Page generation system and page generation method

Abstract

The invention provides a page generation system and a page generation method. The page generation system comprises a storage device and a processor. The storage device stores a scene analysis module, a feature matching module and an element integration module. The processor is coupled to the storage device and executes the scene analysis module, the feature matching module and the element integration module. The scene analysis module generates scene feature data according to the scene input data. And the feature matching module generates page component element data according to the scene feature data. And the element integration module forms a page by the page component element data according to the element space position relation information and the user portrait data. According to the page generation system and the page generation method, the page can be automatically generated.

Description

Technical Field

[0001] The present invention relates to a data processing technology, in particular to a page generation system and a page generation method. Background Art

[0002] In the design process of existing business management pages, the design and development of pages require the collaboration of multiple roles, such as a Product Owner (PO) and a System Architect (SA). For this, the Product Owner and System Architect A need to determine the page prototype according to the actual business scenario and deliver the prototype to the developers of the application program to assemble the page in a manually set manner on the development platform. Therefore, the design and development of existing pages have the problem of too low development efficiency because the process is relatively long and requires a large amount of manpower and time. Summary of the Invention

[0003] The present invention is directed to a page generation system and a page generation method that can automatically generate pages.

[0004] According to an embodiment of the present invention, the page generation system of the present invention includes a storage device and a processor. The storage device stores a scenario analysis module, a feature matching module, and an element integration module. The processor is coupled to the storage device and executes the scenario analysis module, the feature matching module, and the element integration module. The scenario analysis module generates scenario feature data according to scenario input data. The feature matching module generates page composition element data according to the scenario feature data and user portrait data. The element integration module forms the page composition element data into a page according to the element spatial position relationship information.

[0005] According to an embodiment of the present invention, the page generation method of the present invention includes the following steps: generating scenario feature data by a scenario analysis module according to scenario input data; generating page composition element data by a feature matching module according to the scenario feature data; and forming the page composition element data into a page by an element integration module according to the element spatial position relationship information and user portrait data.

[0006] Based on the above, the page generation system and the page generation method of the present invention can automatically analyze the scenario input data input by the user to generate scenario feature data, and obtain the corresponding page composition element data according to the feature matching method. Moreover, the page generation system and the page generation method of the present invention can automatically form the corresponding page according to the page composition element data and the corresponding element spatial position relationship information.

[0007] To make the above features and advantages of the present invention more obvious and understandable, the following specific embodiments are given and described in detail in conjunction with the accompanying drawings. Brief Description of the Drawings

[0008] Figure 1 is a system schematic diagram of the page generation system according to an embodiment of the present invention;

[0009] Figure 2 is a module schematic diagram of the page generation system according to an embodiment of the present invention;

[0010] Figure 3 is a flowchart of the page generation method according to an embodiment of the present invention;

[0011] Figure 4 is a flowchart of the scenario analysis according to an embodiment of the present invention;

[0012] Figure 5 is a data structure diagram of the scenario feature data according to an embodiment of the present invention;

[0013] Figure 6 is a data structure diagram of the element feature data according to an embodiment of the present invention;

[0014] Figure 7 is a data structure diagram of the spatial position relationship data according to an embodiment of the present invention.

[0015] Description of the Reference Numerals in the Drawings

[0016] 100: Page generation system;

[0017] 110: Processor;

[0018] 111: Scenario analysis module;

[0019] 112: Feature matching module;

[0020] 113: Element integration module;

[0021] 120: Storage device;

[0022] 201: Scenario input data;

[0023] 202: Scenario feature data;

[0024] 203: Element feature data;

[0025] 204: Page composition element data;

[0026] 205: Element spatial position relationship information;

[0027] 206: User portrait data;

[0028] 207: Page;

[0029] 211: Scenario feature database;

[0030] 212: Feature characteristic database;

[0031] 213: Spatial position relationship database;

[0032] 214: User portrait database;

[0033] 511: Scene classification;

[0034] 521: Scene story;

[0035] 531 - 535, 621 - 623: Dimensions;

[0036] 541 - 545, 631 - 634: Features;

[0037] 551 - 554, 641 - 644: Feature vector values;

[0038] 611: Page composition element data;

[0039] 711: Controls;

[0040] 721: Capabilities;

[0041] 731: Positions;

[0042] S310 - S330, S401 - S412: Steps. Detailed implementation manners

[0043] Now, reference will be made in detail to the exemplary embodiments of the present invention. Examples of the exemplary embodiments are illustrated in the accompanying drawings. Whenever possible, the same reference numerals are used in the drawings and the description to refer to the same or like parts.

[0044] Figure 1 is a system schematic diagram of a page generation system according to an embodiment of the present invention. Referring to Figure 1 , the page generation system 100 includes a processor 110 and a storage device 120. The processor 110 is coupled to the storage device 120. The processor 110 and the storage device 120 can be implemented as a server. In this embodiment, the page generation system 100 can be, for example, disposed in a cloud server for page developers or users to connect and operate through wired or wireless communication methods, but the present invention is not limited thereto. In one embodiment, the page generation system 100 can also be implemented by multiple processors and storage devices, and the multiple processors and the multiple storage devices can be separately disposed in different cloud devices or computing devices.

[0045] In this embodiment, the processor 110 may be a System on a Chip (SOC), or may include, for example, a Central Processing Unit (CPU) or other programmable general-purpose or special-purpose microprocessor, a Digital Signal Processor (DSP), a programmable controller, an Application Specific Integrated Circuits (ASIC), a Programmable Logic Device (PLD), other similar processing devices, or a combination of these devices. The storage device 120 may include, for example, a Dynamic Random Access Memory (DRAM), a Flash memory, or a Non-Volatile Random Access Memory (NVRAM), etc.

[0046] Figure 2 It is a schematic diagram of the modules of the page generation system according to an embodiment of the present invention. Refer to Figure 1 and Figure 2 , in this embodiment, the storage device 120 may store modules such as Figure 2 the scene analysis module 111, the feature matching module 112, and the element integration module 113 as shown. In this embodiment, the scene analysis module 111, the feature matching module 112, and the element integration module 113 may be implemented in programming languages such as JSON (JavaScript Object Notation), Extensible Markup Language (XML), or YAML, etc. And, in this embodiment, the page generation system 100 may further include a scene feature database 211, an element feature database 212, a spatial position relationship database 213, and a user profile database 214. The scene feature database 211, the element feature database 212, the spatial position relationship database 213, and the user profile database 214 may be stored in the storage device 120 or in other external storage devices.

[0047] Figure 3 It is a flowchart of the page generation method according to an embodiment of the present invention. Refer to Figures 1 to 3, the page generation system 100 can perform the following steps S310 to S330. In this embodiment, a user (or developer) can provide scene input data 201 through an input device. The input device can be, for example, a keyboard, a mouse, and / or a microphone, etc., and the present invention is not limited thereto. In this embodiment, the processor 110 can obtain the scene input data 201. In step S310, the scene analysis module 111 generates scene feature data 202 according to the scene input data 201. In step S320, the feature matching module 112 generates page composition element data 204 according to the scene feature data 202. In step S330, the element integration module 113 forms the page composition element data 204 into a page 207 according to the element spatial position relationship information 205 and the user profile data 206. The user profile data 206 can include, for example, information such as role, age, gender, or position. The element integration module 113 can combine relevant scene data according to the user profile data 206 to combine the overall page. For example, the user profile data 206 can be used to determine information for a specific region or a specific industry, and the page background colors corresponding to different regions or different industries may be different.

[0048] Specifically, in this embodiment, the scene analysis module 111 can analyze the scene input data 201 to determine the corresponding scene data. Then, the scene analysis module 111 can judge the scene category according to the corresponding scene data, and read the scene feature data 202 corresponding to the scene category from the scene feature database 211. The scene data can include various data corresponding to different data dimensions. In this embodiment, the scene data can include at least one of user profile data, time information data, event information data, geographical information data, and terminal information data.

[0049] In this embodiment, the scene analysis module 111 provides the scene feature data 202 to the feature matching module 112. In this embodiment, the feature matching module 112 can calculate the feature vector similarity between the scene feature data 202 and the element feature data 203 provided by the element feature database 212 to determine the generation of the page composition element data 204, where the feature vector similarity calculation is implemented based on an improvement of the cosine similarity calculation.

[0050] In this embodiment, the improvement of the cosine similarity calculation is achieved by improving the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm of the following formula (1). In formula (1), the symbol "TF-IDF A" is the feature vector value of the scenario feature. The symbol "TF-IDF B" is the feature vector value of the element feature. The symbol "w" is the weight coefficient of the scenario degree. The symbol "i" is the scenario dimension (i is a positive integer). In this embodiment, the feature matching module 112 can perform weighted processing on the similarity of the feature vectors of each dimension to improve the algorithm accuracy. In this regard, the weight coefficient "w" can be adjusted according to the initial value and range of the weight coefficient set by the design expert, or by analyzing the historical data of the dimensions input by the user to adjust the expert initial value.

[0051]

[0052] Moreover, the feature matching module 112 can record the scenario dimension classified by the user input into the database. The feature matching module 112 can calculate the proportion of each dimension in the total number of dimensions according to the number of single dimensions and the total number of dimensions in the database through the following formula (2) (the higher the number of a single dimension, the more important this dimension is). The feature matching module 112 can multiply the initial weight value of each dimension by the proportion of each dimension through the following formula (3), and perform normalization processing on this result to obtain the optimized weight coefficient of each dimension. In the following formula (2), the symbol "C" is the number of each dimension in the database. The symbol "∑C" is the total number of all dimensions in the database. The symbol is the optimized dimension weight coefficient.

[0053]

[0054]

[0055] In this embodiment, the scenario analysis module 111 may further analyze the scenario input data 201 to obtain user profile data 206, and store the user profile data 206 in the user profile database 214. The user profile data 206 may include information such as role, age, gender, position, etc. For example. In this embodiment, the element integration module 113 may read the corresponding element spatial position relationship information 205 from the spatial position relationship database 213. The element integration module 113 may read the corresponding user profile data 206 from the user profile database 214. The element integration module 113 may generate a page (page data) 207 based on the page composition element data 204, the element spatial position relationship information 205, and the user profile data 206. Therefore, the page generation system 100 and its method in this embodiment can effectively generate the page 207 required by the user automatically. It should be noted that the page 207 may refer to a customized page of a specific application program or web page used by a specific user for business management.

[0056] In addition, the page generation system 100 may further receive an adjustment instruction input by the user to adjust the content of the page 207 generated by the element integration module 113. Moreover, the page generation system 100 may record the adjustment history of the user for the page 207 to update the scenario feature database 211, the element feature database 212, and the spatial position relationship database 213, so as to effectively optimize the scenario feature data, the element feature data, and the spatial position relationship data.

[0057] Figure 4 It is a flowchart of the scenario analysis of the embodiment of the present invention. Refer to Figure 2 and Figure 4 , the following steps S401 to S412 further specifically illustrate the implementation details. In step S401, the processor 110 may obtain the scenario input data 201. In step S402, the scenario analysis module 111 may determine whether the scenario input data 201 is voice data or text data. In this embodiment, when the scenario input data 201 is voice data, in step S403, the scenario analysis module 111 inputs the voice data into the natural language processing model to convert the scenario input data 201 into a text data format. In step S404, the scenario analysis module 111 may input the scenario input data 201 in text data format into a machine learning model (a large model with a large number of parameters (large model)). Then, in step S406, the scenario analysis module 111 may generate scenario data through the machine learning model.

[0058] In this embodiment, when the scene input data 201 is text data, the scene analysis module 111 determines whether the text data conforms to the data specification. When the text data conforms to the data specification, in step S406, the scene analysis module 111 can directly generate scene data according to the text data. When the text data does not conform to the data specification, in step S404, the scene analysis module 111 can input the text data into a machine learning model (a large model with a large number of parameters) to generate the scene data.

[0059] In step S407, the scene analysis module 111 can determine whether the scene dimension of the scene data is greater than or equal to the dimension threshold. When the scene dimension of the scene data is less than the dimension threshold (the dimension threshold can be, for example, 2), in step S408, the scene analysis module 111 outputs a prompt input message. The page generation system 100 can display the prompt input through the display interface to request the user to supplement the input scene information. When the scene dimension of the scene data is greater than or equal to the dimension threshold, in step S409, the feature matching module 112 performs scene category analysis based on the scene data to determine the scene category. In this embodiment, the feature matching module 112 can calculate the feature vector similarity between the scene feature data and the feature data 203 provided by the feature database 212 of the elements to determine the page composition element data 204 to be generated. Alternatively, the feature matching module 112 can read the feature database 212 of the elements according to the user's selection and the scene category recommendation information to obtain the feature data 203 of the elements.

[0060] In step S410, the feature matching module 112 determines whether the scene category is determined. In step S411, when the scene category analysis does not determine the scene category, the feature matching module 112 outputs a prompt input message. The page generation system 100 can display the prompt input through the display interface to request the user to supplement the input scene information. The feature matching module 112 can directly calculate the similarity between the scene category and the categories in the database according to the supplemented input scene information by the user. And, if there is no matching scene category, the feature matching module 112 can also directly request the user to directly input the scene category information. In step S412, the feature matching module 112 can generate scene category recommendation information. The feature matching module 112 can read the feature database 212 of the elements according to the one with the highest similarity after similarity comparison in the scene category recommendation information to obtain the page composition element data 204. Therefore, the element integration module 113 can obtain the appropriate page composition element data 204 to combine with the corresponding scene for subsequent page generation processing.

[0061] Figure 5 It is the data structure diagram of the scene feature data of the embodiment of the present invention. Refer to Figure 5, for example, the data structure of the scene feature data can be as Figure 5 shown. In this embodiment, the page generation system can analyze the scene input data to obtain the scene classification 511 and the scene story 521. In this example, the scene classification 511 can correspond to, for example, the project management type of scene and the deliverable management (sub - classification). The scene story 521 can correspond to, for example, "XX scene story". The scene classification 511 can correspond to multiple dimensions 531 - 535, where the multiple dimensions 531 - 535 can correspond to, for example, "person", "thing", "time", "place", and "terminal". An instance of dimension 531 ("person") can be, for example, "the person in charge". An instance of dimension 532 ("thing") can be, for example, "document management". The multiple dimensions 531 - 535 respectively have corresponding features 541 - 545, and the features 541 - 545 can respectively correspond to different instances. Instances of feature 541 can be, for example, "view" and "high permission". Instances of feature 542 can be, for example, "download", "upload", and "large amount of data". An instance of feature 543 can be, for example, "time type". And, the features corresponding to the instances "view", "high permission", "download", and "upload" can be further converted into corresponding feature vector values 551 - 554.

[0062] Figure 6 is the data structure diagram of the element feature data of the embodiment of the present invention. Refer to Figure 6 , for example, the data structure of the element feature data can be as Figure 6 shown. The element feature database can store the element feature data corresponding to different scene categories. Taking a certain scene category as an example, the page composition element data 611 can correspond to multiple dimensions 621 - 624, such as "control", "operation", "operation", and "layout". An instance of dimension 621 can be "table". An instance of dimension 622 can be "batch download". The dimensions 621 - 624 can respectively correspond to different features 631 - 634. Instances of feature 631 can be, for example, "large amount of data" and "view". Instances of feature 632 can be, for example, "download" and "batch operation". And, the features corresponding to the instances "large amount of data", "view", "download", and "batch operation" can be further converted into corresponding feature vector values 641 - 644. In this regard, the page generation system can, through the similarity calculation of the above - mentioned embodiment, for example, determine that Figure 5 the feature vector values 551 - 554 of Figure 6 and Figure 6 the feature vector values 641 - 644 of

[0063] Figure 7 is the data structure diagram of the spatial position relationship data of the embodiment of the present invention. Refer to Figure 7 , for example, the spatial position relationship data can be as Figure 7As shown, the page generation system reads the database according to the user profile data and the scenario category to obtain the corresponding spatial position relationship data. The spatial position relationship data may, for example, have a control 711, an ability 721, and a position 731. The page generation system can fill the corresponding instances "table", "batch download", and "upper right corner" into the corresponding spatial positions in the page according to the content of the above page composition element data 611 to effectively generate the page.

[0064] In summary, the page generation system and the page generation method of the present invention can automatically generate a design page according to the scenario input data corresponding to a specific business scenario input by the user, and can be published after minor adjustments, effectively reducing the time required for page research and development and effectively reducing the research and development costs. Moreover, the page generation system of the present invention can enable developers to no longer need to understand the complex configuration rules of the development platform, effectively reducing the learning cost and the setting cost.

[0065] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A page generation system, characterized in that: include: A storage device for storing a scene analysis module, a feature matching module, and an element integration module; as well as a processor, coupled to the storage device, and executing the scene analysis module, the feature matching module, and the element integration module, The scene analysis module generates scene feature data according to the scene input data, and the feature matching module generates page component element data according to the scene feature data. The element integration module integrates the page component data into a page according to the element spatial position relationship information and the user portrait data.

2. The page generation system according to claim 1, characterized in that: When the scene input data is text data, the scene analysis module determines whether the text data conforms to the data specification. When the text data meets the data specification, the scene analysis module directly generates scene data according to the text data. When the text data does not meet the data specification, the scenario analysis module inputs the text data into a machine learning model to generate the scenario data.

3. The page generation system according to claim 2, characterized in that: When the scene input data is voice data, the scene analysis module inputs the voice data into a natural language processing model to convert the scene input data into a text data format, and inputs it into the machine learning model to generate the scene data.

4. The page generation system according to claim 3, characterized in that: The scene analysis module determines whether the scene dimension of the scene data is greater than or equal to a dimension threshold, When the scene dimension of the scene data is less than the dimension threshold, the scene analysis module outputs prompt input information, When the scene dimension of the scene data is greater than or equal to the dimension threshold, the feature matching module performs scene category analysis according to the scene data to determine the scene category.

5. The page generation system according to claim 4, characterized in that: When the scene category analysis fails to determine the scene category, the feature matching module outputs another prompt input information, When the scene category analysis determines the scene category, the feature matching module outputs scene category recommendation information corresponding to the scene category.

6. The page generation system according to claim 5, characterized in that: The feature matching module calculates feature vector similarity between the scene feature data and the element feature data provided by the element feature database to determine the generated page component element data.

7. The page generation system according to claim 6, characterized in that: The feature matching module reads the element feature database according to the user selection and the scene category recommendation information to obtain element feature data.

8. The page generation system according to claim 6, characterized in that: The feature matching module reads the element feature database according to the one with the highest similarity after similarity comparison in the scene category recommendation information to obtain the page component element data.

9. The page generation system according to claim 6, characterized in that: The feature vector similarity calculation is implemented based on an improvement of cosine similarity calculation.

10. The page generation system according to claim 2, characterized in that: The scenario data includes at least one of the user portrait data, time information data, event information data, region information data and terminal information data.

11. A page generation method, characterized in that: include: Generate scene feature data according to scene input data through a scene analysis module; Generate page component element data according to the scene feature data through a feature matching module; as well as The element data of the page are combined into a page through an element integration module according to the element spatial position relationship information and user portrait data.

12. The page generation method according to claim 11, characterized in that: The step of generating the scene feature data comprises: When the scene input data is text data, the scene analysis module determines whether the text data conforms to the data specification; When the text data meets the data specification, directly generating scene data according to the text data by the scene analysis module; and When the text data does not conform to the data specification, the text data is input into the machine learning model through the scene analysis module to generate the scene data.

13. The page generation method according to claim 12, characterized in that: The step of generating the scene feature data further includes: When the scene input data is voice data, the voice data is input into a natural language processing model through the scene analysis module to convert the scene input data into a text data format, and then input into the machine learning model to generate the scene data.

14. The page generation method according to claim 13, characterized in that: The steps of generating the page component element data include: Determining, by the scene analysis module, whether the scene dimension of the scene data is greater than or equal to a dimension threshold; When the scene dimension of the scene data is less than the dimension threshold, outputting prompt input information through the scene analysis module; and When the scene dimension of the scene data is greater than or equal to the dimension threshold, the feature matching module performs scene category analysis based on the scene data to determine the scene category.

15. The page generation method according to claim 14, characterized in that: The step of generating the page component element data also includes: When the scene category analysis fails to determine the scene category, the feature matching module outputs another prompt input information; and When the scene category analysis determines the scene category, the feature matching module outputs scene category recommendation information corresponding to the scene category.

16. The page generation method according to claim 15, characterized in that: The step of generating the page component element data also includes: The feature matching module calculates feature vector similarity between the scene feature data and the element feature data provided by the element feature database to determine the generated page component data.

17. The page generation method according to claim 16, characterized in that: The step of generating the page component element data also includes: The feature matching module reads the element feature database according to the user selection and the scene category recommendation information to obtain element feature data.

18. The page generation method according to claim 16, characterized in that: The step of generating the page component element data also includes: The feature matching module reads the element feature database according to the one with the highest similarity after similarity comparison in the scene category recommendation information to obtain the page component element data.

19. The page generation method according to claim 16, characterized in that: The feature vector similarity calculation is implemented based on an improvement of cosine similarity calculation.

20. The page generation method according to claim 12, characterized in that: The scenario data includes at least one of the user portrait data, time information data, event information data, region information data and terminal information data.

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