different display delay durations between adjacent characters when the text is displayed character by character.

[0055] Specifically, when rendering the target text content using the text rendering component with rendering state control, the rendering of key fields, text content at semantic node positions, non-key fields, and text content at non-semantic node positions in the target text content is differentiated according to the first differentiation marker and the second differentiation marker. The different rendering display frequencies include different display delay durations between adjacent characters when displaying text character by character. For example, if the first 10 characters in the target text content are neither non-key fields nor text content at semantic node positions, they are displayed at a normal display speed interval, such as a character appearance speed of 0.2 seconds, meaning that after the previous character appears, there is a 0.2-second delay before the next character is displayed. If the 11th to 15th characters in the target text content are key fields, they are displayed at a slower display speed interval, such as a character appearance speed of 2 seconds, meaning that after the previous character appears, there is a 2-second delay before the next character is displayed. This achieves character-by-character display control of the text in the target text content.

[0056] In this embodiment, the step of dynamically displaying the text content at key fields and semantic node positions contained in the target text content at a rendering frequency different from the first rendering frequency to the target display position includes: identifying the rendering frequency priority preset for key fields and semantic node positions; if the current text to be rendered is only a key field or only the text content at the semantic node position, then the current text to be rendered is displayed at the corresponding rendering frequency; if the current text to be rendered is both a key field and the text content at the semantic node position, then the current text to be rendered is displayed at the rendering frequency corresponding to the highest rendering frequency priority between the two.

[0057] Specifically, if a certain piece of text to be displayed is both a key field and a semantic node, it will be displayed in accordance with the pre-set rendering frequency priority to avoid conflicts during dynamic rendering.

[0058] In this embodiment, after performing the step of dynamically displaying the text content at the key fields and semantic node positions contained in the target text content at a rendering frequency different from the first rendering frequency to the target display position, the method further includes: displaying the text content at the key fields and semantic node positions contained in the target text content in a personalized manner according to a preset color and animation alternation frequency.

[0059] Specifically, when displaying the key fields and semantic nodes of the target text content on the target interface, colors and animations can be introduced for personalized display to make it more eye-catching and easy for target users to browse or view.

[0060] In this embodiment, upon detecting a target instruction, target text content is generated based on the target dataset. Key fields and semantic node positions within the target text content are identified. A text rendering component with rendering state control is used to control the rendering of the target text content to the target display position. During the rendering of the target text content, the rendering of key fields and semantic node positions is differentiated. This data generation and dynamic rendering method is applied to scenarios where data is generated and dynamically rendered to a target interface based on interface interaction instructions, achieving real-time generation and controllable rendering of target data. Specifically, in the financial business field, for example, intelligently generating and automatically displaying business evaluation or recommendation data based on historical financial business evaluation data enables real-time generation and controllable rendering of interface display data, facilitating browsing and viewing by target personnel.

[0061] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0062] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0063] In this embodiment, upon detecting a target instruction, target text content is generated based on the target dataset. Key fields and semantic node positions within the target text content are identified. A text rendering component with rendering state control is used to control the rendering of the target text content to the target display position. During the rendering of the target text content, the rendering of key fields and semantic node positions is differentiated. This data generation and dynamic rendering method is applied to scenarios where data is generated and dynamically rendered to a target interface based on interface interaction instructions, achieving real-time generation and controllable rendering of target data. Specifically, in the financial business field, for example, intelligently generating and automatically displaying business evaluation or recommendation data based on historical financial business evaluation data enables real-time generation and controllable rendering of interface display data, facilitating browsing and viewing by target personnel.

[0064] Further reference Figure 9 As a response to the above Figure 2 The implementation of the method shown in this application provides an embodiment of a data generation and dynamic rendering apparatus, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0065] like Figure 9 As shown, the data generation and dynamic rendering device 900 described in this embodiment includes: a text content generation module 901, a text content recognition module 902, and a text content rendering module 903. Wherein: The text content generation module 901 is used to generate target text content based on the target dataset when a target instruction is detected. The text content recognition module 902 is used to identify key fields and semantic node positions contained in the target text content; The text content rendering module 903 is used to control the rendering of the target text content to the target display position using a text rendering component that can control the rendering state. In the process of rendering the target text content, the rendering of the key fields and the semantic nodes is performed differently.

[0066] This application, upon detecting a target instruction, triggers the generation of target text content based on the target dataset; identifies key fields and semantic node positions within the target text content; and employs a text rendering component with rendering state control to control the rendering of the target text content to the target display position. During the rendering of the target text content, the rendering of key fields and semantic node positions is differentiated. This data generation and dynamic rendering method is applied to scenarios where data is generated and dynamically rendered to a target interface based on interface interaction instructions, achieving real-time generation and controllable rendering of target data. Specifically, in the financial business field, for example, it can intelligently generate and automatically display business evaluation or recommendation data based on historical financial business evaluation data, enabling real-time generation and controllable rendering of interface display data, facilitating browsing and viewing by target personnel.

[0067] In this embodiment, the data generation and dynamic rendering device 900 further includes a latest instruction detection module and a latest instruction continuous detection module. Wherein: The latest instruction detection module is used to detect the latest instructions of the target operation terminal using a preset instruction detection component, wherein the preset instruction detection component includes an instruction trigger detection component based on the front-end interface. The latest instruction continuous detection module is used to continue detecting the latest instruction of the target operation terminal if the latest instruction is not the target instruction, wherein the target instruction includes a data rendering instruction.

[0068] In this embodiment, the text content generation module 901 includes a full target data acquisition unit and a target text content generation unit. Wherein: The full target data acquisition unit is used to acquire the full target data given by historical users if the latest instruction is the target instruction. The target text content generation unit is used to generate optimal target data based on the full amount of target data to obtain the target text content.

[0069] In this embodiment, the data generation and dynamic rendering device 900 further includes a keyword extraction module. The keyword extraction module is used to extract field keywords from each piece of target data in the full target data according to a preset keyword dictionary, so as to obtain the field keyword extraction results corresponding to each piece of target data.

[0070] In this embodiment, the data generation and dynamic rendering device 900 further includes a text semantic recognition module, an overall semantic tendency summarization module, an optimal target data generation module, and a target text content output module. Wherein: The text semantic recognition module is used to trigger the input of the full amount of target data into a preset semantic understanding agent, and to identify the text semantics corresponding to each piece of target data. The preset semantic understanding agent includes a semantic understanding agent based on text serialization encoding and spatiotemporal attention mechanism. The overall semantic tendency summarization module is used to summarize the overall text semantics of the full set of target data based on the text semantics corresponding to each target data item, and obtain the overall semantic tendency of the full set of target data. The optimal target data generation module is used to generate optimal target data based on the overall semantic tendency and the preset keyword dictionary; The target text content output module is used to output the optimal target data as the target text content.

[0071] In this embodiment, the text semantic recognition module includes a text encoding unit, a text semantic parsing and processing unit, a unit for organizing the sequence of encoded statements, and a text semantic classification and representation unit. Wherein: The text encoding unit is used to perform text serialization encoding on each piece of target data using the text serialization encoding component of the semantic understanding agent, so as to obtain at least one text-encoded statement contained in each piece of target data. The text semantic parsing processing unit is used to perform text semantic parsing on all text-encoded statements respectively, and use the preset semantic relevance algorithm in the semantic understanding agent to remove text-encoded statements whose semantic understanding contribution does not exceed the preset contribution threshold, and only retain text-encoded statements whose semantic understanding contribution exceeds the preset contribution threshold. The sequence relationship organization unit is used to determine the relationship between the retained text encoded statements and the same target data by utilizing the spatiotemporal attention mechanism of the semantic understanding agent, and to organize the sequence relationship between the retained text encoded statements in the same target data. The text semantic classification and representation unit is used to classify and accumulate the semantic understanding contributions of different text-encoded statements in the same target data to obtain the multiple text semantics contained in the same target data and the semantic metric value of each text semantic.

[0072] In this embodiment, the text content recognition module 902 includes a key field recognition and marking unit, an input semantic understanding agent unit, a text semantic recognition unit, and a semantic node position marking unit. Wherein: The key field identification and marking unit is used to identify all keywords contained in the target text content according to the keyword dictionary, as the key fields, and mark the key fields with a first distinguishing mark; An input semantic understanding agent unit is used to input the target text content into the preset semantic understanding agent; The text semantic recognition unit is used to identify, using the semantic understanding agent, multiple types of text semantics contained in the target text content and the semantic metric value of each type of text semantics; The semantic node position marking unit is used to determine the semantic node positions corresponding to the multiple types of text semantics contained in the target text content and the text content position corresponding to each type of text semantic in the target text content, and to mark the semantic node positions using a second distinguishing marker.

[0073] In this embodiment, the text content rendering module 903 includes a rendering data import unit, a differentiation marker position determination unit, a conventional rendering processing unit, and a differentiation rendering processing unit. Wherein: The rendering data import unit is used to import the target text content as data to be rendered and displayed into the text rendering component that can control the rendering state. The differentiation marker location determination unit is used to determine the key fields and semantic node locations contained in the target text content based on the first differentiation marker and the second differentiation marker; The conventional rendering processing unit is used to dynamically display the text content in the target text content at the target display position according to the first rendering display frequency, targeting non-key fields and non-semantic node positions in the target text content. The differentiated rendering processing unit is used to dynamically display the text content at key fields and semantic node positions contained in the target text content at the target display position according to other rendering display frequencies different from the first rendering display frequency. The different rendering display frequencies include different display delay durations between adjacent characters when the text is displayed character by character.

[0074] In this embodiment, the text content rendering module 903 further includes a rendering priority identification unit, a non-conflict rendering processing unit, and a conflict rendering processing unit. Wherein: The rendering priority recognition unit is used to identify the pre-set rendering display frequency priority for key fields and semantic node positions; The non-conflict rendering processing unit is used to display the current text to be rendered according to the corresponding rendering display frequency if the current text to be rendered is only an important field or only the text content at the semantic node position. The rendering processing unit under conflict is used to display the text to be rendered according to the rendering frequency corresponding to the highest rendering frequency priority between the two if the text to be rendered is both an important field and the text content at the semantic node position.

[0075] In this embodiment, the text content rendering module 903 further includes a personalized display unit, which is used to display the key fields and semantic node positions contained in the target text content in a personalized manner according to a preset color and animation alternation frequency.

[0076] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0077] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0078] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 10 , Figure 10 This is a basic structural block diagram of the computer device in this embodiment.

[0079] The computer device 10 includes a memory 10a, a processor 10b, and a network interface 10c, which are interconnected via a system bus. It should be noted that... Figure 10Only a computer device 10 with component memory 10a, processor 10b, and network interface 10c is shown. However, it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0080] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0081] The memory 10a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 10a may be an internal storage unit of the computer device 10, such as the hard disk or memory of the computer device 10. In other embodiments, the memory 10a may also be an external storage device of the computer device 10, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Of course, the memory 10a may include both internal storage units and external storage devices of the computer device 10. In this embodiment, the memory 10a is typically used to store the operating system and various application software installed on the computer device 10, such as computer-readable instructions for a data generation and dynamic rendering method. In addition, the memory 10a can also be used to temporarily store various types of data that have been output or will be output.

[0082] In some embodiments, the processor 10b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 10b is typically used to control the overall operation of the computer device 10. In this embodiment, the processor 10b is used to execute computer-readable instructions stored in the memory 10a or to process data, for example, to execute computer-readable instructions for the data generation and dynamic rendering method described above.

[0083] The network interface 10c may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 10 and other electronic devices.

[0084] The computer device proposed in this embodiment belongs to the field of interface display technology and is applied to scenarios where data is generated and dynamically rendered to a target interface based on interface interaction instructions. When a target instruction is detected, this application triggers the generation of target text content based on the target dataset; identifies key fields and semantic node positions contained in the target text content; and uses a text rendering component with rendering state control to control the rendering of the target text content to the target display position. During the rendering of the target text content, the rendering of key fields and semantic node positions is differentiated. Applying this data generation and dynamic rendering method to scenarios where data is generated and dynamically rendered to a target interface based on interface interaction instructions achieves real-time generation and controllable rendering of target data. Specifically, in the financial business field, for example, it can intelligently generate and automatically display business evaluation or recommendation data based on historical financial business evaluation data, enabling real-time generation and controllable rendering of interface display data, facilitating browsing and viewing by target users.

[0085] This application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions that can be executed by a processor to cause the processor to perform the steps of the data generation and dynamic rendering method described above.

[0086] The computer-readable storage medium proposed in this embodiment belongs to the field of interface display technology and is applied to scenarios where data is generated and dynamically rendered to a target interface based on interface interaction instructions. When a target instruction is detected, this application triggers the generation of target text content based on the target dataset; identifies key fields and semantic node positions contained in the target text content; and uses a text rendering component with rendering state control to control the rendering of the target text content to the target display position. During the rendering of the target text content, the rendering of key fields and semantic node positions is differentiated. Applying this data generation and dynamic rendering method to scenarios where data is generated and dynamically rendered to a target interface based on interface interaction instructions achieves real-time generation and controllable rendering of target data. Specifically, in the financial business field, for example, it can intelligently generate and automatically display business evaluation or recommendation data based on historical financial business evaluation data, enabling real-time generation and controllable rendering of interface display data, facilitating browsing and viewing by target users.

[0087] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0088] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to make the disclosure of this application more thorough and comprehensive. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application. Software tools or components not belonging to this company that appear in the embodiments of this application are merely illustrative examples and do not represent actual use.

Claims

1. A data generation and dynamic rendering method, characterized in that, Includes the following steps: When a target instruction is detected, target text content generation is triggered based on the target dataset; Identify key fields and semantic node locations contained in the target text content; A text rendering component capable of rendering state control is used to control the rendering of the target text content to the target display position. During the rendering of the target text content, the rendering of key fields and semantic nodes is differentiated.

2. The data generation and dynamic rendering method according to claim 1, characterized in that, Before triggering the step of generating target text content based on the target dataset when executing the target detection instruction, the method further includes: The latest command of the target operation terminal is detected by a preset command detection component, wherein the preset command detection component includes a command trigger detection component based on the front-end interface; If the latest instruction is not the target instruction, then the detection of the latest instruction of the target operation terminal continues, wherein the target instruction includes a data rendering instruction; When a target instruction is detected, the step of triggering the generation of target text content based on the target dataset specifically includes: If the latest instruction is the target instruction, then obtain all target data given by historical users; The target text content is obtained by generating optimal target data based on the full set of target data.

3. The data generation and dynamic rendering method according to claim 2, characterized in that, Before performing the step of generating optimal target data based on the full target data to obtain the target text content, the method further includes: Based on the preset keyword dictionary, the field keywords are extracted from each piece of target data in the full target data, and the field keyword extraction results corresponding to each piece of target data are obtained. The step of generating optimal target data based on the full set of target data to obtain the target text content includes: Triggering the input of the full target data into a preset semantic understanding agent to identify the text semantics corresponding to each target data, wherein the preset semantic understanding agent includes a semantic understanding agent based on text serialization encoding and spatiotemporal attention mechanism; Based on the text semantics corresponding to each target data item, the overall text semantics of the full target data are summarized to obtain the overall semantic tendency of the full target data. Optimal target data is generated based on the overall semantic tendency and the preset keyword dictionary; The optimal target data is output as the target text content.

4. The data generation and dynamic rendering method according to claim 3, characterized in that, The step of triggering the input of the full set of target data into a preset semantic understanding agent to identify the text semantics corresponding to each piece of target data specifically includes: The text serialization encoding component of the semantic understanding agent is used to perform text serialization encoding on each piece of target data to obtain at least one text-encoded statement contained in each piece of target data. All text-encoded statements are subjected to text semantic parsing, and the semantic relevance algorithm preset in the semantic understanding agent is used to remove text-encoded statements whose semantic understanding contribution does not exceed the preset contribution threshold, and only text-encoded statements whose semantic understanding contribution exceeds the preset contribution threshold are retained. Using the spatiotemporal attention mechanism of the semantic understanding agent, the relationship between the retained text-encoded statements and the target data is determined, and the sequential relationship between the retained text-encoded statements in the same target data is organized. Based on the semantic understanding contribution of different text encoding statements in the same target data, classification and accumulation processing is performed to obtain the multiple text semantics contained in the same target data and the semantic metric value of each text semantic category.

5. The data generation and dynamic rendering method according to claim 3, characterized in that, The step of identifying key fields and semantic node locations contained in the target text content specifically includes: Based on the keyword dictionary, all keywords contained in the target text content are identified as the key fields, and the key fields are marked with a first distinguishing marker. The target text content is input into the preset semantic understanding agent; Using the semantic understanding agent, the target text content is identified as containing multiple types of text semantics and the semantic metric value of each type of text semantics. Based on the multiple types of text semantics contained in the target text content and the text content position corresponding to each type of text semantics in the target text content, the semantic node positions corresponding to the multiple types of text semantics are determined, and the semantic node positions are marked using a second distinguishing marker.

6. The data generation and dynamic rendering method according to claim 5, characterized in that, The text rendering component capable of rendering state control includes a text rendering component based on the Svelte framework. The step of using this text rendering component to control the rendering of the target text content to the target display position specifically includes: The target text content is imported as data to be rendered and displayed into the text rendering component that can control the rendering state; Based on the first and second distinguishing markers, the key fields and semantic node positions contained in the target text content are determined; For non-key fields and non-semantic node positions in the target text content, the text content is dynamically displayed to the target display position according to the first rendering display frequency; For the key fields and semantic node positions contained in the target text content, the text content is dynamically displayed to the target display position according to other rendering display frequencies that are different from the first rendering display frequency. The different rendering display frequencies include different display delay durations between adjacent characters when the text is displayed character by character.

7. The data generation and dynamic rendering method according to claim 6, characterized in that, The step of dynamically displaying the text content at key fields and semantic node positions contained in the target text content at a rendering frequency different from the first rendering frequency to the target display position includes: Identify the pre-set rendering and display frequency priorities for key fields and semantic node locations; If the text to be rendered is only a key field or only the text content at the semantic node position, then the text to be rendered is displayed according to the corresponding rendering frequency. If the text to be rendered is both an important field and the text content at a semantic node location, then the text to be rendered is displayed according to the rendering frequency corresponding to the highest rendering frequency priority between the two. After executing the step of dynamically displaying the text content at the key fields and semantic node positions contained in the target text content at a rendering frequency different from the first rendering frequency to the target display position, the method further includes: Based on preset color and animation alternation frequencies, the text content at key fields and semantic node locations within the target text is displayed in a personalized manner.

8. A data generation and dynamic rendering apparatus, characterized in that, include: The text content generation module is used to generate target text content based on the target dataset when a target instruction is detected. The text content recognition module is used to identify key fields and semantic node positions contained in the target text content; The text content rendering module is used to control the rendering of the target text content to the target display position using a text rendering component that allows for rendering state control. During the rendering of the target text content, the rendering of key fields and semantic nodes is performed differently.

9. A computer device, characterized in that, The method includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the data generation and dynamic rendering method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the data generation and dynamic rendering method as described in any one of claims 1 to 7.

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