Cross-platform digital identity dynamic data bidirectional mapping method, device and medium

Abstract

A cross-platform digital identity dynamic data bidirectional mapping method, device and medium, by establishing a digital identity certificate with uniqueness and persistence, and establishing a connection with one of the multiple application system platforms to obtain the role data field name required by the platform role, then converting the identity data field name and identity data value into the corresponding role data value through the data conversion model, and adjusting the role data value based on the identity achievement data, further converting the role data value fed back by the platform into the identity data field name and adjustment data value after the platform role runs to update the identity data, and adding the platform achievement data as identity achievement data after data standardization processing, thereby achieving the technical effect of improving cross-platform identity integration and dynamic accumulation capability.

Description

Technical Field

[0001] This invention relates to the field of digital identity management technology, and in particular to a cross-platform digital identity dynamic data bidirectional mapping method, computer equipment, and computer-readable storage medium for digital identity establishment, identity data structure design, cross-platform role data conversion model, platform role feedback reverse mapping mechanism, and platform achievement data standardization processing technology. Background Technology

[0002] With the rapid development of digital services and applications, users often exist simultaneously on multiple application platforms, such as different gaming platforms, learning platforms, or social platforms. Currently, most platforms employ independent account systems and role creation mechanisms. The role data architecture, achievement systems, and scoring standards of each platform are independent, lacking a unified core identity architecture. Therefore, the skills, experience, or achievements accumulated by a user on one platform cannot be automatically transferred to other platforms, leading to the fragmentation of digital identity.

[0003] Furthermore, while some existing technologies provide third-party login mechanisms (such as single sign-on systems), these technologies only address authentication issues and do not further handle the semantic differences and numerical mapping of role data across platforms. In other words, existing technologies lack a mechanism capable of semantically aligning role field names with data structures across different platforms and dynamically converting and adjusting them through a data transformation model.

[0004] Therefore, how to establish a unique and persistent digital identity credential, and realize the bidirectional mapping between identity data and multi-platform role data through an intelligent data conversion model, while standardizing and trustworthy processing of platform achievement data, so that digital identity data can be accumulated, updated and managed in a consistent manner across platforms, is a technical problem that needs to be solved by existing technologies.

[0005] In summary, it is clear that existing technologies have long suffered from the problem of the inability to integrate and dynamically accumulate cross-platform identity data. Therefore, it is necessary to propose improved technical methods to solve this problem. Summary of the Invention

[0006] In view of the problem that existing technologies cannot integrate and dynamically accumulate cross-platform identity data, this invention discloses a cross-platform digital identity dynamic data bidirectional mapping method, a computer device, and a computer-readable storage medium thereof, wherein: This invention discloses a cross-platform method for bidirectional mapping of dynamic digital identity data, which involves performing the following steps using a computer device: Establish unique and persistent digital identity credentials, along with multiple identity data field names and values ​​associated with the digital identity credentials, as well as multiple identity achievement data. Establish a connection with one of multiple application system platforms, obtaining multiple role data field names required by the application system platform for establishing platform roles through the platform's communication interface. Use a data transformation model to convert the multiple identity data field names and values ​​into role data values ​​for the multiple role data field names, and then positively adjust the role data values ​​for the multiple role data field names based on the multiple identity achievement data. Provide the transformed and adjusted role data field names and values ​​to the application system platform to establish platform roles. When receiving feedback on the role data values ​​for the multiple role data field names from the corresponding application system platform through the platform communication interface, use the data transformation model to convert the feedback role data values ​​for the multiple role data field names into multiple identity data field names and adjusted data values, and then adjust the multiple identity data field names and identity data values ​​based on the multiple identity data field names and adjusted data values. Finally, perform data standardization processing on the platform achievement data received from the application system platform to add the processed platform achievement data as identity achievement data to the digital identity credentials.

[0007] In one embodiment of the present invention, the method further includes converting multiple identity data field names and identity data values ​​into role data values ​​of multiple role data field names through a data conversion model, then converting multiple identity achievement data into empowerment directives, providing the converted role data values ​​of multiple role data field names and empowerment directives to the application system platform to establish platform roles based on the converted role data values ​​of multiple role data field names, and then positively adjusting the role data values ​​of the platform roles according to the empowerment directives.

[0008] In one embodiment of the present invention, the data conversion model further includes setting weighting coefficients for converting multiple identity data field names and identity data values ​​into role data values ​​of multiple role data field names based on the program type, usage context tags, and historical interaction frequency of each application system platform, and then using the weighting coefficients to convert multiple identity data field names and identity data values ​​into role data values ​​of multiple role data field names.

[0009] In one embodiment of the present invention, the method further includes calculating a credibility index for the platform achievement data obtained from the platform role feedback obtained from the self-application system platform using a trust scoring model. When the credibility index is greater than or equal to a credibility threshold, the platform achievement data obtained from the platform role feedback obtained from the self-application system platform is then subjected to data standardization processing so that the platform achievement data after digital identity credential addition processing is used as identity achievement data.

[0010] In one embodiment of the present invention, the data transformation model has a semantic embedding layer and a field alignment matrix, which converts the role data field names of different application system platforms into vector representations in a unified semantic space, and then dynamically generates field mapping relationships based on vector similarity.

[0011] In one embodiment of the present invention, the method further includes performing outlier analysis and behavior pattern comparison on the role data values ​​of multiple role data field names obtained from the platform role feedback by the self-application system platform using an anomaly detection model. When it is determined that the role data values ​​of multiple role data field names in the platform role feedback are abnormal values, the conversion between multiple identity data field names and adjustment data values ​​will be terminated and an alert will be generated and issued.

[0012] This invention discloses a computer device, which includes: The storage device stores multiple computer-readable instructions; and One or more hardware processors are electrically connected to a storage device and execute multiple computer-readable instructions to enable a computer device to implement the cross-platform dynamic data bidirectional mapping method for digital identity as described above.

[0013] This invention discloses a computer-readable recording medium storing a computer program that, when executed by one or more hardware processors of a computer device, enables the computer device to perform a cross-platform dynamic bidirectional mapping method for digital identity data.

[0014] The method, computer equipment, and computer-readable storage medium disclosed in this invention are as described above. By establishing a unique and persistent digital identity credential and establishing a connection with one of multiple application system platforms to obtain the role data field names required by the platform role, the identity data field names and identity data values ​​are converted into corresponding role data values ​​through a data conversion model, and the role data values ​​are positively adjusted based on identity achievement data. Furthermore, after the platform role is running, the role data values ​​fed back by the platform are reversely converted into identity data field names and adjusted data values ​​to update the identity data, and the platform achievement data is added as identity achievement data after data standardization processing.

[0015] Through the aforementioned technical means, this invention can achieve the technical effect of enhancing cross-platform identity integration and dynamic accumulation capabilities. Attached Figure Description

[0016] Figure 1 The diagram illustrates the method flowchart of the cross-platform digital identity dynamic data bidirectional mapping method of the present invention.

[0017] Figure 2The illustration is a data conversion diagram illustrating the bidirectional mapping of dynamic digital identity data across platforms according to the present invention.

[0018] Figure 3 The diagram illustrates the computer system architecture for the cross-platform dynamic data bidirectional mapping of digital identity according to the present invention.

[0019] Explanation of reference numerals in the attached figures: Step 101: Establish a unique and persistent digital identity credential, along with multiple identity data field names and values ​​associated with the digital identity credential, and multiple identity achievement data. Step 102: Establish a connection with one of the multiple application system platforms, and obtain the names of multiple role data fields required by the application system platform to establish platform roles through the platform communication interface. Step 103: Using a data transformation model, convert multiple identity data field names and identity data values ​​into role data values ​​for multiple role data field names. Then, based on multiple identity achievement data, positively adjust the role data values ​​for multiple role data field names. Step 104: Provide the transformed and adjusted role data field names and their corresponding role data values ​​to the application system platform to establish platform roles. Step 105: When the role data values ​​of multiple role data field names are obtained from the corresponding application system platform through the platform communication interface, the role data values ​​of the multiple role data field names are converted into multiple identity data field names and adjustment data values ​​through the data transformation model. Then, the multiple identity data field names and identity data values ​​are adjusted based on the multiple identity data field names and adjustment data values. Step 106: Standardize the platform achievement data obtained from the platform role feedback obtained from the self-application system platform to use the platform achievement data after digital identity credential addition as identity achievement data. 20: Identity Data 21: Strength Value 22: Agility value 23: Intelligence Value 30: First Application System Platform 31: Attack Power 31: Defense 40: First Application System Platform 41: Knowledge Mastery 42: Problem Solving Index 43: Learning Efficiency Value 501: CPU 502: ROM 503: RAM 504: Bus 505: I / O Interface 506: Input section 507: Output Section 508: Storage Section 509: Communications Section 510: Driver 511: Removable media Detailed Implementation The following will describe in detail the implementation of the present invention with reference to the accompanying drawings and embodiments, so as to fully understand how the present invention uses technical means to solve technical problems and achieve technical effects and to implement it accordingly.

[0020] The following section will first explain the cross-platform digital identity dynamic data bidirectional mapping method disclosed in this invention, and please refer to [reference needed]. Figure 1 As shown, Figure 1 The diagram illustrates the method flowchart of the cross-platform digital identity dynamic data bidirectional mapping method of the present invention.

[0021] This invention discloses a cross-platform method for bidirectional mapping of dynamic digital identity data, which involves performing the following steps using a computer device: Establish a unique and persistent digital identity credential, along with multiple identity data field names and values ​​associated with the digital identity credential, and multiple identity achievement data (step 101); establish a connection with one of multiple application system platforms, and obtain the names of multiple role data fields required by the application system platform for establishing platform roles through the platform communication interface (step 102); convert the multiple identity data field names and values ​​into role data values ​​for the multiple role data field names using a data transformation model, and then positively adjust the role data values ​​for the multiple role data field names based on the multiple identity achievement data (step 103); provide the converted and adjusted multiple role data field names... The role data value is sent to the application system platform to establish the platform role (step 104); when the role data value of multiple role data field names is obtained from the corresponding application system platform through the platform communication interface, the role data value of multiple role data field names is converted into multiple identity data field names and adjustment data values ​​through the data conversion model, and then the multiple identity data field names and identity data values ​​are adjusted based on the multiple identity data field names and adjustment data values ​​(step 105); and the platform achievement data obtained from the application system platform is processed for data standardization so that the platform achievement data after the digital identity credential addition processing is the identity achievement data (step 106).

[0022] Computer devices establish unique and persistent digital identity credentials, as well as multiple identity data field names and values ​​associated with the digital identity credentials, and multiple identity achievement data. The aforementioned uniqueness is achieved by the digital identity credentials through a globally unique identification code generation mechanism. The computer device generates digital identity credentials by combining timestamps, computer device identification codes, registration serial numbers, and random random number seeds through hash operations (such as the SHA-256 algorithm), making each digital identity credential unique within the computer device.

[0023] It is worth noting that while establishing digital identity credentials on a computer device, a set of asymmetric encryption key pairs can also be generated simultaneously. The public key serves as the identity identifier for external verification, while the private key is stored in a secure storage area (e.g., secure elements and trusted platforms, etc., which are only examples and do not limit the scope of application of this invention) to provide security for the digital identity credentials.

[0024] The persistence of digital identity credentials means that they maintain the same core identification basis across different application system platforms (such as game system platforms, learning system platforms, social interaction system platforms, and other digital service system platforms that can create virtual characters or platform characters, etc., which are only examples and are not intended to limit the application scope of this invention), different devices, and different usage scenarios. The core identification basis does not change due to the switching of application system platforms or the re-establishment of characters. It is worth noting that the persistence of digital identity credentials also includes state accumulation, that is, the identity data field names, identity data values, and identity achievement data associated with the digital identity credentials can be continuously updated and evolved over time, and will not disappear due to the termination or deletion of the platform role of a single application system platform.

[0025] The aforementioned identity data field names include, but are not limited to, basic attributes (e.g., strength, agility, intelligence, health, energy, etc.), rare attributes (e.g., luck, charm, etc.), identity level, etc. Each identity data field records a corresponding identity data value. The aforementioned identity achievement data includes, but is not limited to, achievement rarity, achievement completion, achievement completion rate, etc. from different application system platforms. These are merely illustrative examples and do not limit the application scope of this invention. The aforementioned achievement rarity can represent the difficulty or rarity of obtaining the achievement among all users or users in the same group. The achievement completion rate can represent the stage-based achievement level (e.g., 0% to 100% or 1 to 5 star levels, which are merely illustrative examples and do not limit the application scope of this invention). The achievement completion rate can represent the proportion of achieving this type of achievement goal within a specific period or a specific set of tasks.

[0026] The aforementioned identity data field names, identity data values, and identity achievement data are stored in a structured data format within the digital identity credential associated data structure, enabling the digital identity credential to have long-term cumulative durability. In addition, the identity data field names, identity data values, and identity achievement data can be further recorded through an update log mechanism to ensure their change history, thus giving the digital identity credential traceability.

[0027] The computer device establishes a connection with one of multiple application system platforms. Through the platform's communication interface, it obtains the names of multiple role data fields required by the application system platform to establish platform roles. The computer device establishes a secure connection with the target application system platform through a communication module (e.g., network interface card and wireless communication module, etc.). This connection can be based on HTTPS, WebSocket, or other secure transmission protocols. Authentication and authorization are performed through the public and private key mechanism corresponding to the digital identity credentials. After authentication is completed, the application system platform provides the names of multiple role data fields required to establish platform roles to the computer device through the platform's communication interface.

[0028] The aforementioned platform communication interface can be the application programming interface (API) provided by the application system platform. This API contains role schema definition information, which includes multiple role data field names, field types, field data formats, etc. This is only an example and is not intended to limit the scope of application of this invention.

[0029] Specifically, assuming the application system platform is a game platform, the names of its character data fields include, but are not limited to, character level, attack power, defense power, health points, and skill mastery, etc.; assuming the application system platform is a learning platform, the names of its character data fields include, but are not limited to, learning level, number of completed courses, domain tag, and assessment score, etc. These are merely illustrative examples and are not intended to limit the scope of application of this invention.

[0030] In one implementation, the application system platform can provide a role data model in a structured data format (e.g., JSON or XML, etc., which are merely examples and do not limit the scope of application of the present invention), enabling the computer device to obtain a complete set of role data field names. The multiple role data field names are then stored in a temporary data area, and an initial structure of a Role Field Mapping Table is established for subsequent data conversion models to perform semantic alignment and mapping between identity data fields and role data fields.

[0031] In one implementation, the platform communication interface further provides a Role Type Identifier and a Usage Context Tag, enabling the computer device to adjust the field parsing method according to different role types (e.g., competitive roles, educational roles, social roles, etc., which are only examples and are not intended to limit the application scope of the present invention). That is, the computer device can first parse the semantic attributes of the role field, and then hand it over to the semantic embedding layer of the data conversion model and the field alignment matrix for field semantic standardization processing to facilitate subsequent data mapping operations.

[0032] In addition, computer devices can also establish a platform field index for the role data field names provided by different application system platforms, so that the same digital identity credential can dynamically obtain the role field structure required by the corresponding platform under the role creation requirements of different platforms, without having to hardcode fixed field names in advance.

[0033] Once the computer device has obtained the names of multiple role data fields provided by the application system platform, the computer device uses a data transformation model to map and transform the names of multiple identity data fields and identity data values ​​into role data values ​​that conform to the role data field names of the target application system platform. The aforementioned data transformation model includes a semantic embedding layer and a field alignment matrix to convert the names of multiple identity data fields associated with the digital identity credential and their corresponding identity data values ​​into role data values ​​that conform to the role data field names required by the target application system platform.

[0034] Specifically, the data conversion model first converts the identity data field names and role data field names into vector representations in a unified semantic space, and then establishes field correspondence through vector similarity calculation. For example, when the identity data field name is "strength" and the role data field name is "attack power", the data conversion model determines that the two are highly related fields through semantic similarity and establishes a corresponding mapping. This is only an example and is not intended to limit the application scope of the present invention.

[0035] The data transformation model uses pre-learned mapping parameters to convert multiple identity data field names and identity data values ​​into multiple role data field names and role data values. For example, the data transformation model uses linear transformation, scaling, and non-linear function transformation to convert multiple identity data field names and identity data values ​​into multiple role data field names and role data values.

[0036] Please refer to Figure 2 As shown, Figure 2 The illustration is a data conversion diagram illustrating the bidirectional mapping of dynamic digital identity data across platforms according to the present invention. Specifically, assuming that the identity data 20 in the digital identity credential contains a strength value 21 of 80, an agility value 22 of 65, and an intelligence value 23 of 70, and the character data field name of the first application system platform 30 contains an attack power of 31 and a defense power of 32, the data conversion model linearly converts the strength value 21 of 80 and the agility value 22 of 65 to an attack power of 31 of 75.5, i.e., attack power = 0.7 (mapping parameter corresponding to strength value 21) × 80 (strength value 21) + 0.3 (mapping parameter corresponding to agility value 22) × 65 (agility value 22); the data conversion model linearly converts the agility value 22 of 65 and the intelligence value 23 of 70 to a defense power of 32 of 67, i.e., defense power = 0.6 (mapping parameter corresponding to agility value 22) × 65 (agility value 22) + 0.4 (mapping parameter corresponding to intelligence value 23) × 70 (intelligence value 23). This is only an example for illustration and does not limit the application scope of the present invention.

[0037] The computer equipment then positively adjusts the character data values ​​of multiple character data field names based on multiple identity achievement data. The data transformation model can convert identity achievement data into gain coefficients to positively enhance the character data values ​​converted into multiple character data field names.

[0038] Specifically, assuming that the digital identity credential contains achievement rarity, achievement completion rate, and achievement completion percentage, the data conversion model first standardizes these metrics to convert them into calculable numerical features. Achievement rarity is converted into a rarity score R of 0 to 1 based on the application system platform's statistical data (e.g., Legendary level is 1, Epic level is 0.8, Rare level is 0.5, and Common level is 0.2; these are merely illustrative examples and do not limit the scope of application of this invention). Achievement completion rate can be converted into a completion score D of 0 to 1 (e.g., 80% completion results in D of 0.8, and 70% completion results in D of 0.7; these are merely illustrative examples and do not limit the scope of application of this invention). Achievement completion percentage can be converted into a completion rate score C of 0 to 1 (e.g., a completion rate of 60% during a fixed period results in C of 0.6, and a completion rate of 50% during a fixed period results in C of 0.5; these are merely illustrative examples and do not limit the scope of application of this invention).

[0039] The data conversion model integrates the rarity score R, completion score D, and completion rate score C into a boost coefficient (β). The boost coefficient is calculated, for example, by the following formula: β = 1 + (w_r × R) + (w_d × D) + (w_c × C), where w_r, w_d, and w_c are the weight values ​​corresponding to the rarity score, completion score, and completion rate score, respectively. The weight values ​​corresponding to the rarity score, completion score, and completion rate score can be dynamically adjusted according to the application system platform type. For example, in a competitive game platform, w_r can be set higher to highlight the value of rare achievements, while in a learning platform, w_c or w_d can be increased to reflect the ability to consistently complete courses or tasks. This is only an example and does not limit the application scope of the present invention. It is worth noting that, in order to avoid excessive boost causing character numerical imbalance, the data conversion model can further set an upper limit value for the boost coefficient.

[0040] In a specific embodiment, it is assumed that the identity achievement data are "Legendary Rarity (R=1.0)", "Completion Rate 90% (D=0.9)" and "Completion Percentage 50% (C=0.5)", and the application system platform weight values ​​are set to w_r=0.12, w_d=0.08 and w_c=0.05 respectively, with a gain coefficient β=1+ (0.12×1.0)+(0.08×0.9)+(0.05×0.5)=1.217, the character data field name's attack power 31 is 75.5, then the positively adjusted character data value attack power 31 is attack power×β=75.5×1.217=91.88, the character data field name's defense power 32 is 67, then the positively adjusted character data value defense power 31 is defense power×β=67×1.217=81.54. This is only an example for illustration and does not limit the application scope of the present invention.

[0041] In another implementation, the gain coefficient can also be designed in a segmented or threshold manner to improve stability. For example, when the achievement rarity R is higher than the rarity threshold R_th and the completion rate D is higher than the completion rate threshold D_th, the additional gain term Δβ is activated, that is, β=1+(w_r×R)+(w_d×D)+(w_c×C)+Δβ; where Δβ can be used to strengthen the positive reflection of the character's ability by the achievement of "high rarity and high completion". When the achievement completion rate C is lower than the completion rate threshold C_th, the data conversion model can reduce w_c or directly apply a reduction factor to β to reflect the situation that the achievement is rare but the user's retention is insufficient.

[0042] In another implementation, the data conversion model can be converted into empowerment directives based on multiple identity achievement data. The empowerment directive computer device then provides the converted role data values ​​of multiple role data field names and the empowerment directives to the application system platform. The application system platform establishes platform roles based on the role data values ​​of the converted multiple role data field names, and then adjusts the role data values ​​of the platform roles in a positive direction according to the empowerment directives.

[0043] In another implementation, the data transformation model can dynamically adjust or set weighting coefficients based on the application system platform's program type, usage context tags, and historical interaction frequency, so that the same set of identity data values ​​presents different mapping results in different application system platforms. For example, in a competitive platform, the weight of attack attributes is strengthened, while in a learning platform, the mapping weight of attributes related to professional knowledge and assessment performance is increased.

[0044] After the computer equipment converts and adjusts the role data values ​​of the multiple role data field names, the computer equipment then provides the converted and adjusted role data values ​​of the multiple role data field names to the application system platform to establish platform roles.

[0045] When a computer device obtains the role data values ​​of multiple role data field names from the corresponding application system platform through the platform communication interface, it converts the role data values ​​of the multiple role data field names into multiple identity data field names and adjustment data values ​​through a data conversion model, and then adjusts the multiple identity data field names and identity data values ​​based on the multiple identity data field names and adjustment data values.

[0046] Specifically, the data conversion model uses the previously established field alignment matrix and the semantic vector relationship generated by the semantic embedding layer to determine the correspondence between the role data field names and the identity data field names. That is, when the role data returned by the application system platform contains "attack power is 120" and "defense power is 85", the data conversion model first determines through semantic similarity matching that the attack power corresponds to the combination of the identity data fields "strength" and "agility", while the defense power corresponds to the combination of "agility" and "intelligence".

[0047] By using inverse matrix operations or least squares estimation, the changes in character data ΔAttack Power and ΔDefense Power are allocated to corresponding identity data adjustment values. Specifically, if Attack Power 31 increases from 91.88 to 111.88, the increase is 20, and proportionally allocated as follows: Strength adjustment value = 20 × 0.7 = 14, Agility adjustment value = 20 × 0.3 = 6. The Strength value 21 of identity data 20 in the digital identity certificate will be adjusted from 80 to 94 (increased by the Strength adjustment value 14), and the Agility value 22 of identity data 20 in the digital identity certificate will be adjusted from 65 to 71 (increased by the Agility adjustment value 6). Similarly, if Defense Power 32... The value is increased from 81.54 to 91.54, an increase of 10. The proportional distribution is as follows: Agility adjustment value = 10 × 0.6 = 6, Intelligence adjustment value = 10 × 0.4 = 4. The Agility value 22 of identity data 20 in the digital identity certificate will be adjusted from 65 to 71 by the Agility adjustment value 6. The Intelligence value 23 of identity data 20 in the digital identity certificate will be adjusted from 70 to 74 by the Intelligence adjustment value 4. It is worth noting that multiple role data fields affect the same identity data field at the same time. In this case, the data conversion model can sum up, weight average, or integrate all relevant adjustment values ​​into the final adjustment data value through optimization algorithms.

[0048] In another implementation, the data transformation model can further introduce a Decay Factor or a Confidence Weight to avoid excessive impact of short-term platform fluctuations on core identity data. Specifically, the data transformation model can set the identity data update formula as: New identity data value = Original identity data value + (α × Adjusted data value), where α is a learning rate parameter between 0 and 1 to control the magnitude of identity data updates. In addition, when the application system platform is a high-confidence platform or the usage scenario is a long-term cumulative task, the α value can be higher; otherwise, it can be lower.

[0049] It is worth noting that before updating the identity data values ​​of multiple identity data field names, the computer device can use an anomaly detection model to perform outlier analysis and behavior pattern comparison on the role data values ​​of multiple role data field names. When it is determined that the change in role data value exceeds a reasonable range (e.g., exceeding three standard deviations of the historical average, which is only an example and does not limit the application scope of this invention), the conversion between multiple identity data field names and adjusted data values ​​will be terminated and an alert will be generated and issued to ensure the data stability and security of digital identity credentials.

[0050] After the reverse conversion and adjustment are completed, the computer equipment writes the updated names and values ​​of multiple identity data fields into the digital identity credential associated data structure, and records the timestamp of the change, the source platform identification code and the reason for the change through the update log mechanism, so that the evolution of identity data is traceable.

[0051] When the role data field names of the second application system platform 40 include knowledge mastery 41, problem-solving index 42, and learning efficiency value 43, the data transformation model linearly transforms the intelligence value 23 (70), agility value 22 (71), and strength value 21 (94) into a knowledge mastery 41 of 72.7. That is, knowledge mastery = 0.6 (mapping parameter corresponding to intelligence value 23) × 70 (intelligence value 23) + 0.3 (mapping parameter corresponding to agility value 22) × 71 (agility value 22) + 0.1 (mapping parameter corresponding to strength value 21) × 94 (strength value 21). The data transformation model linearly transforms the intelligence value 23 (70), agility value 22 (71), and strength value 21 (94) into a problem-solving index 42 of 77.4. =0.5 (mapping parameter corresponding to Intelligence value 23) × 70 (Intelligence value 23) + 0.3 (mapping parameter corresponding to Strength value 21) × 94 (Strength value 21) + 0.2 (mapping parameter corresponding to Agility value 22) × 71 (Agility value 22); The data conversion model linearly converts Intelligence value 23 (70), Agility value 22 (71), and Strength value 21 (94) to a learning efficiency value 43 (75.3), i.e., Learning efficiency value = 0.5 (mapping parameter corresponding to Agility value 22) × 71 (Agility value 22) + 0.3 (mapping parameter corresponding to Intelligence value 23) × 70 (Intelligence value 23) + 0.2 (mapping parameter corresponding to Strength value 21) × 94 (Strength value 21). This is only an example and is not intended to limit the application scope of the present invention.

[0052] The knowledge mastery level of the character data value in the character data field name is 72.7. Therefore, the positively adjusted knowledge mastery level of the character data value 41 is knowledge mastery level × β = 72.7 × 1.217 = 88.48. The problem-solving index of the character data value in the character data field name is 77.4. Therefore, the positively adjusted problem-solving index of the character data value 42 is problem-solving index × β = 77.4 × 1.217 = 94.20. The learning efficiency value of the character data value in the character data field name is 75.3. Therefore, the positively adjusted learning efficiency value of the character data value 42 is learning efficiency value × β = 75.3 × 1.217 = 91.64. This is only an example for illustration and does not limit the application scope of the present invention.

[0053] Computer equipment performs data standardization processing on platform achievement data obtained from platform role feedback from application system platforms to add platform achievement data after digital identity credential processing as identity achievement data. Since different application system platforms have different achievement classification, grading system and calculation standards, directly writing it into digital identity credential may lead to inconsistent achievement measurement standards across platforms. Therefore, computer equipment needs to perform data standardization processing on the platform achievement data.

[0054] The computer device first performs structured parsing on the platform achievement data, converting it into a unified intermediate representation data structure. Then, the computer device uses a standardized transformation model to apply a unified scoring scale to the achievement standards of different application system platforms. For example, if a game system platform uses "Bronze, Silver, Gold, and Platinum" to represent achievement levels, while another game system platform uses "1 star to 5 stars" to represent achievement levels, the standardized model can convert them into a standardized rare score R_n of 0 to 1 based on a pre-established lookup table or through statistical distribution transformation methods (e.g., percentile mapping).

[0055] Similarly, achievement completion and completion rate can be standardized through scaling transformation. For example, if the completion of one application system platform is expressed as a percentage (e.g., 0% to 100%), and another application system platform is expressed as a level range (e.g., beginner, intermediate, and advanced), then the standardized model can be converted into a standardized completion score D_n through a level-correspondence score table; the completion rate can be calculated based on the number of historical completed tasks and the total number of tasks, and then standardized to a completion rate score C_n of 0 to 1 over a fixed period.

[0056] In one implementation, the computer device further calculates a credibility index for the platform achievement data through a trust scoring model. The credibility index can be calculated based on factors such as platform reputation weight, consistency of user historical behavior, anomaly detection results, and data source verification mechanism. When the credibility index is greater than or equal to a preset credibility threshold, the application system platform achievement data is allowed to enter the standardization process and be written into digital identity credentials; if it is lower than the threshold, the writing can be temporarily suspended or marked as pending verification.

[0057] The processed platform achievement data is used as the newly added identity achievement data and written into the digital identity credential associated data structure. The computer device also records the addition time, source platform and standardized version information through the update log mechanism to ensure that the digital identity credential has traceability and version consistency.

[0058] In another implementation, the computer device can aggregate similar achievements from multiple platforms, for example, by weighting the rarity score and completion score of the same type of achievement and selecting the maximum value to form a comprehensive identity achievement index, thus avoiding double counting or achievement inflation.

[0059] An embodiment of the present invention also provides a computer device, the computer device comprising: Storage device, storing multiple computer-readable instructions; and One or more hardware processors, electrically connected to a storage device, execute multiple computer-readable instructions to enable a computer device to implement the above-described cross-platform dynamic data bidirectional mapping method for digital identity.

[0060] Embodiments of this application also provide a computer-readable recording medium storing a computer program that, when executed by one or more hardware processors of a computer device, causes the computer device to perform the cross-platform digital identity dynamic data bidirectional mapping method described above. This computer-readable recording medium may be included in the computer device described in the above embodiments, or it may exist independently and not incorporated into the computer device.

[0061] Please refer to Figure 3 As shown, Figure 3 The diagram illustrates the computer system architecture for the cross-platform dynamic data bidirectional mapping of digital identity according to the present invention. It should be noted that... Figure 3 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0062] like Figure 3 As shown, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 502 or programs loaded from storage portion 508 into Random Access Memory (RAM) 503. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An Input / Output (I / O) interface 505 is also connected to the bus 504.

[0063] The following components are connected to I / O interface 505: input section 506 including keyboard, mouse, etc.; output section 507 including cathode ray tube (CRT), liquid crystal display (LCD), and speakers, etc.; storage section 508 including hard disk, etc.; and communication section 509 including network interface card such as LAN (Local Area Network) card, modem, etc. Communication section 509 performs communication processing via a network such as the Internet. Drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0064] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs various functions defined in the system of the present invention.

[0065] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), electronically erasable programmable read-only memory (EPROM), flash memory, optical fiber, compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband frequency or as part of a carrier wave, wherein a computer-readable computer program is carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0066] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0067] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of the present invention.

[0068] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the cross-platform digital identity dynamic data bidirectional mapping method as described above. This computer-readable storage medium may be included in the computer device described in the above embodiments, or it may exist independently and not assembled into the computer device.

[0069] Embodiments of the present invention also provide a computer program product or computer program including computer-readable instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the cross-platform dynamic data bidirectional mapping method for digital identity provided in the various embodiments described above.

[0070] In summary, by establishing unique and persistent digital identity credentials and connecting them with one of multiple application system platforms to obtain the role data field names required by the platform roles, a data conversion model is used to convert the identity data field names and identity data values ​​into corresponding role data values. Based on identity achievement data, the role data values ​​are positively adjusted. Furthermore, after the platform roles are running, the role data values ​​fed back by the platform are reversed and converted into identity data field names and adjusted data values ​​to update the identity data. Finally, the platform achievement data is standardized and added as identity achievement data.

[0071] This technology can solve the problem of existing technologies being unable to integrate and dynamically accumulate cross-platform identity data, thereby achieving the technical effect of improving the ability to integrate and dynamically accumulate cross-platform identity data.

[0072] While the embodiments disclosed in this invention are as described above, the content is not intended to directly limit the scope of patent protection for this invention. Anyone skilled in the art to which this invention pertains may make minor modifications in form and detail without departing from the spirit and scope disclosed herein. The scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A cross-platform method for bidirectional mapping of dynamic digital identity data, comprising the following steps performed via computer equipment: Establish a unique and persistent digital identity credential, as well as multiple identity data field names and values ​​associated with the digital identity credential, and multiple identity achievement data; Establish a connection with one of multiple application system platforms, and obtain the names of multiple role data fields required by the application system platform to establish platform roles through the platform communication interface; The data conversion model converts the names and values ​​of multiple identity data fields into the values ​​of multiple role data field names, and then positively adjusts the values ​​of multiple role data field names based on the multiple identity achievement data. Provide the transformed and adjusted role data field names of the role data to the application system platform to establish the platform roles; When the role data values ​​of multiple role data field names are obtained from the corresponding application system platform through the platform communication interface, the role data values ​​of multiple role data field names are converted into multiple identity data field names and adjustment data values ​​through the data conversion model, and then the multiple identity data field names and identity data values ​​are adjusted based on the multiple identity data field names and adjustment data values. and The platform achievement data obtained from the platform role feedback obtained from the application system platform is processed into platform achievement data after the digital identity credential is added, and this processed platform achievement data is used as the identity achievement data.

2. The cross-platform digital identity dynamic data bidirectional mapping method as described in claim 1, further comprising converting multiple identity data field names and identity data values ​​into role data values ​​of multiple role data field names through the data conversion model, then converting multiple identity achievement data into empowerment instructions, providing the converted role data values ​​of multiple role data field names and the empowerment instructions to the application system platform to establish the platform role based on the converted role data values ​​of multiple role data field names, and then positively adjusting the role data values ​​of the platform role according to the empowerment instructions.

3. The cross-platform digital identity dynamic data bidirectional mapping method as described in claim 1, wherein the data conversion model further includes setting a weighting coefficient for converting multiple identity data field names and identity data values ​​into role data values ​​of multiple role data field names based on the program type, usage context tag, and historical interaction frequency of each application system platform, and then using the weighting coefficient to convert multiple identity data field names and identity data values ​​into role data values ​​of multiple role data field names.

4. The cross-platform digital identity dynamic data bidirectional mapping method as described in claim 1, further comprising calculating a credibility index for the platform achievement data obtained from the platform role feedback obtained from the application system platform through a trust scoring model; when the credibility index is greater than or equal to a credibility threshold, performing data standardization processing on the platform achievement data obtained from the platform role feedback obtained from the application system platform to obtain the platform achievement data after digital identity credential addition processing as the identity achievement data.

5. The cross-platform digital identity dynamic data bidirectional mapping method as described in claim 1, wherein the data conversion model has a semantic embedding layer and a field alignment matrix, converts the role data field names of different application system platforms into vector representations in a unified semantic space, and then dynamically generates field mapping relationships based on vector similarity.

6. The cross-platform digital identity dynamic data bidirectional mapping method as described in claim 1, further comprising performing outlier analysis and behavior pattern comparison on the role data values ​​of multiple role data field names obtained from the application system platform through an anomaly detection model; when it is determined that the role data values ​​of multiple role data field names obtained from the platform role are abnormal values, the conversion between multiple identity data field names and adjusted data values ​​will be terminated and an alert will be generated and issued.

7. A computer device, the computer device comprising: Storage device, storing multiple computer-readable instructions; and One or more hardware processors, electrically connected to the storage device, execute the plurality of computer-readable instructions to enable the computer device to implement the cross-platform dynamic data bidirectional mapping method for digital identity as described in any one of claims 1 to 6.

8. A computer-readable recording medium having a computer program stored thereon, which, when executed by one or more hardware processors of a computer device, causes the computer device to perform the cross-platform dynamic data bidirectional mapping method for digital identity as described in any one of claims 1 to 6.

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