Platform management method, terminal and storage medium

Abstract

The invention relates to the technical field of platform management, and particularly discloses a platform management method, a terminal and a storage medium, and the method comprises the steps: firstly analyzing platform setting information, so as to enable the platform setting information to be disassembled into various platform setting contents; further introducing an artificial intelligence technology based on deep learning to carry out semantic analysis and fine-grained semantic query interaction on various platform setting contents and the preference description input by the user, so as to dynamically modulate the various platform setting contents according to the user preference information; therefore, the platform page identification information meeting the personalized requirements of the user is intelligently generated. According to the method, personalized customization of the platform page can be realized, and specific requirements of different users are met.

Description

Technical Field

[0001] The present application relates to the technical field of platform management, and more specifically, to a platform management method, a terminal and a storage medium. Background Art

[0002] In today's era of rapid digital development, various platforms play a vital role in the daily operations and lives of enterprises and users. As the scale of enterprises continues to expand and the business becomes increasingly complex, the number of information systems supporting enterprise business continues to increase. For example, many applications such as financial management, marketing management, and production management have been built and put into use, which has improved the work efficiency of enterprises to a certain extent.

[0003] In order to provide a better user experience, the platform needs to be able to adjust its platform interface and service content according to the personalized needs of users. Generally speaking, the identification information settings of platform pages mainly rely on predefined templates and limited options manually selected by users, such as the title of the platform homepage, logo image information, and the color tones and fonts of different pages. In actual application scenarios, different users often have unique preferences for the display style, functional layout, etc. of platform pages. For example, some users may prefer a concise and clear page design, while others may need a richer and more detailed information display.

[0004] However, in the existing technology, identification is simply determined based on platform setting information, lacking the ability to deeply explore and accurately respond to users' personalized needs. In addition, considering that user preferences may change over time, a good platform should allow dynamic adjustment of these settings and be able to safely and reliably save the latest configuration so that it will take effect immediately the next time you visit.

[0005] Therefore, an optimized platform management method, terminal and storage medium are needed. Summary of the invention

[0006] In order to solve the above technical problems, this application is proposed. The embodiments of this application provide a platform management method, a terminal and a storage medium, which first parses the platform setting information to decompose it into various platform setting contents, and then further introduces artificial intelligence technology based on deep learning to perform semantic parsing and fine-grained semantic query interaction on various platform setting contents and preference descriptions input by users, so as to dynamically modulate various platform setting contents according to user preference information, thereby intelligently generating platform page identification information that meets the personalized needs of users, and realizing personalized customization of platform pages to meet the specific needs of different users.

[0007] Accordingly, according to one aspect of the present application, a platform management method is provided, which includes: obtaining platform setting information, and determining identification information of a platform page based on the platform setting information, including: parsing the platform setting information to obtain a set of platform setting contents; semantically encoding each platform setting content in the set of platform setting contents to obtain a set of platform setting content semantic coding vectors; obtaining a text description of user preferences; semantically encoding the text description of user preferences to obtain a user preference semantic coding vector; performing fine-grained cross-domain association learning on the set of platform setting content semantic coding vectors and the user preference semantic coding vectors to obtain a set of user preference modulated platform setting content semantic coding vectors; and generating identification information of the platform page based on the set of user preference modulated platform setting content semantic coding vectors.

[0008] Preferably, semantically encoding the text description of the user preference to obtain the user preference semantic encoding vector includes: using a semantic encoder based on the Bert model to semantically encode the text description of the user preference to obtain the user preference semantic encoding vector.

[0009] Preferably, fine-grained cross-domain association learning is performed on the set of platform setting content semantic coding vectors and the user preference semantic coding vectors to obtain a set of user preference modulated platform setting content semantic coding vectors, including: performing association strength measurement on each platform setting content semantic coding vector in the set of user preference semantic coding vectors and the platform setting content semantic coding vectors to obtain a set of user preference-platform setting content fine-grained ablation factors; based on the set of user preference-platform setting content fine-grained ablation factors, performing fine-grained ablation modulation on the set of platform setting content semantic coding vectors to obtain the set of user preference modulated platform setting content semantic coding vectors.

[0010] Preferably, the association strength of each platform setting content semantic coding vector in the set of the user preference semantic coding vector and the platform setting content semantic coding vector is measured to obtain a set of user preference-platform setting content fine-grained ablation factors, including: performing cross-domain query interaction based on the attention mechanism on each platform setting content semantic coding vector in the set of the user preference semantic coding vector and the platform setting content semantic coding vector to obtain a set of user preference-platform setting content cross-domain query interaction feature vectors; and inputting each user preference-platform setting content cross-domain query interaction feature vector in the set of user preference-platform setting content cross-domain query interaction feature vectors into an ablation measurement function to obtain the set of user preference-platform setting content fine-grained ablation factors.

[0011] Preferably, a cross-domain query interaction based on an attention mechanism is performed on each platform setting content semantic coding vector in the set of the user preference semantic coding vector and the platform setting content semantic coding vector to obtain a set of user preference-platform setting content cross-domain query interaction feature vectors, including: performing a linear transformation on the user preference semantic coding vector to obtain a query vector and a value vector; performing a linear transformation on the platform setting content semantic coding vector to obtain a key vector; and inputting the query vector, the value vector and the key vector into a cross-domain interaction encoder based on a simulated transformer structure to obtain the user preference-platform setting content cross-domain query interaction feature vector.

[0012] Preferably, based on the set of user preference-platform setting content fine-grained ablation factors, the set of platform setting content semantic coding vectors is subjected to fine-grained ablation modulation to obtain the set of user preference modulated platform setting content semantic coding vectors, including: inputting the set of user preference-platform setting content fine-grained ablation factors into an ablation effect coding module including a normalization function and a masking function to obtain a set of user preference-platform setting content fine-grained ablation weight factors; based on the set of user preference-platform setting content fine-grained ablation weight factors, weightedly modulating the set of platform setting content semantic coding vectors to obtain the set of user preference modulated platform setting content semantic coding vectors.

[0013] According to another aspect of the present application, a terminal device is provided, which includes: a memory for storing instructions; a processor coupled to the memory, the processor being configured to execute the platform management method as described above based on the instructions stored in the memory.

[0014] According to another aspect of the present application, a storage medium is provided, on which a platform management program is stored. When the platform management program is executed by a processor, the platform management method as described above is implemented.

[0015] This application has at least the following technical effects: Compared with the prior art, the platform management method, terminal and storage medium provided by the present application first parse the platform setting information to decompose it into various platform setting contents, and then further introduce artificial intelligence technology based on deep learning to perform semantic parsing and fine-grained semantic query interaction on various platform setting contents and preference descriptions input by users, so as to dynamically modulate various platform setting contents according to user preference information, thereby intelligently generating platform page identification information that meets the personalized needs of users. The present application can realize personalized customization of platform pages to meet the specific needs of different users. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1 Flowchart of a platform management method according to an embodiment of the present application.

[0018] Figure 2 Schematic diagram of data flow of the platform management method according to an embodiment of the present application.

[0019] Figure 3 Flow chart of step S150 in the platform management method according to an embodiment of the present application.

[0020] Figure 4 A block diagram of a terminal device according to an embodiment of the present application.

[0021] Reference numerals: 10. Terminal device; 11. Processor; 12. Memory; 13. Input device; 14. Output device. DETAILED DESCRIPTION

[0022] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0023] It should be noted that the acquisition and processing of all information or data in this application are carried out in compliance with the relevant national data protection laws and policies and with the authorization given by the owner of the corresponding device.

[0024] As mentioned in the background technology above, in the prior art, identification is simply performed based on platform setting information, lacking the ability to deeply explore and accurately respond to user personalized needs. In addition, considering that user preferences may change over time, a good platform should allow dynamic adjustment of these settings and be able to safely and reliably save the latest configuration so that it will take effect immediately the next time you visit. In response to this technical problem, this application proposes a platform management method, specifically, Figure 1 Flowchart of a platform management method according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of the platform management method according to an embodiment of the present application. Figure 1 and Figure 2As shown, the platform management method according to the embodiment of the present application includes the steps of: S110, parsing the platform setting information to obtain a set of platform setting contents; S120, semantically encoding each platform setting content in the set of platform setting contents to obtain a set of platform setting content semantic encoding vectors; S130, acquiring a text description of user preferences; S140, semantically encoding the text description of user preferences to obtain a user preference semantic encoding vector; S150, performing fine-grained cross-domain association learning on the set of platform setting content semantic encoding vectors and the user preference semantic encoding vectors to obtain a set of user preference modulated platform setting content semantic encoding vectors; S160, generating identification information of the platform page based on the set of user preference modulated platform setting content semantic encoding vectors.

[0025] Specifically, platform setting information refers to a collection of data used to define the platform's appearance (such as color schemes, font styles), functional layout (such as menu location, toolbar visibility), and brand elements (such as logos, slogans, etc.). In actual applications, this type of information is usually entered by administrators or users during the first installation or use, or can be imported from other systems through API interfaces.

[0026] In the specific implementation, in order to accurately adjust the identification information of the platform page according to the user's preferences, it is necessary to first comprehensively and deeply obtain and parse the existing setting information of the platform. This step is crucial because it provides basic data for subsequent semantic encoding and association learning.

[0027] Specifically, you first need to access the data source that stores the platform settings information. For many modern Web applications and services, this information is usually stored in one or more databases, which may be in SQL or NoSQL format, depending on the specific architecture of the platform. For example, an e-commerce website may store its product categories, page templates, color themes and other information in a relational database, while behavioral data such as user click streams and dwell time may use a non-relational database to efficiently handle large-scale concurrent write operations. Therefore, obtaining platform settings information requires establishing a stable and reliable connection to these data sources and ensuring that the required data can be retrieved securely and quickly.

[0028] Next, different methods are needed to extract and organize different types of platform setting information. For static and structured information, such as page titles, logo links, etc., you can directly extract it from the database through query statements (SQL queries or other applicable query languages). This type of information often has a clear correspondence between field names and values, which is easy to parse. However, for more complex information, such as page layout rules, animation effect parameters, etc., it may be nested in configuration files in JSON or XML format, or even exist in the form of code in the front-end JavaScript framework. At this point, you need to use a parsing library (such as the json module or lxml library in Python) to decode these unstructured data and extract the key elements.

[0029] In addition, with the development of technology, more and more platforms have begun to support the provision of services through API interfaces, which has also become an important way to obtain platform setting information. The API interface allows third-party developers or internal systems to request specific resources programmatically, thereby simplifying the data exchange process across systems. In the scenario of this application, the latest version of platform setting information can be obtained by calling the relevant API, including but not limited to the latest UI component library version number, recommended style guides, etc. This not only ensures the freshness of the data, but also helps to quickly respond to platform changes.

[0030] On the basis of the above work, it is also necessary to consider the possibility that the platform setting information will change over time. The platform manager or operation team may regularly update page elements, adjust the functional layout, or introduce new features to adapt to market trends and user needs. This requires not only the ability to capture the current platform setting information at one time, but also the ability to continuously monitor and promptly detect and record any changes. One feasible method is to establish an event-driven mechanism. Whenever there is a major change in the platform, the corresponding callback function is triggered to re-evaluate the latest setting status. In addition, periodic inspection tasks can also be set to regularly synchronize the latest platform setting information to ensure that the system is always up to date.

[0031] Finally, the process of obtaining platform setting information inevitably involves privacy and security issues. Especially when it comes to user personal data or sensitive business information, strict laws and regulations must be followed, such as GDPR (General Data Protection Regulation). To this end, the principle of minimization should be given priority when designing the data collection process, and only the minimum amount of information required to complete the task should be collected, and measures such as encrypted transmission and access control should be taken to ensure data security. At the same time, the purpose of the user's data should be clearly informed and the user's consent should be obtained to maintain a transparent and trusting relationship.

[0032] In the above platform management method, the step S110 parses the platform setting information to obtain a set of platform setting contents. Specifically, considering that the platform setting information contains a variety of different setting items, in order to more accurately personalize each platform setting element, the present application further parses the platform setting information to identify and distinguish different setting contents to form a set of platform setting contents. For example, the color scheme can be further subdivided into background color, text color, button color, etc.; the functional layout can be subdivided into menu bar position, button size, icon style, etc.; brand elements can be subdivided into logo design, slogan font, etc.

[0033] In the specific implementation process, in order to convert the original and possibly disorganized platform setting information into a series of structured, easy-to-understand and process elements, it is required not only to be able to accurately identify the different setting items, but also to be able to distinguish and classify these setting items to form an organized content collection, so that each setting element can be more accurately personalized in subsequent steps.

[0034] Before parsing the platform setting information, data preprocessing is required. First, ensure the completeness and accuracy of the acquired platform setting information. This may involve interacting with multiple data sources, such as extracting records from related tables in a database, or reading specific fields in a configuration file. At the same time, the acquired data must be formatted and cleaned to remove possible invalid data, duplicate data, and data with incorrect formats.

[0035] For example, for the setting information stored in the database, some fields may have empty values ​​or not conform to the preset format due to untimely data updates or data entry errors. At this time, these abnormal values ​​need to be processed through data cleaning algorithms, such as filling empty values ​​with default values, or making reasonable repairs based on the context of the data and other related data. For data read from the configuration file, there may be format inconsistencies, such as some color values ​​are expressed in hexadecimal, while others are in RGB format. They need to be unified into a standard format for subsequent parsing and processing.

[0036] In addition, the platform setting information needs to be classified and labeled to better identify different types of setting content. This can be achieved by establishing a metadata dictionary to classify common setting items, such as visual, layout, interaction, brand, etc., and assign a unique identifier and description information to each setting item. In this way, during the parsing process, the category to which each data fragment belongs can be quickly and accurately determined, improving the efficiency and accuracy of the parsing.

[0037] A common method for parsing platform setting information is rule-based parsing, which analyzes and decomposes the platform setting information one by one according to a set of predefined parsing rules, thereby obtaining a collection of platform setting contents.

[0038] For the analysis of color schemes, the rules can be defined as: find all color-related fields, and assign color values ​​to corresponding subcategories based on the keywords in the field names (such as "background", "text", "button", etc.). For example, if there is a field called "pageBackgroundColor" and its value is "#FFFFFF", it can be identified as the background color, and "#FFFFFF" can be recorded as the background color value in the corresponding position in the platform settings content collection. For the analysis of functional layouts, the rules can be set as: based on specific layout element keywords (such as "menu bar", "button", "icon", etc.), extract its position, size, style and other related attributes. For example, for the "menuBarPosition" field, if its value is "top", the menu bar position is recorded as the top.

[0039] When parsing brand elements, for information related to logo design, the rule can be: find fields containing the keyword "logo" and extract information such as its size, graphic format, color composition, etc. For example, the "logoWidth" and "logoHeight" fields record the width and height values ​​of the logo respectively, and the "logoColorPalette" field stores the color list used by the logo. This information is integrated to form a complete logo design description and added to the platform setting content collection.

[0040] The rule-based parsing method has the advantages of being simple, direct and easy to implement, and is particularly suitable for platform setting information with a relatively fixed structure and standardized format. However, its disadvantages are also obvious. When the structure of the platform setting information changes or does not conform to the preset rules, the accuracy and completeness of the parsing may be affected, and the parsing rules need to be constantly updated and maintained.

[0041] In order to overcome the limitations of rule-based parsing methods, machine learning technology can be introduced to implement the parsing of platform setting information. The machine learning model can automatically identify and extract different setting contents by learning from a large number of labeled platform setting information samples, and has stronger adaptability and generalization capabilities.

[0042] First, a suitable machine learning model needs to be built, such as a decision tree or a deep learning model. For the input platform setting information, it needs to be converted into a feature form suitable for model processing. This can be achieved through feature engineering. For example, for color values, they can be converted into RGB component values ​​as features; for text description settings (such as descriptions of functional layouts), they can be converted into numerical features using methods such as bag-of-words models and TF-IDF. In the training phase, the model is trained using a labeled platform setting information dataset, and the model learns the characteristic patterns and rules of different setting contents. For example, through a large number of sample learning, the model can automatically identify different color scheme setting patterns and the characteristic representations of each element in the functional layout. In the actual parsing process, the platform setting information to be parsed is input into the trained model, and the model outputs the corresponding parsing results, that is, a collection of platform setting contents. The advantage of machine learning methods is that they can automatically adapt to the changes and complexity of platform setting information, and have better parsing effects for irregular and diverse setting information.

[0043] In the process of parsing platform setting information, in order to improve the efficiency and accuracy of parsing, some optimization strategies and error handling mechanisms can also be adopted. In terms of optimization strategies, a cache mechanism can be used to cache the parsed platform setting information fragments or common setting patterns that have been parsed. The next time the same or similar information is encountered, the parsing results are directly obtained from the cache to avoid repeated calculations. At the same time, parallel computing technology can be used to parallelize the parsing process of different types of setting information (such as visual elements, functional layout, etc.), making full use of the performance of multi-core processors to improve the parsing speed.

[0044] The error handling mechanism is also crucial. Since the platform setting information may contain various uncertainties and errors, such as missing data, format errors, incomplete setting descriptions, etc., these errors need to be detected and handled in a timely manner during the parsing process. When encountering missing data, you can make reasonable guesses and fill in based on the platform's default settings or the user's historical setting habits; for format errors, try to convert and repair the format. If it cannot be repaired, record the error information and notify relevant personnel for manual intervention; for incomplete setting descriptions, you can interact with the user (such as popping up a prompt box to ask the user for specific information about the relevant settings) or refer to the platform's general setting specifications to supplement and improve.

[0045] Finally, the platform setting content set obtained through the above parsing process needs to be verified and integrated to ensure its accuracy and completeness. The verification process can be carried out in a variety of ways, such as comparing and verifying with the actual display effect of the platform to check whether the parsed setting content can accurately reflect the actual presentation of the platform page; or comparing with known correct parsing result samples, and evaluating the quality of the parsing results by calculating indicators such as accuracy and recall. In terms of data integration, it is necessary to reasonably organize and structure the various platform setting contents obtained through parsing to form a complete and orderly platform setting content set. For example, the setting contents of various aspects such as visual elements, functional layout, and brand elements are arranged according to a certain hierarchical structure to facilitate subsequent semantic coding and personalized adjustment processing. At the same time, it is necessary to ensure the consistency and coordination between different setting contents to avoid contradictory setting information, such as inconsistent color matching and chaotic functional layout.

[0046] Through the above detailed implementation process of parsing the platform setting information, different elements in the platform setting information can be more accurately identified and distinguished, providing a solid data foundation for the subsequent dynamic adjustment of identification information based on user preferences. In the above platform management method, the step S120 semantically encodes each platform setting content in the set of platform setting content to obtain a set of semantic encoding vectors of platform setting content. Specifically, considering that the platform setting content may be text, label or other forms of data, it is difficult to use it directly for computer processing. Therefore, the present application further semantically encodes each platform setting content in the set of platform setting content to convert each platform setting content into a numerical vector representation, and generates a set of semantic encoding vectors of platform setting content, so that the deep learning model can perform data analysis and processing on it. In a specific implementation, for the platform setting content in text form, natural language processing technology, such as word embedding or BERT model, etc., can be used to convert the text content into a vector representation with rich semantic information. For data in the form of labels, one-hot encoding and other methods are used for conversion. For image data such as Logo design, image processing technology such as convolutional neural network (CNN) is used for feature extraction. Through the above processing, each platform setting content can be converted into a unified numerical vector format, thereby providing a basis for subsequent personalized adjustment.

[0047] In the above platform management method, the step S130 obtains a text description of the user's preference. In the technical solution of the present application, the text description of the user's preference is a text description directly input by the user, which includes the user's personalized needs for the platform interface style, functional layout, brand elements, etc. By introducing the user's preference information, the platform setting content can be adjusted more accurately to meet the user's personalized needs. For example, if the user prefers a simple interface style, the background color, text color, button color, etc. can be automatically adjusted to light tones; if the user prefers detailed information display, the page layout can be adjusted to add more content display areas.

[0048] In the above platform management method, the step S140 semantically encodes the text description of the user preference to obtain a user preference semantic encoding vector. Similarly, in order to convert the textual description of the user preference into a computer-processable format, the present application uses natural language processing technology to semantically encode the text description of the user preference to extract the deep contextual semantic information in the user preference description and generate a user preference semantic encoding vector. In a specific example of the present application, a semantic encoder based on the Bert model is used to semantically encode the text description of the user preference to obtain the user preference semantic encoding vector. Specifically, the Bert model is a pre-trained language representation model, which can capture the bidirectional context information of each word in the text through the internal bidirectional Transformer architecture, so as to fully understand the meaning of each word in a specific context and provide a more accurate semantic representation. Based on this, the present application can accurately capture the user's specific preferences and deeply understand the contextual information of the user preference description by using the Bert model to semantically encode the text description of the user preference, thereby providing high-quality semantic encoding feature representation for the text description of the user preference and providing strong support for subsequent personalized customization.

[0049] In the above platform management method, the step S150 performs fine-grained cross-domain association learning on the set of semantic coding vectors of the platform setting content and the user preference semantic coding vector to obtain a set of semantic coding vectors of the platform setting content modulated by user preference. That is, in order to achieve a deep integration of platform setting content and user preference information, the present application further performs cross-domain association learning on the set of semantic coding vectors of the platform setting content and the user preference semantic coding vector to dynamically modulate various platform setting contents using user preference information.

[0050] Figure 3 FIG. 1 is a flow chart of step S150 in the platform management method according to an embodiment of the present application. Figure 3As shown, the step S150 includes: S151, performing association strength measurement on each platform setting content semantic coding vector in the set of the user preference semantic coding vector and the platform setting content semantic coding vector to obtain a set of user preference-platform setting content fine-grained ablation factors; S152, based on the set of user preference-platform setting content fine-grained ablation factors, performing fine-grained ablation modulation on the set of platform setting content semantic coding vectors to obtain the set of user preference modulated platform setting content semantic coding vectors.

[0051] Specifically, the step S151 includes: first, performing a cross-domain query interaction based on the attention mechanism on each platform setting content semantic coding vector in the set of the user preference semantic coding vector and the platform setting content semantic coding vector to obtain a set of user preference-platform setting content cross-domain query interaction feature vectors. More specifically, the cross-domain query interaction based on the attention mechanism includes: performing a linear transformation on the user preference semantic coding vector to obtain a query vector and a value vector; performing a linear transformation on the platform setting content semantic coding vector to obtain a key vector; inputting the query vector, the value vector and the key vector into a cross-domain interaction encoder based on a simulated converter structure to obtain the user preference-platform setting content cross-domain query interaction feature vector, which is expressed as: in, represents the user preference semantic encoding vector, represents a set of semantic coding vectors of the platform settings content, , , and Respectively represent the first, second, and third in the set of semantic coding vectors of the platform setting content and Each platform sets the content semantic encoding vector, The number of vectors in the set of content semantic encoding vectors is set for the platform, , and denote the query embedding matrix, value embedding matrix and key embedding matrix respectively, , and Represent different bias terms, represents the matrix multiplication operation, , and Represent the query vector, the value vector and the The corresponding key vector, is the characteristic scale value of the key vector, is the normalized exponential function, represents the transpose of a vector, Indicates the and stated The user preference-platform setting content cross-domain query interaction feature vector.

[0052] First, a query vector and a value vector are constructed based on the user preference semantic encoding vector, a key vector is constructed based on each platform setting content semantic encoding vector in the set of platform setting content semantic encoding vectors, and the correlation between user preference information and platform setting content is captured through cross-domain query interaction based on the converter structure. In this process, the converter structure can effectively process information interaction between different domains, strengthen the correlation between user preferences and platform setting content through the attention mechanism, and thus establish a direct connection between user preferences and each platform setting content.

[0053] Then, each user preference-platform setting content cross-domain query interaction feature vector in the set of user preference-platform setting content cross-domain query interaction feature vectors is input into the ablation metric function to obtain a set of user preference-platform setting content fine-grained ablation factors, which is expressed as: in, represents the ablation metric function, Indicates taking the maximum value, and Respectively indicate the The feature mean and feature variance of represents the regularization term, Indicates the The corresponding user preference-platform setting content fine-grained ablation factor.

[0054] That is, by further introducing the ablation metric function, the cross-domain query interaction characteristics between user preference information and each platform setting content are evaluated and quantified. Among them, the ablation metric function is similar to the ablation analysis in experimental design, which is used to judge the impact of removing a certain specific relationship on the final interaction effect. It helps to identify the degree of influence of user preference information on each platform setting content, thereby ensuring that platform setting content that is highly relevant to user preferences is given priority while reducing the impact of redundant platform setting content. For example, if the user preference information indicates that the user prefers a simple interface style, then the setting content related to the interface style in the platform setting information will be given a higher weight. In this way, the personalized customization process can be ensured to be more efficient and accurate.

[0055] Specifically, the step S152 includes: first, inputting the set of user preference-platform setting content fine-grained ablation factors into an ablation effect encoding module including a normalization function and a masking function to obtain a set of user preference-platform setting content fine-grained ablation weight factors, which is expressed as: in, represents the exponential function with base e, Indicates the The corresponding normalized user preference-platform setting content fine-grained ablation factor, is the mask function, is the mask threshold, For the The corresponding user preference-platform setting content fine-grained ablation weight factor.

[0056] That is, in order to standardize the weights and exclude irrelevant items, the set of user preference-platform setting content fine-grained ablation factors is further normalized and masked. The normalization function is used to convert all ablation factors to a standard range (such as between 0 and 1) for direct comparison; and the masking function is used to increase the ablation factors of platform setting content that is significantly associated with user preferences, while reducing or excluding the ablation factors of platform setting content that is irrelevant or less relevant to user preferences, so as to focus resources and attention on the platform settings that are most closely related to user preferences.

[0057] Then, based on the set of user preference-platform setting content fine-grained ablation weight factors, the set of platform setting content semantic coding vectors is weighted modulated to obtain the set of user preference modulated platform setting content semantic coding vectors.

[0058] in, , , and Respectively , , and stated The corresponding user preference-platform setting content fine-grained ablation weight factor, A collection of semantic encoding vectors representing user preference modulation platform settings content.

[0059] Specifically, based on the generated set of user preference-platform setting content fine-grained ablation weight factors, the original set of platform setting content semantic coding vectors is fine-grained ablation modulated to strengthen the platform setting content that is highly consistent with the user preference, while also retaining the uniqueness of the original platform setting content, thereby achieving personalized customization and optimization of the platform setting content. In this way, users can enjoy platform services that are more in line with their personal needs.

[0060] In the above-mentioned platform management method, the step S160 generates the identification information of the platform page by modulating the set of semantic coding vectors of the platform setting content based on the user preference. In a specific example of the present application, after adding a prompt word to the tail of the set of semantic coding vectors of the platform setting content modulated by the user preference, the dynamic adjuster of the identification information based on the large model is input to obtain the identification information of the platform page, and the prompt word is to adjust the identification information based on user preference. That is, by using the deep learning ability of the large model, the set of semantic coding vectors of the platform setting content modulated by the user preference is further feature-learned and refined to generate platform page identification information that matches the personalized needs of the user. Here, the role of the prompt word is to guide and constrain. By explicitly specifying the prompt word as "adjusting the identification information based on user preference", it can be ensured that the large model always takes the personalized needs of the user as the primary consideration when generating the platform page identification information, which helps to maintain a high degree of consistency between the output result and the user's expectations, thereby providing an interface design that is more in line with the user's preferences. In a specific example of the present application, the large model is a GPT series model.

[0061] Preferably, after adding a prompt word at the end of the set of content semantic coding vectors of the user preference modulation platform setting, inputting a large model-based identification information dynamic adjuster to obtain the identification information of the platform page includes: First, the set of user preference modulation platform setting content semantic coding vectors is cascaded to obtain a user preference modulation platform setting content semantic cascade coding vector; Next, the distance between each pair of eigenvalues ​​of the user preference modulation platform setting content semantic concatenation coding vector is calculated, such as the L2 distance, and the square root of the distance is taken to obtain the user preference modulation platform setting content semantic concatenation distance representation matrix, that is, in, Indicates user preference modulation platform settings content semantic cascade distance representation matrix The element value at position, Represents the user preference modulation platform setting content semantic cascade coding vector, , Respectively represent the first Position and The eigenvalues ​​at the positions, represents the distance metric function; Then, the user preference modulation platform setting content semantic cascade autocorrelation matrix of the user preference modulation platform setting content semantic cascade coding vector as a row vector is obtained, that is: in, Representing the user preference modulation platform setting content semantic cascade self-correlation matrix; Secondly, the user preference modulation platform setting content semantic cascade coding vector is matrix-multiplied with the user preference modulation platform setting content semantic cascade distance representation matrix to obtain the user preference modulation platform setting content semantic cascade primary mapping vector, that is: in, Represents user preference modulation platform setting content semantic cascade distance representation matrix, Represents the user preference modulation platform setting content semantic cascade first-level mapping vector; Furthermore, the user preference modulation platform setting content semantic cascade primary mapping vector is matrix-multiplied with the matrix product of the user preference modulation platform setting content semantic cascade distance representation matrix and the user preference modulation platform setting content semantic cascade self-correlation matrix to obtain the user preference modulation platform setting content semantic cascade multi-level mapping vector, that is: in, Represents user preference modulation platform setting content semantic cascade multi-level mapping vector; Finally, the user preference modulation platform setting content semantic cascade multi-level mapping vector is interpolated with the user preference modulation platform setting content semantic cascade associated eigenvector (if the eigenvalue is insufficient, interpolation or zero padding) composed of the eigenvalues ​​of the user preference modulation platform setting content semantic cascade self-association matrix to obtain an optimized user preference modulation platform setting content semantic cascade coding vector; after adding a prompt word to the end of the optimized user preference modulation platform setting content semantic cascade coding vector, it is input into the large model-based identification information dynamic adjuster to obtain the identification information of the platform page.

[0062] Among them, considering that the set of semantic coding vectors of the platform setting content represents the semantic coding features of each platform setting content, and the user preference semantic coding vector represents the semantic coding features of user preference, when performing cross-domain attention joint encoding on them, the semantic coding features of the user preference are fine-grained ablation modulation of the semantic coding features of the platform setting contents. However, the semantic flow field offset between the semantic coding features of user preference and the semantic coding features of each platform setting content will cause the set of user preference modulated platform setting content semantic coding vectors to have a lack of fine-grained interactive feature instance determination, thereby affecting the accuracy of the platform page identification information obtained by adding the prompt word and inputting the large model-based identification information dynamic adjuster.

[0063] Therefore, by using the linear target mapping representation of the similarity distance representation matrix of the user preference modulation platform setting content semantic cascade coding vector obtained by cascading the set of content semantic coding vectors based on the user preference modulation platform setting, the self-correlation complete similarity instantiation of the user preference modulation platform setting content semantic cascade coding vector is performed with a secondary target mapping representation based on a multi-level distribution hierarchy, and the association mismatch negative influence factor is compensated by the association fusion kernel bias to improve the degree of instance determination of the eigenvalue of the user preference modulation platform setting content semantic cascade coding vector under similarity constraints, that is, the significance of the eigenvalue as an instance for generative regression determination, and improve the accuracy of the identification information of the platform page obtained by adding a prompt word to the tail of the user preference modulation platform setting content semantic cascade coding vector and inputting the identification information dynamic adjuster based on the large model.

[0064] In summary, the platform management method according to the embodiment of the present application is explained, which first analyzes the platform setting information to break it down into various platform setting contents, and then further introduces artificial intelligence technology based on deep learning to perform semantic analysis and fine-grained semantic query interaction on various platform setting contents and preference descriptions input by users, so as to dynamically modulate various platform setting contents according to user preference information, thereby intelligently generating platform page identification information that meets the personalized needs of users. In this way, personalized customization of platform pages can be achieved to meet the specific needs of different users.

[0065] Exemplary terminal equipment, see Figure 4 To describe the terminal device according to an embodiment of the present application. Figure 4 FIG. 1 is a block diagram of a terminal device according to an embodiment of the present application. Figure 4 As shown, the terminal device 10 includes one or more processors 11 and a memory 12 .

[0066] The processor 11 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the terminal device 10 to perform desired functions.

[0067] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may run the program instructions to implement the platform management method of each embodiment of the present application described above and / or other desired functions. Various contents such as platform setting information and text descriptions of user preferences may also be stored in the computer-readable storage medium.

[0068] In one example, the terminal device 10 may further include: an input device 13 and an output device 14 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0069] The input device 13 may include, for example, a keyboard, a mouse, etc.

[0070] The output device 14 can output various information to the outside, including identification information of the platform page, etc. The output device 14 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0071] Of course, to simplify, Figure 4 Only some of the components in the terminal device 10 related to the present application are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application conditions, the terminal device 10 may also include any other appropriate components.

[0072] Exemplary computer program product and computer-readable storage medium. In addition to the above-mentioned methods and systems, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the platform management method according to various embodiments of the present application described in the above-mentioned exemplary method section of this specification.

[0073] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0074] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the platform management method according to various embodiments of the present application described in the above exemplary method section of this specification.

[0075] The computer-readable storage medium may adopt any combination of one or more readable storage media. The readable storage medium may be a readable signal storage medium or a readable storage medium. The readable storage medium may, for example, include but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0076] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A platform management method, characterized in that: include: Acquiring platform setting information, and determining identification information of the platform page according to the platform setting information, including: Parsing the platform setting information to obtain a set of platform setting contents; Performing semantic coding on each platform setting content in the set of platform setting content to obtain a set of platform setting content semantic coding vectors; Get a text description of the user's preferences; Performing semantic encoding on the text description of the user preference to obtain a user preference semantic encoding vector; Performing fine-grained cross-domain association learning on the set of platform setting content semantic coding vectors and the user preference semantic coding vectors to obtain a set of user preference modulated platform setting content semantic coding vectors; A set of semantic coding vectors of platform setting content is modulated based on the user preference to generate identification information of the platform page.

2. The platform management method according to claim 1, characterized in that: Performing semantic encoding on the text description of the user preference to obtain a user preference semantic encoding vector, including: The text description of the user preference is semantically encoded using a semantic encoder based on the Bert model to obtain the user preference semantic encoding vector.

3. The platform management method according to claim 2, characterized in that: The method performs fine-grained cross-domain association learning on the set of platform setting content semantic coding vectors and the user preference semantic coding vectors to obtain a set of user preference modulation platform setting content semantic coding vectors, including: Performing association strength measurement on each platform setting content semantic coding vector in the set of the user preference semantic coding vector and the platform setting content semantic coding vector to obtain a set of user preference-platform setting content fine-grained ablation factors; Based on the set of user preference-platform setting content fine-grained ablation factors, the set of platform setting content semantic coding vectors is fine-grainedly ablated and modulated to obtain the set of user preference modulated platform setting content semantic coding vectors.

4. The platform management method according to claim 3, characterized in that: Performing association strength measurement on each platform setting content semantic coding vector in the set of the user preference semantic coding vector and the platform setting content semantic coding vector to obtain a set of user preference-platform setting content fine-grained ablation factors, including: Performing a cross-domain query interaction based on an attention mechanism on each platform setting content semantic encoding vector in the set of the user preference semantic encoding vector and the platform setting content semantic encoding vector to obtain a set of user preference-platform setting content cross-domain query interaction feature vectors; Each user preference-platform setting content cross-domain query interaction feature vector in the set of user preference-platform setting content cross-domain query interaction feature vectors is input into an ablation metric function to obtain a set of user preference-platform setting content fine-grained ablation factors.

5. The platform management method according to claim 4, characterized in that: Performing a cross-domain query interaction based on an attention mechanism on each platform setting content semantic coding vector in the set of the user preference semantic coding vector and the platform setting content semantic coding vector to obtain a set of user preference-platform setting content cross-domain query interaction feature vectors, including: Performing a linear transformation on the user preference semantic encoding vector to obtain a query vector and a value vector; Performing a linear transformation on the platform setting content semantic encoding vector to obtain a key vector; The query vector, the value vector and the key vector are input into a cross-domain interaction encoder based on an imitation transformer structure to obtain the user preference-platform setting content cross-domain query interaction feature vector.

6. The platform management method according to claim 5, characterized in that: Based on the set of user preference-platform setting content fine-grained ablation factors, fine-grained ablation modulation is performed on the set of platform setting content semantic coding vectors to obtain the set of user preference modulated platform setting content semantic coding vectors, including: Inputting the set of user preference-platform setting content fine-grained ablation factors into an ablation effect encoding module including a normalization function and a masking function to obtain a set of user preference-platform setting content fine-grained ablation weight factors; Based on the set of user preference-platform setting content fine-grained ablation weight factors, the set of platform setting content semantic coding vectors is weighted modulated to obtain the set of user preference modulated platform setting content semantic coding vectors.

7. The platform management method according to claim 6, characterized in that: Modulating a set of semantic coding vectors of platform setting content based on the user preference to generate identification information of the platform page includes: After adding a prompt word at the end of the set of content semantic coding vectors set for the user preference modulation platform, a large model-based dynamic adjuster of identification information is input to obtain identification information of the platform page, wherein the prompt word is used to adjust the identification information based on user preference.

8. A terminal device, characterized in that: include: A memory for storing instructions; A processor is coupled to the memory, and the processor is configured to execute the platform management method according to any one of claims 1 to 7 based on instructions stored in the memory.

9. A storage medium, characterized in that: The storage medium stores a platform management program, and when the platform management program is executed by the processor, the platform management method according to any one of claims 1 to 7 is implemented.

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