Data processing method, device, electronic device and storage medium

Abstract

The present disclosure provides a data processing method, device, electronic device and storage medium, wherein the data processing method includes: collecting data on users to be collected based on a preset result information library to obtain target preset result information and user basic information of the users to be collected; converting the target preset result information and user basic information into abstract data in a standardized format; and performing social matching processing based on the abstract data. In the present disclosure, multi-dimensional analysis of user data can be performed to provide users with a comprehensive and in-depth personalized experience, and through accurate social matching and personalized recommendations, social interaction between users can be better promoted to form meaningful social connections.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a data processing method, device, electronic device and storage medium. Background Art

[0002] At present, with the development of the Internet and social media, users have generated a large amount of personal data on network platforms. The privacy protection and security processing of this data has become a key issue. In related technologies, common privacy protection measures include data encryption, anonymization, and access control. However, when facing complex and advanced attack methods, there is still a risk of being cracked, and the security of user data cannot be fully guaranteed.

[0003] In addition, the social matching algorithms in related technologies are usually based on simple feature matching, lack comprehensive analysis of users' multi-dimensional characteristics, fail to deeply analyze users' complex psychological and behavioral patterns, and cannot achieve accurate social matching, resulting in poor matching results and affecting users' social experience. Moreover, related technologies are facing increasingly stringent legal and regulatory requirements in terms of privacy protection and data processing. Compliance issues have become a major challenge. How to effectively use data for personalized analysis and recommendations while protecting user privacy, existing technologies have not yet found the best balance. Summary of the invention

[0004] In view of this, in order to solve the above technical problems, the present disclosure provides a data processing method, device, electronic device and storage medium.

[0005] According to a first aspect of an embodiment of the present disclosure, a data processing method is provided, the data processing method comprising:

[0006] Based on the preset result information library, data is collected from the user to be collected, and target preset result information and user basic information of the user to be collected are obtained; wherein the preset result information library includes multiple original preset results, and the target preset result information includes multiple target preset results corresponding to the user to be collected;

[0007] Converting the target preset result information and the user basic information into abstract data in a standardized format;

[0008] Performing visual abstraction mapping on the abstracted data to obtain corresponding visual abstraction information;

[0009] The visual abstract information is processed based on artificial intelligence technology to obtain a visualized art work.

[0010] Optionally,

[0011] The abstracted data includes numerical identification information; and / or,

[0012] The visual abstract information includes at least one of the following: color information, light information, sound information, shape information, texture information, and icon information.

[0013] Optionally, when the abstract data includes numerical identification information, and the visual abstract information includes color information, performing visual abstract processing on the abstract data to obtain corresponding visual abstract information includes:

[0014] Sorting the positions of the multiple numerical identifiers in the data identifier information;

[0015] The plurality of numerical identifiers after position sorting are subjected to color abstraction mapping to obtain the color information composed of a plurality of color identifiers; wherein the plurality of color identifiers correspond one to one to the plurality of numerical identifiers.

[0016] Optionally, after converting the target preset result information and the user basic information into abstract data in a standardized format, the data processing method includes:

[0017] If the data injection authorization instruction is collected, social matching processing is performed based on the abstracted data;

[0018] or,

[0019] If the data injection authorization instruction is not collected, saving the abstracted data;

[0020] The data injection authorization instruction indicates that the user to be collected agrees to use the abstracted data for social matching processing.

[0021] Optionally, performing social matching processing based on the abstracted data includes:

[0022] Based on the abstract data, determine the Lingxi value corresponding to the user to be collected; wherein the Lingxi value is an indicator for quantifying the personalized characteristics of the user;

[0023] Based on the Lingxi similarity between the Lingxi value of the user to be collected and the Lingxi values ​​of other users, social matching processing is performed on the user to be collected; wherein the Lingxi similarity represents the similarity degree of personalized characteristics between two users.

[0024] Optionally, the preset result information library is constructed through historical data of multiple dimensions, wherein the multiple dimensions include at least two of the following: psychological, physiological, cognitive, social, cultural, interactive, interesting and sensory.

[0025] According to a second aspect of an embodiment of the present disclosure, a data processing device is provided, the data processing device comprising:

[0026] A data collection module, used to collect data from the user to be collected based on the preset result information library, and obtain the target preset result information and user basic information of the user to be collected; wherein the preset result information library includes a plurality of original preset results, and the target preset result information includes a plurality of target preset results corresponding to the user to be collected;

[0027] A data security module, used to convert the target preset result information and the user basic information into abstract data in a standardized format; and also used to perform visual abstract mapping on the abstract data to obtain corresponding visual abstract information;

[0028] The art module is used to process the visual abstract information based on artificial intelligence technology to obtain a visual art work.

[0029] Optionally, the data processing device includes a social matching module, configured to:

[0030] After converting the target preset result information and the user basic information into abstract data in a standardized format,

[0031] If the data injection authorization instruction is collected, social matching processing is performed based on the abstracted data;

[0032] or,

[0033] If the data injection authorization instruction is not collected, saving the abstracted data;

[0034] The data injection authorization instruction indicates that the user to be collected agrees to use the abstracted data for social matching processing.

[0035] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, the electronic device comprising:

[0036] processor;

[0037] a memory for storing instructions executable by the processor;

[0038] Wherein, the processor is configured to execute the data processing method as described in any one of the first aspects.

[0039] According to a fourth aspect of an embodiment of the present disclosure, a non-temporary computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can perform the data processing method as described in any one of the first aspects.

[0040] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: In the present disclosure, data collection can be performed on the users to be collected based on the preset result information library, so as to obtain multiple target preset results and user basic information corresponding to the users to be collected, and the multiple target preset results constitute the target preset result information. Then, the target preset result information and the user basic information are converted into abstract data in a standardized format to facilitate subsequent data processing, and to a certain extent ensure the security of the data and better protect user privacy. In addition, after obtaining the abstract data in a standardized format, social matching processing can be performed based on the abstract data. In the present disclosure, multi-dimensional analysis of user data can be performed to provide users with a comprehensive and in-depth personalized experience. Through accurate social matching and personalized recommendations, social interaction between users can be better promoted to form meaningful social connections.

[0041] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0043] Figure 1 The figure is a flow chart of a data processing method according to an exemplary embodiment.

[0044] Figure 2 is a flowchart of a data processing method according to another exemplary embodiment.

[0045] Figure 3 is a block diagram of a data processing device according to an exemplary embodiment.

[0046] Figure 4 is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0047] The following will describe the implementation methods of the present application with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, not for limiting the scope of protection of the present application.

[0048] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0049] To facilitate understanding of the embodiments of the present application, further explanation will be given below with reference to specific embodiments in conjunction with the accompanying drawings. The embodiments do not constitute a limitation on the embodiments of the present application.

[0050] The embodiments of the present disclosure provide a data processing method, device, electronic device and storage medium. In the present disclosure, data can be collected from users to be collected based on a preset result information library, so as to obtain multiple target preset results and user basic information corresponding to the users to be collected, and multiple target preset results constitute target preset result information. Then, the target preset result information and user basic information are converted into abstract data in a standardized format to facilitate subsequent data processing, and to ensure data security to a certain extent, and better protect user privacy. In addition, after obtaining the abstract data in a standardized format, social matching processing can be performed based on the abstract data. In the present disclosure, user data can be analyzed in multiple dimensions to provide users with a comprehensive and in-depth personalized experience. Through accurate social matching and personalized recommendations, social interaction between users can be better promoted to form meaningful social connections.

[0051] In an exemplary embodiment, a data processing method is provided, which can be applied to an electronic device. For example, it can be applied to a privacy data abstraction and security protection algorithm system (PASPAS) in an electronic device. Figure 1 As shown, the method may include:

[0052] S110, collecting data from the user to be collected based on the preset result information library, and obtaining target preset result information and user basic information of the user to be collected; wherein the preset result information library includes multiple original preset results, and the target preset result information includes multiple target preset results corresponding to the user to be collected;

[0053] S120, converting the target preset result information and the user basic information into abstract data in a standardized format;

[0054] S130: Perform social matching processing based on the abstracted data.

[0055] In step S110, the electronic device may be equipped with a data collection module, which may include an outcome presetting and feedback collection and processing system (OPFPS). The system may be responsible for presetting the user interaction results (recorded as the original preset results) and collecting the user's personal data. By analyzing the user's response in different interaction links, the preset results are dynamically adjusted and optimized to ensure the efficiency and accuracy of data collection, while improving the user experience.

[0056] The system can be a multi-dimensional analysis system, focusing on the design of experience content and the collection of corresponding result feedback. It can integrate at least two key dimensions of multiple dimensions such as psychology, physiology, cognition, social, culture, interaction, interest and sensory (preferably including all the above-mentioned psychological, physiological, cognitive, social, cultural, interactive, interesting and sensory dimensions), and adopt a multidisciplinary cross-integration design approach based on the specific needs of the project for user trait data. When designing interactive content, the system can provide users with feedback mechanisms and channels at the same time to facilitate accurate analysis and collection of user traits. In other words, the preset result information library is constructed through historical data of multiple dimensions, wherein the multiple dimensions include at least two of the following: psychology, physiology, cognition, social, culture, interaction, interest and sensory, etc.

[0057] The core advantage of the result preset and feedback collection and processing system lies in its comprehensive multi-dimensional analysis method. This method goes beyond the limitations of a single data point and adopts a comprehensive perspective to deeply understand user behavior and preferences. Each dimension provides a unique perspective and deep insights for the overall analysis, making it easier to build a rich and three-dimensional user portrait in the future.

[0058] In this step, before data collection, a series of possible user interaction results (i.e., original preset results) can be scientifically preset. These original preset results are intended to capture the user's behavior and preferences in specific interactions. These preset results will serve as a preset result information library to guide the data collection of the users to be collected.

[0059] When determining the original preset results, a comprehensive preset result information library can be built based on in-depth analysis of user behavior, psychological principles, and past interaction data. The preset result information library can cover a wide range of user behaviors and preferences to ensure that the user's response in a specific interaction scenario can be accurately captured.

[0060] In order to achieve the above goals, data mining technology, machine learning algorithms and behavioral science theories can be used to conduct a comprehensive analysis of users' historical interaction data. By identifying patterns and trends, the possible behavior results of users can be predicted and the original preset results can be designed accordingly. The original preset results can include not only direct behavioral feedback, but also users' emotional tendencies, preferences, potential needs, etc.

[0061] In addition, when designing the original preset results, demographic information, cultural differences, and personalized characteristics can be considered to enhance the diversity and inclusiveness of the original preset results. In this way, the scientificity and practicality of the preset results can be ensured, providing a solid foundation for subsequent data analysis and user trait insights.

[0062] After completing the configuration of the preset result information library, the user to be collected can interact with the above-mentioned data collection system so that the data collection system can collect the personal data of the user to be collected, and obtain the target preset result information corresponding to the above-mentioned personal data based on the preset result information library, that is, the target preset result information of the user to be collected.

[0063] The interactive process may include games, questionnaires or various activities conducted through any interactive platform. The main purpose of designing this interaction is to collect personal data such as user behavior data and feedback data.

[0064] In some embodiments,

[0065] The user to be collected is recorded as user A, who uses an extended reality (XR) device to participate in a virtual fun constellation interaction including elves and animals.

[0066] In this session, user A is faced with a choice. There are three mysterious containers in front of him, each of which contains a different type of elf. User A can freely choose a elf to use based on his own preferences and the magic of the constellation.

[0067] Among them, the container and its characteristics are as follows:

[0068] a. Round container, red in color, containing a fire dragon spirit;

[0069] b. A square container, blue in color, containing a water dragon spirit;

[0070] c. A triangular container, green in color, containing a grass dragon elf.

[0071] After careful consideration, User A finally chooses option a - a round red fire dragon elf.

[0072] In this interaction between the system and user A, the user's personality and preferences can be fully captured to collect various personal data of user A. In this implementation, 9 target preset results can be collected, each targeting a specific feature or behavior habit of user A.

[0073] Among them, geographical location: that is, the geographical location of user A, which helps us understand the user's cultural background and possible environmental influences. Dominant hand: that is, the hand that user A is accustomed to using, which may be related to certain operating habits and preferences. Height: that is, the height information of user A, which can be used for personalized interface design and adjustment of interactive experience. Average heart rate: that is, the average heart rate data of user A in different situations, reflecting his physiological response and emotional state. Online time: that is, the time when user A logs into the system, which helps to analyze his active time and usage habits. Zodiac sign: that is, the zodiac sign to which user A belongs, which can increase the fun of user interaction. Preferred music type: that is, the music style preferred by user A, which can reflect his aesthetic tendency and emotional expression. Shape preference: that is, user A's preference for different shapes, which may be related to his personal taste and design preference. Color preference: that is, the color that user A likes. Color preference can reveal the user's emotional and psychological characteristics. Type of elves used: that is, the type of elves that user A chooses to use in the interaction, which can reflect his personality and decision-making style.

[0074] It should be noted that in addition to collecting the personal data of the user to be collected through the above-mentioned methods, it can also be collected through other methods, without limitation.

[0075] In addition, in addition to collecting the target preset result information, basic user information can also be collected simultaneously in this step. Basic user information can refer to information that is not included in the preset result information library and represents the basic characteristics of the user. For example, basic user information can refer to the gender, age, name, etc. of the user to be collected, which is not limited to this.

[0076] In step S120, after obtaining the target preset result information and user basic information of the user to be collected, the collected information can be converted into a standardized format. The standardized format may be different in design and logic according to different project and technical requirements, and there is no limitation on this. It should be noted that the setting of the standardized format is based on the analysis and processing of programs, servers, auxiliary plug-ins, and deep algorithms.

[0077] Among them, a data security module can be configured in the electronic device, and the data security module can include a privacy data security and abstraction system (PDSA). The system focuses on protecting the security of user privacy data. Through abstraction technology, personal privacy data is converted into data in an abstract format that is difficult to reverse engineer, ensuring the highest level of privacy protection.

[0078] In some embodiments,

[0079] When designing an abstract representation of user data, a prefix relationship system consisting of three main components can be introduced, including abstraction level (L), preset result (PR) and digital identifier (DID). The following is an introduction to the meaning of these three prefixes.

[0080] Abstraction level (L):

[0081] Abstraction levels are high-level modules used to classify user data. Each level (L) represents a specific area or aspect of user data, such as basic information, preferences, or behavior patterns. By defining different abstraction levels, user data can be organized and processed more systematically, ensuring that each level focuses on specific user characteristics.

[0082] Default result (PR):

[0083] Under each abstraction level, a series of preset results (PR) are set, which are the expected options or outputs for specific attributes or behaviors in that level. Preset results allow for standardized encoding of possible user choices or feedback, thereby simplifying the data processing process and laying the foundation for further analysis and application.

[0084] Digital Identifier (DID):

[0085] A digital identifier (DID) is a unique numeric code assigned to each preset result. This identifier provides a concise, standardized method to reference specific user choices or behaviors, ensuring accuracy and consistency during data storage, retrieval, and analysis. They represent the user's characteristics or preferences on a specific dimension. By assigning a DID to each preset result, data related to a specific user behavior can be quickly identified and processed.

[0086] Specifically, abstract level (L): First, the user's handedness information can be classified in the "personal basic information level" (L1) because it is part of the description of the user's basic characteristics.

[0087] Preset result (PR): Under the L1 level, a preset result "dominant hand" (L1_PR2) can be defined. This preset result is specifically used to capture the information of the user's main hand.

[0088] Among them, a certain digital identifier (DID) is assigned a series of digital identifiers to represent different options for the preset result of "dominant hand".

[0089] For example:

[0090] L1_PR2_DID:001 stands for "right-handed"

[0091] L1_PR2_DID:002 stands for "left-handed"

[0092] L1_PR2_DID:003 stands for "Both hands are equally flexible"

[0093] Example description:

[0094] Assume that user A (the user to be collected) extracts his hand-dominant information through the XR device during the interaction. Based on the user's hand-dominant information, the system will use the following prefixes to identify this information:

[0095] If user A is right-handed, the system will record this information as: L1_PR2_DID:001.

[0096] If user A is left-handed, the system will record this information as: L1_PR2_DID:002.

[0097] If user A is equally agile in both hands, the system will record this information as: L1_PR2_DID:003.

[0098] In this way, the information of user A's dominant hand is clearly and accurately recorded in the system, while maintaining the standardization and consistency of the data. This prefix system not only facilitates data management and analysis within the system, but also helps to quickly retrieve and apply user information when needed, providing users with more personalized services and experiences.

[0099] It can be seen from the above introduction that the abstracted data may include numerical identification information composed of multiple numerical identifications. Each data identification corresponds to a target preset result or a basic feature in the user's basic information. For example, in this implementation, if the user's dominant hand is the right hand, the target preset result corresponding to the user's dominant hand information can be converted into the standardized format of L1_PR2_DID:001 through a one-to-one mapping relationship between multiple original preset results and multiple digital identifications.

[0100] It should be noted that, in addition to converting the target preset result information into abstract data in a standardized format, other methods can also be used for conversion, and there is no limitation on this. In addition, the method for converting the user basic information into abstract data in a standardized format can refer to the above-mentioned method for processing the target preset result information, which will not be described in detail.

[0101] In this embodiment, reference Figure 2 As shown, after obtaining the abstract data, the abstract data can be further abstracted:

[0102] S121, performing visual abstraction mapping on the abstracted data to obtain corresponding visual abstraction information;

[0103] S122. Process the visual abstract information based on artificial intelligence technology to obtain a visualized artwork.

[0104] In step S121 , the visual abstract information may be, for example, color information, light information, sound information, shape information, texture information, or icon information, etc., without limitation.

[0105] Among them, when the abstract data includes numerical identification information and the visual abstract information includes color information, in the process of performing visual abstraction processing on the abstract data to obtain the corresponding visual abstraction information, the multiple numerical identifications in the data identification information can be first sorted by position according to the set rules, and then the multiple numerical identifications after position sorting can be subjected to color abstraction mapping to obtain color information composed of multiple color identifications; wherein the multiple color identifications correspond one-to-one to the multiple numerical identifications.

[0106] In some embodiments,

[0107] In the process of visual abstraction mapping, the privacy data security and abstraction system can convert the standardized abstract data into concrete abstraction mapping objects. Each numerical identifier in each abstract data can be assigned a specific abstraction representation, which will then be used to create a visual concrete representation. Visual abstraction mapping not only increases the artistry of the data, but also provides an innovative way of expression for further processing of the data.

[0108] In this implementation, the visual abstraction mapping is set as a color mapping for example, and the purpose is to visually abstract the abstract data obtained by the system again and extract colors through a color mapping table, thereby forming an abstract mapping object with higher data security and artistry.

[0109] In this implementation, the numerical identifiers in the data identification information in the standardized format can be sorted according to the set rules. This step helps to organize the data into a structure that is easy to understand and analyze. The set rules can be set according to actual needs and are not limited to this. Then, the sorted numerical identifiers are mapped to specific colors to obtain corresponding color information. It should be noted that color mapping not only adds visual identification to the data, but also enhances the expressiveness of the data through the diversity and distinction of colors.

[0110] In this implementation, through this series of steps, color mapping creates a highly abstract data representation that has the following advantages:

[0111] Enhanced privacy protection: The original data is processed through multiple layers of abstraction to ensure strict confidentiality of user privacy;

[0112] Data consistency: Standardized numerical identifiers ensure data consistency and interoperability across the entire system;

[0113] Easy to analyze: Position sorting and color mapping make data easier for humans to understand and machines to analyze;

[0114] Visual Expression: The use of color provides an intuitive visual expression of the data, helping to quickly identify and distinguish different data points.

[0115] For specific color mapping, you can create a color table through one-hot encoding, or you can use the color gamut to create a color relationship with a smooth transition. Here we take one-hot encoding as an example:

[0116]

[0117]

[0118] It should be noted that, in addition to the above-mentioned visual abstraction processing, visual abstraction processing can also be performed in other ways, which are not limited to this.

[0119] In step S122, artificial intelligence (AI) technology can be used to transform the user's abstract data into a visual work of art, thereby enhancing the emotional resonance of the data and providing the user with a new personalized visual experience. At the same time, important user data will be abstracted again to protect user privacy.

[0120] This step is committed to pushing artistic and innovative expression to a new level. Through this step, we not only focus on the aesthetic value of artworks, but also attach importance to further enhancing the security of user data through artistic transformation.

[0121] In this embodiment, the data that has been subjected to multiple abstract processing has been stripped from its original form and converted into a series of abstract symbols and numerical values. On this basis, AI technology is used to convert these abstract data points into artworks with unique style and expression. Among them, AI technology can include AI painting technology, AI 3D modeling technology, and other AI technologies related to artistic processing, which are not limited to this.

[0122] Among them, an artistic module can be set in the electronic device, and the artistic module can include an artistic data processing and innovative expression system (Colour). Through the above system combined with AI technology and deep learning algorithms, user data can be converted into visual works of art, enhancing the emotional resonance of the data, and bringing users a new personalized experience and innovative expression form.

[0123] It should be noted that this embodiment can use AI technology based on the Latent Diffusion Model (LDM). This type of model can use random noise in the latent space to gradually generate images through a series of iterative steps. Taking the Stable Diffusion model as an example, the process can be described as follows:

[0124] Data abstraction: First, the user data is abstracted and mapped to colors. In this process, each color represents a specific noise value.

[0125] Initial Image Construction: These colors (noise values) are then organized into an initial image of 512×512 pixels. This image acts as a random noise field and provides a starting point for the generation process.

[0126] Conditional encoding training: At the same time, the Stable Diffusion model is fed with trained conditional encodings, which can be text descriptions, style guides, or other types of conditions to guide the image generation process.

[0127] Iterative generation process: The Stable Diffusion model uses a diffusion process, starting from an initial noisy image, gradually removing noise and adding details, and this process is completed through multiple iterative steps.

[0128] Generate clear images: After iterative optimization of the model, clear and detailed images matching the conditional encoding are finally generated.

[0129] In the above way, the Stable Diffusion model is able to transform abstract user data into artworks with specific style and content, thereby realizing personalized AI creation.

[0130] In some embodiments,

[0131] Let's take the example of defining the abstract mapping of visual abstraction as color and combining it with AI through one-hot encoding. The following is a detailed description of combining prefixes, color mapping, position arrangement (P), one-hot encoding, and combining it with AI:

[0132] Prefix:

[0133] Each user data (such as target preset result information or a single data set in user basic information) is identified by a unique prefix, which consists of a level identifier (L), a preset result (PR), and a digital identifier (DID). For example, a user-selected data may be identified as L3_PR1_DID:201.

[0134] Color Mapping:

[0135] Each of the user’s target preset outcomes is associated with a specific color through a color mapping process. This mapping is based on the user’s emotional and psychological state, as well as their behavioral data.

[0136] Position ranking (P):

[0137] The color-mapped data is placed in specific locations according to its attributes. For example, on a canvas, different colors may represent different emotional states, and they are arranged in specific areas on the canvas to express the flow or change of an emotion.

[0138] One-Hot Encoding:

[0139] To further enhance the expressive power of AI, this implementation can use one-hot encoding to generate a unique numerical vector for each color. This vector is used as input to the AI ​​algorithm to help AI understand the independent characteristics of each color.

[0140] Noise Generation:

[0141] In the AI ​​process, controlled noise is introduced to simulate natural and random artistic effects. These noises are based on one-hot encoded numerical vectors, providing initial randomness for AI to create unique artistic patterns.

[0142] AI Technology:

[0143] This information can be transformed into visual art using AI models such as Stable Diffusion, which create images that reflect the user’s personality and emotional state based on the positional arrangement, mapping, and one-hot encoding of the colors, as well as the training provided.

[0144] Emotional resonance:

[0145] Through artistic and innovative expression, the user's abstract data is not only transformed into visual images, but also can trigger emotional resonance. Users can see the reflection of their emotions and personality in these works of art, which can ensure privacy and improve the user experience.

[0146] It should be noted that, in addition to converting visual abstract information into visual artworks in the above-mentioned manner, visual abstract information may also be converted into visual artworks in other manners, which are not limited to this.

[0147] It should be noted that traditional algorithms often only focus on a single dimension of user data and cannot fully capture the complexity and diversity of users. This embodiment provides a richer and more accurate analysis of user characteristics by comprehensively considering the user's behavior and feedback in multiple dimensions, so as to better understand user needs and preferences. In addition, in the related art, the direct use of user data may expose personal privacy and cause concerns about security and privacy. This embodiment uses abstraction technology, and the privacy data abstraction security protection comprehensive algorithm can perform analysis without leaking the original data, effectively protect user privacy, and enhance user trust in the platform. Moreover, the presentation of data in the related art is usually boring and abstract, which is not easy for users to understand and resonate emotionally. The method of this embodiment provides a novel and emotional way of expressing data by converting data into visual works of art, thereby enhancing the user's interactive experience.

[0148] In step S130, a user injection authorization mechanism may be configured for the data security module. In this embodiment, after the target preset result information and the user basic information are converted into abstract data in a standardized format, if the data injection authorization instruction is collected, social matching processing is performed based on the abstract data. If the data injection authorization instruction is not collected, the abstract data is saved. Among them, the data injection authorization instruction represents that the user to be collected agrees to use the abstract data for social matching processing. It can be understood that the electronic device may also include a social matching module, which may include a user trait insight and precise social matching system (Connect). The system can use complex data analysis technology to extract a multidimensional model of user personality and preferences from user behavior and feedback, and perform precise social matching based on Soul Quotient (SQ) and Soul Quotient Similarity (SQS) to promote deep social connections.

[0149] The user-injected authorization mechanism aims to provide a secure and transparent method for users to decide how their personal data is used. Through active user-injected authorization, it not only protects user privacy, but also improves data accuracy and user trust.

[0150] Definition of data injection: User data injection is a clear action through which users "inject" the abstract data they generate in the game (or other interactive platforms) into our algorithm, thereby activating the right to use the data.

[0151] In it, users interact in actual projects and generate corresponding abstract data in the background. Then, the generated abstract data is displayed to users. Users view their own data and decide whether to inject it into the algorithm of the user trait insight and precise social matching system (Connect). Users authorize the use of their data through a clear operation (such as clicking the "Confirm Injection" button). Once the user confirms the injection, the data will be officially authorized to the algorithm for subsequent social matching analysis.

[0152] In this embodiment, privacy control can be enhanced by setting up a user injection authorization mechanism. Users have full control over their own data and can choose whether and when to authorize the use of data. It can also ensure that all data used is voluntarily and accurately generated by the user, avoiding the impact of unauthorized use or erroneous data. In addition, through clear and voluntary injection steps, user trust in the platform is enhanced. In this embodiment, user data will not be used for any matching or analysis before injection. In addition, users can view, modify or revoke their data injection authorization at any time.

[0153] This embodiment respects and values ​​each user's right to control their own data. If the user chooses not to perform data infusion during the data infusion stage, the user's personal data will not be transmitted to our server. However, the data will still be updated and iterated as the user uses it. Even if the user initially chooses not to infuse data, they can still change their decision at any time and choose to infuse data later to further enhance the user experience.

[0154] In the process of social matching based on abstract data, the Lingxi value corresponding to the user to be collected can be determined based on the abstract data; wherein the Lingxi value is an indicator for quantifying the personalized characteristics of the user. Then, based on the Lingxi similarity between the Lingxi value of the user to be collected and the Lingxi values ​​of other users, the social matching process is performed on the user to be collected; wherein the Lingxi similarity represents the similarity of the personalized characteristics between the two users.

[0155] It should be noted that Soul Quotient (SQ) refers to a quantitative indicator used to capture and express the deep personalized characteristics of users in the project while ensuring the security and privacy of user data. Based on the abstract data, the Soul Quotient corresponding to the user to be collected can be determined in two cases. The first one can be obtained directly by analyzing the abstract data; the second one can be obtained indirectly through the abstract data, for example, by analyzing the visual abstract information corresponding to the abstract data. There is no limitation on this.

[0156] Soul Quotient Similarity (SQS) is based on the user's soul quotient value and is used to measure the similarity between two users at psychological and emotional levels. The higher the soul quotient similarity score, the more similar the two users are in personality and preference, which helps the platform to perform accurate social matching and promote deep social connections. For example, user B refers to a user group whose soul quotient value is suitable for user A, that is, their soul quotient similarity reaches the preset matching standard, and the system will recommend user B as a potential social partner of user A. In this embodiment, by calculating the soul quotient similarity between users, social partners who are in harmony with the user's mind can be recommended to promote more meaningful social connections. And the soul quotient value provides a standardized quantitative indicator, which makes the comparison of user traits intuitive and consistent, and helps to improve the accuracy of social matching.

[0157] In some embodiments,

[0158] The Lingxi value is calculated by weighted summing of the numerical identifiers in the abstract data.

[0159]

[0160] in:

[0161] RA is the Lingxi value of user A,

[0162] n is the total number of numeric identifiers of user A,

[0163] Wj is the weight of the jth numerical identifier. Given the complexity of human traits, not all numerical identifiers are equally important to the overall trait description of the user when calculating the Lingxi value. Therefore, a specific weight can be assigned to each numerical identifier.

[0164] Uj can be the sum of the values ​​of the jth value identifier group or the value of a single value identifier.

[0165] By comparing the Lingxi values ​​between users, the Lingxi similarity between the two users is determined:

[0166]

[0167] in:

[0168] SQS(A,B) represents the Lingxi similarity score between user A and user B.

[0169] RA and RB are the Lingxi values ​​of user A and user B respectively.

[0170] Max Possible Difference is the maximum possible difference in the Lingxi value.

[0171] In other embodiments,

[0172] The telepathic value is calculated through the abstract mapping in the visual abstract information.

[0173] Depending on the project requirements, the visual abstract information obtained after visually abstracting the abstract data may be different, such as color, light perception, sound, shape, texture, icon, etc. Each abstract mapping object in the visual abstract information can capture and express the user's private data from a unique perspective.

[0174] Take color as an example. First, key trait data such as the user's physiological information, color preferences, values, and zodiac signs can be collected. Then, according to pre-defined rules, these data are mapped to specific color values. In this way, a color block arrangement diagram with specific position coordinates and sequences can be obtained. This color block arrangement diagram is not just an image, it actually represents the user's telepathy value. Each color block corresponds to the user's characteristics or preferences in a certain dimension, and these data have been subjected to multiple abstract processing during the mapping process.

[0175] Then the telepathic similarity between users is determined by abstracting the mapping objects.

[0176] In this implementation, the powerful capabilities of deep learning algorithms can be used to directly perform in-depth comparison and analysis on the abstract mapping of user data, thereby accurately determining the similarities between users.

[0177] Take color as an example. The abstract mapping object is defined as a color block arrangement map with specific position coordinates and sequences. This color block arrangement map is not only a visual presentation, but also an intuitive mapping of the user's personality and preferences. Each color block carries key information about the user in a specific dimension, and this information together constitutes the user's unique telepathic value.

[0178] Furthermore, the information of these color block arrangement diagrams can be input into advanced deep learning models, such as convolutional neural networks (CNN). Utilizing the excellent performance of CNN in image recognition and pattern matching, it is possible to conduct a comprehensive comparative analysis of the color block arrangement diagrams of different users. Through this comparison, the similarity between users can be quantified, and then their spiritual similarity can be calculated.

[0179] The advantage of this implementation lies in its high accuracy and objectivity. Since the deep learning algorithm can automatically learn and extract key features in the color block arrangement diagram, it can capture the subtle differences and similarities between users more finely. In addition, by combining abstract mapping and deep learning, it not only protects the privacy of users, but also provides users with an innovative and personalized service experience.

[0180] In addition, in this implementation, privacy protection is effectively combined with deep learning algorithms such as convolutional neural networks (CNN), which can effectively process image data, provide personalized services, and enhance user experience, especially in the application of extended reality (XR) technology, which can provide a better user experience.

[0181] It should be noted that, in addition to the above-mentioned methods, social matching can also be performed in other ways, which are not limited to this.

[0182] This embodiment provides a kind of efficient data collection and feedback, strong privacy protection, accurate user trait analysis and social matching, and innovative data art expression, which can play an important role in the fields of data security, social matching and personalized expression, and provide users with a comprehensive and personalized digital experience and privacy protection. The algorithm system provides deep user insights and accurate social matching functions while realizing user data security protection through the combination of multiple data abstraction, deep learning and artistic expression through result preset and feedback mechanism.

[0183] In an exemplary embodiment, a data processing device is provided, which is applied to an electronic device, specifically, a privacy data abstraction and security protection algorithm system (PASPAS) in an electronic device. The device is used to implement the above data processing method. For example, refer to Figure 3 As shown, the device may include:

[0184] The data collection module 10 is used to collect data from the user to be collected based on the preset result information library, and obtain the target preset result information and user basic information of the user to be collected; wherein the preset result information library includes a plurality of original preset results, and the target preset result information includes a plurality of target preset results corresponding to the user to be collected;

[0185] A data security module 20, used to convert the target preset result information and the user basic information into abstract data in a standardized format;

[0186] The social matching module 40 is used to perform social matching processing based on the abstract data.

[0187] In an exemplary embodiment, a data processing device is provided, which is applied to an electronic device, referring to Figure 3 As shown, the device may include a social matching module 40, which may be used to:

[0188] Before performing social matching processing based on the abstracted data, a data injection authorization instruction is collected; wherein the data injection authorization instruction indicates that the user to be collected agrees to use the abstracted data for social matching processing;

[0189] or,

[0190] After the target preset result information and the user basic information are converted into abstract data in a standardized format, if the data injection authorization instruction is not collected, the abstract data is saved.

[0191] In an exemplary embodiment, a data processing device is provided, which is applied to an electronic device, referring to Figure 3 As shown, in the device, the social matching module 40 can be used to:

[0192] Based on the abstract data, determine the Lingxi value corresponding to the user to be collected; wherein the Lingxi value is an indicator used to quantify the user's personalized characteristics;

[0193] Based on the Lingxi similarity between the Lingxi value of the user to be collected and the Lingxi values ​​of other users, social matching processing is performed on the user to be collected; wherein the Lingxi similarity represents the similarity degree of personalized characteristics between two users.

[0194] In an exemplary embodiment, a data processing device is provided, which is applied to an electronic device, referring to Figure 3 As shown, the device may include an artistic module 30 .

[0195] The data security module 20 may be used to convert the target preset result information and the user basic information into abstract data in a standardized format, and then perform visual abstract mapping on the abstract data to obtain corresponding visual abstract information;

[0196] The art module 30 can be used to process the visual abstract information based on artificial intelligence technology to obtain a visual art work.

[0197] In an exemplary embodiment, a data processing device is provided, which is applied to an electronic device, wherein:

[0198] The abstracted data includes numerical identification information; and / or,

[0199] The visual abstract information includes at least one of the following: color information, light information, sound information, shape information, texture information, and icon information.

[0200] In an exemplary embodiment, a data processing device is provided, which is applied to an electronic device, referring to Figure 3 As shown, in the device, when the abstract data includes numerical identification information, and the visual abstract information includes color information, the data security module 20 can be used to:

[0201] Sorting the positions of the multiple numerical identifiers in the data identifier information;

[0202] The plurality of numerical identifiers after position sorting are subjected to color abstraction mapping to obtain the color information composed of a plurality of color identifiers; wherein the plurality of color identifiers correspond one to one to the plurality of numerical identifiers.

[0203] In an exemplary embodiment, a data processing device is provided for application to an electronic device, wherein a preset result information library is constructed using historical data of multiple dimensions, wherein the multiple dimensions include at least two of the following: psychological, physiological, cognitive, social, cultural, interactive, fun, and sensory.

[0204] In an exemplary embodiment, an electronic device is provided, which may be a notebook computer, a desktop computer, a server, a mobile phone or other electronic device, without limitation.

[0205] Among them, reference Figure 4 As shown, the electronic device 100 includes: at least one processor 101, a memory 102, at least one network interface 104 and a user interface 103. The various components in the electronic device 100 are coupled together through a bus system 105. It can be understood that the bus system 105 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 105 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity of the description, various buses are marked as bus systems 105 in the figure.

[0206] The user interface 103 may include a display, a keyboard, or a pointing electronic device (eg, a mouse, a trackball), a touch pad, or a touch screen.

[0207] It can be understood that the memory 102 in the embodiment of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 102 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0208] In some implementations, the memory 102 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system 1021 and application programs 1022 .

[0209] Among them, the operating system 1021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 1022 includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. The program that implements the method of the embodiment of the present application can be included in the application 1022.

[0210] In the embodiment of the present application, by calling the program or instructions stored in the memory 102, specifically, the program or instructions stored in the application 1022, the processor 101 is used to execute the method steps provided by each method embodiment.

[0211] The method disclosed in the above embodiment of the present application can be applied to the processor 101, or implemented by the processor 101. The processor 101 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 101 or the instruction in the form of software. The above processor 101 can be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to execute, or the hardware and software units in the decoding processor can be executed. The software unit can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 102, and the processor 101 reads the information in the memory 102 and completes the steps of the above method in combination with its hardware.

[0212] It is understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit may be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing electronic devices (DSPD), programmable logic electronic devices (PLD), field programmable gate arrays (FPGA), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions of the present application, or a combination thereof.

[0213] For software implementation, the technology of this article can be implemented by a unit that performs the functions of this article. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0214] The terminal provided in this embodiment can execute all the steps of the above-mentioned data processing method, thereby achieving the technical effect of the above-mentioned data processing method. Please refer to the relevant description of the above-mentioned data processing method for details. For the sake of concise description, it will not be repeated here.

[0215] The embodiment of the present application also provides a storage medium (computer-readable storage medium). The storage medium here stores one or more programs. The storage medium may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk or a solid-state drive; the memory may also include a combination of the above-mentioned types of memory.

[0216] When one or more programs in the storage medium can be executed by one or more processors, the above method executed on the electronic device side is implemented.

[0217] The processor is used to execute the program stored in the memory to implement the following steps of the method performed on the electronic device side.

[0218] The professionals should also be further aware that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with 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 this application.

[0219] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0220] It should be noted that the phrases "one implementation", "an embodiment", "an exemplary embodiment", "some embodiments", etc. mentioned in the specification indicate that the described embodiments may include certain features, structures or characteristics, but not every embodiment may include the certain features, structures or characteristics. In addition, such phrases do not necessarily refer to the same embodiment. In addition, when describing certain features, structures or characteristics in conjunction with an embodiment, it is within the knowledge of those skilled in the art to implement such features, structures or characteristics in conjunction with other embodiments, whether explicitly or not explicitly described.

[0221] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or electronic device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or electronic device. In the absence of more limitations, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or electronic device including the elements.

[0222] The above embodiments are only preferred embodiments for fully illustrating the present application, and the protection scope of the present application is not limited thereto. Any equivalent substitution or change made by a person skilled in the art based on the present application is within the protection scope of the present application.

Claims

1. A data processing method, characterized in that: The data processing method comprises: Based on the preset result information library, data is collected from the user to be collected, and target preset result information and user basic information of the user to be collected are obtained; wherein the preset result information library includes multiple original preset results, and the target preset result information includes multiple target preset results corresponding to the user to be collected; the user basic information refers to information that is not included in the preset result information library and represents the basic characteristics of the user; Converting the target preset result information and the user basic information into abstract data in a standardized format; wherein the abstract data is difficult to reverse engineer; the abstract data includes data identification information composed of multiple data identifiers, each data identifier corresponding to a target preset result or a basic feature in the user basic information; Performing social matching processing based on the abstracted data; Wherein, the data processing method includes: The result preset and feedback collection processing system based on the data collection module presets a plurality of the original preset results; wherein the original preset results at least include the user's behavioral feedback, emotional tendency, preference selection and potential needs; The data collection of the user to be collected is performed based on the preset result information library to obtain the target preset result information and user basic information of the user to be collected, including: Based on the interaction between the user to be collected and the data collection module, collect the personal data and the basic user information of the user to be collected; Obtaining the target preset result information corresponding to the personal data based on the preset result information database; Before performing social matching processing based on the abstracted data, the data processing method includes: A data injection authorization instruction is collected; wherein the data injection authorization instruction indicates that the user to be collected agrees to use the abstracted data for social matching processing; After converting the target preset result information and the user basic information into abstract data in a standardized format, the data processing method includes: If the data injection authorization instruction is not collected, the abstracted data is saved.

2. The data processing method according to claim 1, characterized in that: The social matching process based on the abstracted data includes: Based on the abstract data, determine the Lingxi value corresponding to the user to be collected; wherein the Lingxi value is an indicator for quantifying the personalized characteristics of the user; Based on the Lingxi similarity between the Lingxi value of the user to be collected and the Lingxi values ​​of other users, social matching processing is performed on the user to be collected; wherein the Lingxi similarity represents the similarity degree of personalized characteristics between two users.

3. The data processing method according to claim 1, characterized in that: After converting the target preset result information and the user basic information into abstract data in a standardized format, the data processing method includes: Performing visual abstraction mapping on the abstracted data to obtain corresponding visual abstraction information; The visual abstract information is processed based on artificial intelligence technology to obtain a visualized art work.

4. The data processing method according to claim 3, characterized in that: The visual abstract information includes at least one of the following: color information, light information, sound information, shape information, texture information, and icon information.

5. The data processing method according to claim 4, characterized in that: When the abstract data includes data identification information, and the visual abstract information includes color information, performing visual abstract processing on the abstract data to obtain corresponding visual abstract information includes: Sorting the positions of the multiple data identifiers in the data identifier information; The plurality of data identifiers after position sorting are subjected to color abstraction mapping to obtain the color information composed of a plurality of color identifiers; wherein the plurality of color identifiers correspond one-to-one to the plurality of data identifiers.

6. The data processing method according to any one of claims 1 to 5, characterized in that: The preset result information library is constructed through historical data of multiple dimensions, wherein the multiple dimensions include at least two of the following: psychological, physiological, cognitive, social, cultural, interactive, interesting and sensory.

7. A data processing device, characterized in that: The data processing device comprises: A data collection module is used to collect data from the user to be collected based on the preset result information library, and obtain the target preset result information and user basic information of the user to be collected; wherein the preset result information library includes multiple original preset results, and the target preset result information includes multiple target preset results corresponding to the user to be collected; the user basic information refers to information that is not included in the preset result information library and represents the basic characteristics of the user; A data security module, used to convert the target preset result information and the user basic information into abstract data in a standardized format; wherein the abstract data is difficult to reverse engineer; the abstract data includes data identification information composed of multiple data identifiers, each data identifier corresponding to a target preset result or a basic feature in the user basic information; A social matching module, used for performing social matching processing based on the abstracted data; Wherein, the data collection module is also used for: Presetting a plurality of the original preset results based on the result preset and feedback collection and processing system; wherein the original preset results at least include the user's behavioral feedback, emotional tendency, preference selection and potential needs; Based on the interaction between the user to be collected and the data collection module, collect the personal data and the basic user information of the user to be collected; Obtaining the target preset result information corresponding to the personal data based on the preset result information database; The social matching module is also used for: Before performing social matching processing based on the abstracted data, a data injection authorization instruction is collected; wherein the data injection authorization instruction indicates that the user to be collected agrees to use the abstracted data for social matching processing; After the target preset result information and the user basic information are converted into abstract data in a standardized format, if the data injection authorization instruction is not collected, the abstract data is saved.

8. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the data processing method as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the data processing method as described in any one of claims 1 to 6.

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