A method and system for constructing a personal data pedigree based on a light guide film

Abstract

This invention discloses a method and system for constructing a personal data lineage map based on a photoconductive film. It establishes a lineage map of personal data generated by users in multiple application scenarios such as health monitoring, security protection, social services, virtual worlds, and smart homes through a unified identity authentication center. The system includes a data lineage acquisition module, a data flow tracking module, a data processing link module, an authorization and traceability module, a lineage map construction module, and a lineage query module. Each data point generates a globally unique ID, recording the collection source, time, device, and application. Hash values ​​are used to verify integrity, and the collection location is blurred by default. Data flow records the reader, time, and purpose; processing records the input and output relationships, with a nesting depth limit of 20 levels, after which automatic merging occurs; authorization records include time, scope, duration, and withdrawal records. The lineage map adopts a directed acyclic graph structure, automatically detecting and blocking circular dependencies. It supports queries by data ID, time, application, and type, and a lineage comparison function highlights differences. Lineage is retained for 30 days after data deletion, and users can set the retention period. Exported data is anonymized by default, but users can choose to include data values ​​and add watermarks. Automatic archiving occurs when storage capacity is exceeded. This invention solves the problems of unclear sources, unclear flow, difficulty in tracing authorization, and high risk of privacy leaks for personal data, and is a core infrastructure for compliant personal data management.

Description

Technical Field

[0001] This invention relates to the fields of data management, data traceability, knowledge graphs, and privacy protection. Specifically, it relates to a method and system for constructing a personal data lineage map based on a photoconductive film. By establishing a lineage map of personal data generated by users in multiple application scenarios such as health monitoring, security protection, social services, virtual worlds, and smart homes through a unified identity authentication center, the method traces the source, flow, processing, and usage authorization of the data, thus solving the problems of unclear personal data sources, unclear flow, difficulty in tracing authorization, and high risk of privacy leakage in existing technologies. Citation of prior application

[0002] This application is based on the technology of the applicant's previously filed patent application, specifically cited as follows: 1. Prior patent application (application number 2026103505829, application date 2026-03-20, invention title: A method and system for unique identification of human optical spectral features based on photoconductive film) This patent discloses a method for uniquely identifying human optical spectral features. It uses a photoconductive film to collect human optical spectral features and generate a unique feature code for identity verification. The unified identity authentication center in this application uses the spectral feature acquisition and comparison technology of this patent as the basis for identity construction based on data lineage.

[0003] 2. Prior patent application (application number 2026103373270, application date 2026-03-19, invention title: A distributed housekeeping system and method based on multi-terminal collaboration) This patent discloses a distributed housekeeping system based on multi-terminal collaboration, including a main housekeeping unit and sub-housekeeping units. The data lineage construction framework in this application is implemented based on the housekeeping system architecture of this patent.

[0004] 3. Prior patent application (application number 2026103511641, application date 2026-03-21, invention title: A unified identity authentication and authorization center system and method based on photoconductive film) This patent discloses a unified identity authentication and authorization mechanism, including authorization record management and audit logs. The authorization information traceability in this application reuses the authorization record technology of this patent.

[0005] 4. Patents for various application scenarios submitted by the applicant. This application provides data lineage construction services for patents in various application scenarios, including health monitoring patents (blood pressure monitoring patent 2026103509995, blood oxygen monitoring patent 2026103510140, blood glucose monitoring patent 202610351030X, heart rate variability monitoring patent 2026103510583, arteriosclerosis monitoring patent 2026103510672, sleep monitoring patent 2026103510846, metabolic monitoring patent 202610351094X, fatigue driving monitoring patent 2026103511016, health trend analysis patent 2026103511548) and safety protection patents (anti-fraud patent 2). The patents listed are: 026103476031 (vehicle rescue patent 2026103450582, physiological abnormality rescue patent 2026103461708), social service patents (family tracing patent 2026103505068, virtual world scene patent 2026103511336), and intelligent interaction patents (eye tracking patent 2026103269949, unified identity authentication patent 2026103511641, status synchronization patent 2026103511938, offline collaboration patent 2026103512150, energy consumption management patent 2026103512343, conflict arbitration patent 2026103512451). The application dates of these prior basic patents are all earlier than this application, and they were not published before the filing date of this application; therefore, they do not constitute prior art for this application. Background Technology

[0006] With the development of smart terminals and IoT technologies, users generate a large amount of personal data in multiple scenarios (health monitoring, security protection, social services, virtual world, smart home). However, existing data management technologies have the following shortcomings: Data source unknown: Users cannot trace which device, time point, or application collected a certain health indicator (such as blood pressure); Unclear data flow: Users do not know which applications read, process, or share their data, and cannot confirm whether their data is being misused; Authorization is difficult to trace: The authorization records of applications previously authorized by users are scattered, making it impossible to centrally view the specific content and time of authorization; The data processing is opaque: the data undergoes multiple processing steps (such as raw blood pressure → average value → trend analysis → health report), the processing chain is unclear, and it is difficult to verify the accuracy of the data; High risk of privacy breach: The lack of data lineage makes it impossible to trace the source of the breach and identify the responsible party after the data is leaked; Compliance audit difficulties: Companies are unable to prove to regulatory agencies that their processing of personal data complies with the Personal Information Protection Law.

[0007] The applicant has previously filed patents for a distributed management system and a unified identity authentication system, which enable real-time online status synchronization and authorization record management. Building upon these, this invention further constructs a personal data lineage map, enabling complete traceability of data sources, flows, processing procedures, and authorization information. Summary of the Invention

[0008] (a) Purpose of the invention The purpose of this invention is to provide a method and system for constructing a personal data lineage map based on a photoconductive film. By establishing a lineage map of personal data generated by users in multiple application scenarios such as health monitoring, security protection, social services, virtual world, and smart home through a unified identity authentication center, the invention traces the source, flow, processing, and usage authorization of the data, thereby solving the problems of unclear source, unclear flow, difficulty in tracing authorization, and high risk of privacy leakage in the prior art.

[0009] (II) Technical Solution 1. A personal kinship mapping system based on a photoconductive film, characterized in that it comprises: The unified identity authentication center is used to collect the optical spectral characteristics of a user's human body through a photoconductive film and generate a unique identity feature code, which serves as the identity basis for data lineage construction. The data lineage collection module is used to collect metadata about personal data generated in various application scenarios through a distributed steward system architecture, including data source, collection time, collection device, collection application, data format, and data content hash value. The data flow tracking module is used to record the flow of data between various application scenarios, including the data reader, reading time, reading purpose, and sharing scope; The data processing link module is used to record the data processing process, including raw data, processing algorithm, processing time, processing result, and processor; The authorization traceability module is used to record the user's data authorization records for each application, including authorization time, authorization scope, authorization period, and authorization status; The lineage graph construction module is used to associate data sources, flow directions, processing links, and authorization information to construct a directed acyclic graph structure for data lineage graphs. The lineage query module allows users to query data lineage relationships by time, data type, application, device, and other dimensions, and displays the complete lifecycle of the data.

[0010] 2. The system according to claim 1, characterized in that, in the data lineage acquisition module, the metadata includes data ID, data type, data value, data source, acquisition time, acquisition location, data format, and data content hash value; the data content hash value is calculated using the SHA-256 algorithm and is used to verify data integrity; the acquisition location is fuzzy by default (retaining the administrative region level), and users can enable precise location recording in the unified identity authentication center.

[0011] 3. The system according to claim 1, wherein the data flow tracking module includes a flow record comprising a data ID, a source application identifier, a target application identifier, a flow time, a flow reason, a reading range, and a shared object.

[0012] 4. The system according to claim 1, characterized in that, in the data processing link module, the processing record includes a list of input data IDs, output data IDs, processing algorithm identifiers, processing parameters, processing time, processor, and processing version; the processing link supports multi-level nesting, and can trace the complete transformation path from the original data to the final result; the default upper limit of the nesting depth of the processing link is 20 levels, and when the upper limit is exceeded, the system automatically merges intermediate nodes and generates summary nodes.

[0013] 5. The system according to claim 1, wherein the authorization record in the authorization traceability module includes authorization ID, authorization application identifier, authorization data type, authorization scope, authorization period, authorization time, authorization status, withdrawal time, and withdrawal reason.

[0014] 6. The system according to claim 1, characterized in that, in the lineage graph construction module, the directed acyclic graph structure includes the following node types: data source node, processing node, data node, application node, and authorization node; edges represent the dependency relationships between nodes, including "generated from", "processed from", "read", and "authorized"; the system automatically detects cyclic dependencies, and if a cycle is detected, it is marked as an abnormal data stream and a reminder is pushed, automatically blocking the abnormal processing link; users can view the blocked link records in the unified identity authentication center and can manually unblock (requiring secondary confirmation).

[0015] 7. The system according to claim 1, characterized in that the bloodline query module supports the following query methods: querying and displaying a complete bloodline graph by data ID; querying and displaying bloodline relationships within a specified time period by time range; querying and displaying data bloodline relationships of a specified application by application; querying and displaying data bloodline relationships of a specified data type by type; the bloodline comparison function supports selecting two time points, and the system constructs bloodline graphs for the two time points respectively and calculates the differences (addition of nodes, deletion of nodes, changes in node attributes, addition / deletion of edges), and the differences are highlighted in the graph.

[0016] 8. A method for constructing a personal kinship map based on a photoconductive film, characterized by comprising the following steps: S1: Users complete identity authentication through the optical guide film, establishing a unified identity across scenarios; S2: The data lineage collection module collects personal data metadata generated in various application scenarios and generates a globally unique data ID; S3: The data flow tracking module records the flow of data between various applications; S4: The data processing link module records the data processing process and establishes input-output relationships; S5: The authorization traceability module records the user's data authorization records for each application; S6: The kinship graph construction module associates data sources, flow directions, processing links, and authorization information to construct a directed acyclic graph structure; S7: The bloodline map is stored in encrypted local storage in the supervisor unit, and users can export and share it; S8: Users can use the bloodline query module to query the bloodline relationship of data by dimension and display the complete life cycle of the data.

[0017] 9. The method according to claim 8, characterized in that, in step S6, the system automatically detects circular dependencies when constructing the kinship map. If a cycle is detected, it is marked as an abnormal data stream and a reminder is pushed, automatically blocking the abnormal processing link; after data deletion, the kinship relationship is retained for 30 days by default. Users can set the retention period (7 days, 30 days, 90 days, permanent) in the unified identity authentication center, and users can manually clean it up in advance.

[0018] 10. The method according to claim 8, characterized in that, in step S7, the kinship map is encrypted using AES-256, and the key is stored in the terminal's secure area; users can export the kinship map as JSON or PDF format with one click, and the system performs data value desensitization processing by default during export; users can select "Include Data Values" in the export settings (requires secondary confirmation), and the exported file is watermarked (including user ID and export time); users can manually delete specified data and its kinship relationships, and after deletion, the kinship mark of the associated data is synchronously triggered to "Deleted"; the upper limit of the kinship map storage capacity is preset by the system, and when the upper limit is exceeded, the system automatically archives the earliest kinship record, retaining only the summary information. Detailed Implementation

[0019] System architecture and data flow The core innovation of this system lies in combining a distributed steward system with data lineage construction to achieve complete traceability of the source, flow, processing chain, and authorization information of personal data.

[0020] The system architecture is as follows: Unified Identity Authentication Center: Deployed on the user's designated home device (default mobile phone), it uses a unique identity recognition based on a photoconductor film as the identity foundation for data lineage construction.

[0021] The supervisor unit is responsible for aggregating data metadata from various scenarios, recording data flow, processing links, authorization records, constructing lineage maps, and providing queries.

[0022] Sub-Manager Unit: A lightweight software module installed on various smart terminal devices. Each sub-manager corresponds to an application scenario and is responsible for reporting data metadata, flow records, processing records, and authorization records.

[0023] Data flow path: Users complete identity authentication through the optical guide film, establishing a unified identity. Each sub-manager reports data metadata to their supervisor; When data flows between applications, the data manager reports the flow record; During data processing, the sub-manager reports the processing records (input → output); When a user grants authorization, the authorization traceability module records the authorization history. The kinship graph construction module associates all records to construct a directed acyclic graph; Users can query their bloodline data through the bloodline query module.

[0024] The following provides a detailed description of each module: Example 1: Complete Blood Pressure Data and Bloodline Tracing User Zhang used the Blood Pressure Manager app to measure blood pressure (raw data), generating a data ID: BP_20260321_001. The metadata records include: Source (Blood Pressure Manager), Collection Time (2026-03-21 08:00:00), Collection Device (Mobile Phone), Data Value (135 / 85), and Hash Value (SHA-256, used for integrity verification). The data content hash value is calculated using the SHA-256 algorithm. When data is read or processed, the system recalculates the current data hash value and compares it with the original hash value. If they do not match, it is marked as "data has been tampered with," and a notification is sent.

[0025] The collected location is obfuscated by default (retaining the administrative district level, such as "Nanshan District, Shenzhen"). Users can enable precise location recording at the unified identity authentication center. Obfuscated location information is not considered sensitive personal information and complies with the requirements of the Personal Information Protection Law.

[0026] The health trend analysis application reads this blood pressure data and generates a flow record: Data ID BP_20260321_001 was read by the health trend analysis application, read time 2026-03-21 08:05:00, read purpose "generate health report".

[0027] The health trend analysis application processes this data (calculates a 7-day average), generating a new data ID: TREND_20260321_001. Processing record: Input BP_20260321_001, Output TREND_20260321_001. Processing algorithm: "moving average". Processing time: 2026-03-21 08:06:00. The default maximum nesting depth of the processing chain is 20 levels. When this limit is exceeded, the system automatically merges intermediate nodes and generates summary nodes.

[0028] User Zhang authorized the Family Doctor app to read his health report. Authorization record: App authorized: "Family Doctor", Data type authorized: "Health Trend Report", Authorization period: "30 days", Authorization time: 2026-03-21 09:00:00.

[0029] The kinship graph construction module generates a directed acyclic graph: Data source node: BP_20260321_001 Processing nodes: Moving average algorithm Data node: TREND_20260321_001 Application nodes: Health trend analysis applications, family doctor applications Authorization Node: Authorize family doctors to read health reports Edge: BP_20260321_001 → (processed from) → TREND_20260321_001, TREND_20260321_001 → (read) → Health Trend Analysis Application, TREND_20260321_001 → (authorized) → Family Doctor Application The system automatically detects circular dependencies. If a circular dependency is detected, it is marked as an abnormal data stream, and an alert is sent, automatically blocking the abnormal processing link. Users can view the blocked link records in the unified identity authentication center and can manually unblock the link (secondary confirmation is required).

[0030] When a user queries the pedigree chart of data ID BP_20260321_001, the system displays the complete lifecycle: raw data collection → processing into a trend report → being read by the health trend application → being authorized for use by family doctors.

[0031] Example 2: Multi-source data fusion and processing for kinship tracing User Li simultaneously used blood pressure, blood oxygen, and blood glucose measurement data from the health trend analysis application. The application read these three raw data sets, fused and processed them to generate a "cardiovascular health score" (data ID: SCORE_20260321_001). Processing record: Input BP_20260321_001, SpO2_20260321_001, GLU_20260321_001; Output SCORE_20260321_001; Processing algorithm: "Cardiovascular Risk Assessment Model"; Processing time: 2026-03-21 10:00:00.

[0032] The pedigree graph construction module generates a multi-input, single-output node: three data source nodes point to the processing node, and the processing node points to the result data node. When a user queries the pedigree graph of SCORE_20260321_001, the system displays the three original data sources it depends on, as well as the complete pedigree chain of each original data source.

[0033] Example 3: Authorization Tracing and Revocation Tracking User Zhang authorized a health app to read his blood pressure data (authorization record: authorization date 2026-03-01, authorization period "permanent"). On 2026-03-15, Zhang discovered that the app had shared the data with a third party without authorization and withdrew the authorization at the unified identity authentication center. The authorization traceability module recorded the withdrawal date as 2026-03-15 and the reason for withdrawal as "data misuse".

[0034] When a user queries the kinship chart of this blood pressure data, the system displays: the data was authorized to a health application (from March 1, 2026 to March 15, 2026), the authorization has been revoked on March 15, 2026, and the reason for revocation is "data misuse". The user can export this authorization record as evidence for rights protection.

[0035] Example 4: Bloodline Marking After Data Deletion User Zhang deleted a blood pressure measurement record (BP_20260321_001). The system marked this data node as "deleted," but retained its lineage (data ID, metadata, flow record, processing record), only marking the deletion status. Lineage is retained by default for 30 days after data deletion; users can set the retention period (7 days, 30 days, 90 days, permanent) at the unified identity authentication center, and can manually clear it earlier. When a user queries the data lineage, the system prompts "This data has been deleted, deletion time 2026-03-22, lineage is for reference only." All processing results data that depend on this data will display "Dependent data deleted" in their lineage graph.

[0036] Example 5: Bloodline Chart Query and Comparison User Zhang queried the kinship graph of blood pressure data (BP_20260321_001), and the system displayed the complete kinship chain. Zhang used the kinship comparison function, selecting two time points (T1: 2026-03-01, T2: 2026-03-21). The system constructed kinship graphs for both time points and calculated the differences: added nodes, deleted nodes, changes in node attributes, and added / deleted edges. Differences were highlighted in the graph (green for additions, red for deletions, and yellow for modifications), and the user could click to view details.

[0037] Example 6: Bloodline Map Export User Zhang exported his kinship chart as a PDF. During export, the system by default anonymizes the data values ​​(e.g., blood pressure 135 / 85 is displayed as "anonymized"), retaining only the data structure, source, flow, and processing chain. Users can select "Include Data Values" in the export settings (requires secondary confirmation) to add a watermark (including user ID and export time) to the exported file to prevent unauthorized dissemination. Exception handling mechanism

[0038] Data missing handling: When the metadata of a data node in the pedigree is missing, the system marks it as "information missing" and pushes a reminder: "Some data pedigree information is incomplete. It is recommended to check the data acquisition module." Circular dependency detection: Circular dependencies are automatically detected during kinship graph construction. When a cycle is detected, it is marked as abnormal and automatically blocked, with a push notification: "Abnormal data circular dependency detected, automatically blocked. Please check the processing logic." Authorization expiration handling: After the authorization record expires, the system automatically updates the authorization status to "expired", and the data flow tracking module will no longer record the application's data reading behavior.

[0039] Bloodline relationships retained after data deletion: Bloodline relationships are retained by default for 30 days after data deletion, but users can manually delete them in advance.

[0040] Storage capacity management: The storage capacity limit for the kinship map is preset by the system (e.g., 1GB). When the limit is exceeded, the system automatically archives the oldest kinship record, retaining only summary information (data ID, time, type), and not retaining detailed processing links. Users can view archived records in the unified identity authentication center and can manually delete historical kinship records for a specified time period. Beneficial effects

[0041] Data source traceability: Each data point has a unique ID, recording the collection device, time, and application, ensuring a clear source; hash value verification verifies integrity and prevents tampering; Data flow is traceable: It fully records which applications read, share, and transfer data, and the flow is transparent; Auditable processing chain: Records every processing step of the data, with a nesting depth limit of 20 levels. If the nesting depth is exceeded, it will be automatically merged, and the path will be complete. Authorization is traceable: Authorization records include time, scope, duration, and withdrawal records, which meets the audit requirements of the Personal Information Protection Law; Kinship graph visualization: The directed acyclic graph structure intuitively displays the complete life cycle of data and supports multi-dimensional queries and kinship comparison; Privacy breaches can be traced back to the responsible party: After a data breach, the source of the breach and the responsible party can be traced, providing evidence for protecting one's rights; Anomaly detection and blocking: Automatically detects abnormal data streams such as circular dependencies and blocks them automatically. Users can view and manually unblock these streams. Data deletion is traceable: After data is deleted, the lineage relationship is retained for 30 days for tracing history, without affecting the processed results; Export privacy protection: Data is de-identified by default during export, but you can choose to include data values ​​(confirmation required) and add watermarks to prevent misuse; Storage capacity management: Automatically archive historical lineage to prevent storage overflow; Technological synergy: This system integrates a distributed steward system and a unified identity authentication patent to form a complete closed loop of "identity → data → lineage → auditing," which is the core infrastructure for personal data compliance management.

Claims

1. A personal data lineage mapping system based on optical guide film, characterized in that, include: The unified identity authentication center is used to collect the optical spectral characteristics of a user's human body through a photoconductive film and generate a unique identity feature code, which serves as the identity basis for data lineage construction. The data lineage collection module is used to collect metadata about personal data generated in various application scenarios through a distributed steward system architecture, including data source, collection time, collection device, collection application, data format, and data content hash value. The data flow tracking module is used to record the flow of data between various application scenarios, including the data reader, reading time, reading purpose, and sharing scope; The data processing link module is used to record the data processing process, including raw data, processing algorithm, processing time, processing result, and processor; The authorization traceability module is used to record the user's data authorization records for each application, including authorization time, authorization scope, authorization period, and authorization status; The lineage graph construction module is used to associate data sources, flow directions, processing links, and authorization information to construct a directed acyclic graph structure for data lineage graphs. The lineage query module allows users to query data lineage relationships by time, data type, application, device, and other dimensions, and displays the complete lifecycle of the data.

2. The system according to claim 1, characterized in that, In the data lineage acquisition module, the metadata includes data ID, data type, data value, data source, acquisition time, acquisition location, data format, and data content hash value; The hash value of the data content is calculated using the SHA-256 algorithm to verify data integrity; the collection location is obfuscated by default, but users can enable precise location recording at the unified identity authentication center.

3. The system according to claim 1, characterized in that, In the data flow tracking module, the flow record includes data ID, source application identifier, target application identifier, flow time, flow reason, reading range, and shared object.

4. The system according to claim 1, characterized in that, In the data processing link module, the processing record includes a list of input data IDs, output data IDs, processing algorithm identifiers, processing parameters, processing time, processor, and processing version. The processing link supports multi-level nesting, allowing for the tracing of the complete transformation path from the original data to the final result. The default upper limit for the nesting depth of the processing link is 20 levels. When the upper limit is exceeded, the system automatically merges intermediate nodes and generates summary nodes.

5. The system according to claim 1, characterized in that, In the authorization traceability module, the authorization record includes authorization ID, authorized application identifier, authorized data type, authorization scope, authorization period, authorization time, authorization status, withdrawal time, and withdrawal reason.

6. The system according to claim 1, characterized in that, In the lineage graph construction module, the directed acyclic graph structure includes the following node types: data source node, processing node, data node, application node, and authorization node; edges represent the dependencies between nodes, including "generated from", "processed from", "read", and "authorized"; the system automatically detects circular dependencies, and if a cycle is detected, it is marked as an abnormal data stream and a reminder is pushed, automatically blocking the abnormal processing link; users can view the blocked link records in the unified identity authentication center and can manually unblock them.

7. The system according to claim 1, characterized in that, The bloodline query module supports the following query methods: querying and displaying a complete bloodline map by data ID; querying and displaying bloodline relationships within a specified time period by time range; querying and displaying data bloodline relationships by application; querying and displaying data bloodline relationships by type; the bloodline comparison function supports selecting two time points, and the system constructs bloodline maps for the two time points respectively and calculates the differences, which are highlighted in the map.

8. A method for constructing a personal kinship map based on a photoconductive film, characterized in that, Includes the following steps: S1: Users complete identity authentication through the optical guide film, establishing a unified identity across scenarios; S2: The data lineage collection module collects personal data metadata generated in various application scenarios and generates a globally unique data ID; S3: The data flow tracking module records the flow of data between various applications; S4: The data processing link module records the data processing process and establishes input-output relationships; S5: The authorization traceability module records the user's data authorization records for each application; S6: The kinship graph construction module associates data sources, flow directions, processing links, and authorization information to construct a directed acyclic graph structure; S7: The bloodline map is stored in encrypted local storage in the supervisor unit, and users can export and share it; S8: Users can use the bloodline query module to query the bloodline relationship of data by dimension and display the complete life cycle of the data.

9. The method according to claim 8, characterized in that, In step S6, the system automatically detects circular dependencies when constructing the kinship map. If a cycle is detected, it is marked as an abnormal data stream and a reminder is pushed, automatically blocking the abnormal processing link. After the data is deleted, the kinship relationship is retained for 30 days by default. Users can set the retention period in the unified identity authentication center, and users can manually clean it up in advance.

10. The method according to claim 8, characterized in that, In step S7, the kinship map is encrypted using AES-256, and the key is stored in the terminal's secure area. Users can export the kinship map as JSON or PDF format with one click. During export, the system performs data value desensitization processing by default. Users can select "Include data values" in the export settings and add watermarks to the exported files. Users can manually delete specified data and its kinship relationships. After deletion, the kinship markers of related data are simultaneously triggered to "Deleted". The upper limit of the kinship map storage capacity is preset by the system. When the upper limit is exceeded, the system automatically archives the oldest kinship record and only retains the summary information.