Hybrid directory management method and system for automobile industry trusted data space

Abstract

The invention discloses a mixed directory management method and system for a trusted data space in the automobile industry, and aims to solve the contradiction between sovereignty protection and query efficiency of pure centralized and pure federated directory architecture. The method is based on a hybrid architecture of participating nodes, centralized directory services and data consumers, and through metadata acquisition preprocessing and automatic grading, public layer metadata is desensitized and differentially synchronized to a central directory; and completing query request verification and standardization in combination with DID identity authentication, intelligently selecting a query path by a routing engine according to multi-dimensional cost, executing distributed fine-grained authorization on business / sensitive layer metadata, and finally aggregating multi-source result feedback. Experiments prove that the query efficiency of the method is improved by 1-2 orders of magnitude compared with a pure federation mode, the synchronization bandwidth is reduced by 97% compared with a pure centralization mode, the balance of efficient global discovery and strict sovereign protection of data resources in the automobile industry is realized, and the method adapts to large-scale collaboration scenes such as supply chain collaboration.

Description

Technical Field

[0001] This invention relates to the field of trusted data space construction technology, specifically to a hybrid directory management method and system for trusted data spaces in the automotive industry. Background Technology

[0002] In the process of building a trusted data space in the automotive industry, the data catalog system, as the core infrastructure for discovering, locating, and accessing data resources, directly impacts the efficiency of data element circulation and the protection of data sovereignty. Currently, mainstream solutions primarily adopt either a fully centralized or fully federated catalog architecture. While centralized catalogs offer efficient global queries, they require participants to upload complete metadata, posing risks such as loss of data sovereignty, single points of failure, and performance bottlenecks. Conversely, purely federated catalogs, while ensuring localized data control, face challenges such as low query efficiency and insufficient global visibility in large-scale, multi-node industrial ecosystems.

[0003] With the rapid development of business scenarios such as intelligent connected vehicles and collaborative manufacturing in the supply chain, the automotive industry has placed higher demands on the real-time performance, security, and collaborative efficiency of data sharing. Existing single-architecture directory systems are unable to simultaneously meet the two core requirements of "efficient global discovery" and "strict data sovereignty," which severely restricts the large-scale application of data space in complex industrial environments.

[0004] Traditional solutions mainly exist in two typical models: One approach is a fully centralized directory service, which requires all participants to register their metadata with a central node. While this method enables fast retrieval, it carries the risk of a single point of failure, and participants generally have low willingness to register due to concerns about data sovereignty, resulting in insufficient directory coverage.

[0005] Second, there is fully federated directory querying, where each node maintains its own directory, and query requests must be broadcast to all nodes. While this method ensures data sovereignty, query latency increases significantly as the number of nodes grows, and it cannot support efficient global statistics and resource discovery, making it difficult to meet the needs of large-scale, real-time collaborative scenarios in the automotive industry. Summary of the Invention

[0006] The present invention proposes a hybrid catalog management method and system for trusted data spaces in the automotive industry, which can at least solve one of the technical problems in the background art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A hybrid catalog management method for trusted data spaces in the automotive industry involves executing the following steps via computer equipment: Based on a hybrid directory system architecture that includes participating nodes, a centralized directory service, and data consumers, this method includes the following steps: S1. Participating nodes collect raw metadata from automotive industry-related business systems, and generate metadata objects with feature vectors through standardized preprocessing, providing a data foundation for subsequent metadata classification. S2. The metadata classification module of the participating node performs sensitivity calculation on the metadata object, divides it into public layer, business layer and sensitive layer according to the preset threshold, and generates public layer summary metadata as needed for business layer or sensitive layer metadata, thereby defining the scope of metadata synchronization. S3. The participating nodes perform desensitization processing on the public layer metadata and public layer summary metadata, and then use a differentiated synchronization strategy to synchronize the desensitized incremental public layer metadata to the centralized directory service. The centralized directory service stores it in the public metadata pool and updates the global resource index to provide data support for global queries. S4. Data consumers submit query requests carrying DID identity credentials through a unified query interface. The centralized directory service first verifies the legality of the request signature, and then standardizes the legal original query statement into a structured query expression, providing a unified analysis basis for routing decisions. S5. The centralized directory service's query routing engine performs hierarchical analysis on the structured query expression, and calculates the comprehensive cost of the three routing modes (centralized, federated, and hybrid) by combining multiple dimensions such as query complexity, number of nodes, and access permissions, and selects the optimal routing mode with the lowest comprehensive cost. S6. If the optimal routing mode involves access to metadata of the business layer or sensitive layer, the centralized directory service will forward the query request to the corresponding participating node. The participating node will verify the identity and attribute set of the data consumer through the DID identity authentication module, and then perform authorization judgment based on the attribute-based access control policy. Access to the corresponding level of metadata will only be granted to requests that have passed the authorization. S7. Based on the optimal routing mode, collect query results from centralized directory services and / or authorized participating nodes, perform deduplication, sorting, and credibility weighting fusion processing on the multi-source results, and finally return the aggregated unified query results to the data consumer through a unified query interface.

[0008] On the other hand, the present invention also discloses a hybrid directory management system for a trusted data space in the automotive industry, which is used in the above-mentioned hybrid directory management method for a trusted data space in the automotive industry, and includes participating nodes, a centralized directory service and data consumers. The participating nodes deploy local federated directories, metadata hierarchical modules, and DID identity authentication modules. The centralized directory service deploys global resource indexes, public metadata pools, query routing engines, and policy decision points. The data consumers configure unified query interfaces and DID identity credentials.

[0009] Furthermore, the local federated directory is used to store the complete business metadata of the data provider locally. This metadata contains sensitive information and is maintained only within the local node. Metadata classification module: used to automatically classify local business metadata into public layer, business layer and sensitive layer, so as to realize the classification and management of metadata according to sensitivity level; DID identity authentication module: used to verify the identity of the data consumer who initiates the data access request, and to complete access permission authorization and authentication according to preset rules; Global Resource Index: Used to uniformly maintain data resource information and data location information of all participating nodes in the entire network, forming a globally searchable resource catalog; Public metadata pool: used to aggregate and store public layer metadata synchronized from each participating node, does not contain sensitive information, and supports global public query; Query routing engine: intelligently selects the execution path of central query or federated query based on the complexity of the current query request, data distribution and permission conditions; Policy decision point: Used to formulate and execute globally unified access control policies, and to perform permission judgment and policy control on cross-node data access; Unified query interface: Provides a standardized and unified data resource query portal to support data consumers in retrieving and accessing global data resources; DID (Digital Identity Certificate): A digital identity identifier used by data consumers to submit identity authentication, request signatures, and verify permissions to the system.

[0010] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0011] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0012] As can be seen from the above technical solution, the hybrid directory management method for trusted data spaces in the automotive industry of this invention constructs a logically unified but physically distributed directory system. At the centralized level, it maintains a global resource index and public metadata to support efficient retrieval; at the federated level, it retains complete metadata and sensitive information of each participating party, and achieves on-demand and authorized access through intelligent routing and synchronization mechanisms. Specifically, the system automatically synchronizes public information to the central directory through a metadata hierarchical classification strategy to ensure global discoverability; and achieves fine-grained access control of distributed data resources through digital identity authentication and attribute encryption mechanisms. This architecture leverages the query efficiency advantages of a centralized directory while retaining the sovereignty protection characteristics of a federated directory.

[0013] The specific advantages of this invention are as follows: By introducing a hybrid architecture of federated and centralized directories, a balance is achieved between "controllable and manageable" and "efficiently visible" data resources in the automotive industry's trusted data space. Under the premise of strictly protecting the data sovereignty of all participants, it can provide global data discovery efficiency that is no less than that of centralized solutions.

[0014] By introducing metadata hierarchical classification and intelligent query routing mechanisms, an adaptive directory service that combines flexibility and performance is built. It can dynamically select the optimal execution path based on the complexity and sensitivity of query requests, thereby maintaining efficient query response even in complex multi-node environments.

[0015] This hybrid catalog synchronization and management approach provides a scalable and governable data resource organization paradigm for the automotive industry's trusted data space, laying a solid data infrastructure for advanced applications such as cross-enterprise data collaboration, supply chain transparency, and collaborative R&D. Attached Figure Description

[0016] Figure 1 This is a diagram of the hybrid data catalog system architecture of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0018] like Figure 1 As shown in this embodiment, the hybrid directory management method for trusted data spaces in the automotive industry is based on the collaborative work of centralized and federated directories to achieve efficient discovery and sovereignty protection of data resources. Implementation is based on a hybrid directory system architecture that includes participating nodes, centralized directory services, and data consumers. The method includes the following steps: S1. Participating nodes collect raw metadata from automotive industry-related business systems, and generate metadata objects with feature vectors through standardized preprocessing, providing a data foundation for subsequent metadata classification. S2. The metadata classification module of the participating node performs sensitivity calculation on the metadata object, divides it into public layer, business layer and sensitive layer according to the preset threshold, and generates public layer summary metadata as needed for business layer or sensitive layer metadata, thereby defining the scope of metadata synchronization. S3. Participating nodes perform desensitization processing on public layer metadata and public layer summary metadata, and then use a differentiated synchronization strategy to synchronize the desensitized incremental public layer metadata to the centralized directory service. The centralized directory service stores it in the public metadata pool and updates the global resource index to provide data support for global queries. S4. Data consumers submit query requests carrying DID identity credentials through a unified query interface. The centralized directory service first verifies the legality of the request signature, and then standardizes the legal original query statement into a structured query expression, providing a unified analysis basis for routing decisions. S5. The centralized directory service's query routing engine performs hierarchical analysis on the structured query expression, and calculates the comprehensive cost of the three routing modes (centralized, federated, and hybrid) by combining multiple dimensions such as query complexity, number of nodes, and access permissions, and selects the optimal routing mode with the lowest comprehensive cost. S6. If the optimal routing mode involves access to metadata of the business layer or sensitive layer, the centralized directory service will forward the query request to the corresponding participating node. The participating node will verify the identity and attribute set of the data consumer through the DID identity authentication module, and then perform authorization judgment based on the attribute-based access control policy. Access to the corresponding level of metadata will only be granted to requests that have passed the authorization. S7. Based on the optimal routing mode, collect query results from centralized directory services and / or authorized participating nodes, perform deduplication, sorting, and credibility weighting fusion processing on the multi-source results, and finally return the aggregated unified query results to the data consumer through a unified query interface.

[0019] The following is for Figure 1 The system architecture shown is illustrated below, along with the descriptions of its components and functions: Participating nodes (data providers) Local federated directory: Used to store complete business metadata of the data provider locally. This metadata contains sensitive information and is maintained only within the local node.

[0020] Metadata Classification Module: This module is used to automatically classify local business metadata into public, business, and sensitive layers, enabling metadata to be classified and managed according to its sensitivity level.

[0021] The DID identity authentication module is used to verify the identity of the data consumer who initiates the data access request and to complete the access permission authorization and authentication according to preset rules.

[0022] Centralized directory services Global Resource Index: Used to uniformly maintain the data resource information and data location information of all participating nodes in the entire network, forming a globally searchable resource catalog.

[0023] Public metadata pool: Used to aggregate and store public layer metadata synchronized from each participating node. It does not contain sensitive information and supports global public query.

[0024] Query routing engine: Used to intelligently select the execution path of central query or federated query based on the complexity of the current query request, data distribution and permission conditions.

[0025] Policy Decision Point: Used to formulate and execute globally unified access control policies, and to perform permission judgment and policy control on cross-node data access.

[0026] Data consumer Unified query interface: Provides a standardized and unified data resource query portal to support data consumers in retrieving and accessing global data resources.

[0027] DID (Digital Identity Certificate): A digital identity identifier used by data consumers to submit identity authentication, request signatures, and verify permissions to the system.

[0028] Specifically, based on Figure 1 The system architecture shown, and the workflow of this solution, include the following key steps and innovative parts: Step 1: Metadata Collection and Preprocessing On the participating nodes (data providers), the local federated catalog collects raw metadata from business systems such as manufacturing, supply chain, and vehicle operation. For unstructured / semi-structured data from different business sources, it performs standardization processing, including field parsing, format unification, noise removal, and structure extraction, generating metadata objects in a unified format.

[0029] The output of this step is a structured meta-dataset, which lays the foundation for subsequent automatic hierarchical, synchronization, and authorization processes.

[0030] Original metadata collection: (1) Feature structuring: (2) in For the participating node numbers, For nodes The collected number One original metadata record, The amount of raw metadata collected. For nodes The original metadata set. This provides metadata standardization and feature extraction functions, including processing steps such as field parsing, noise reduction, and structure transformation. raw metadata The resulting feature vector, composed of multiple feature dimensions, is used for subsequent sensitivity calculation and classification.

[0031] Step 2: Automatic metadata classification (public layer / business layer / sensitive layer) The metadata grading module within each participating node performs sensitivity assessments on each piece of metadata and determines its grading based on whether it contains publicly available information. The grading is divided into three categories: Public Layer (synchronizable), Business Layer (locally stored), and Sensitive Layer (strictly restricted). This step ensures that only metadata containing sensitive content can be synchronized to the centralized directory, safeguarding data sovereignty.

[0032] The classification system calculates sensitivity to metadata feature vectors and automatically makes classification decisions based on set thresholds.

[0033] Sensitivity rating: (3) Grading rules: (4) in Features The Each feature value. This is a feature sensitivity mapping function that maps feature values ​​to sensitivity contributions, such as field type → risk score. Sensitivity weighting coefficient is used to reflect the degree of influence of different features on sensitivity. Give an overall sensitivity score to metadata. and These are the upper and lower bounds of the sensitivity threshold, used to distinguish between the public layer, the business layer, and the sensitive layer. The final classification hierarchy for metadata records (Public / Business / Sensitive).

[0034] Step 3: Public Layer Metadata Desensitization and Differentiation Synchronization Metadata classified as "public layer" is anonymized and field-filtered, retaining only non-sensitive identifiers, business tags, and other summary information. Subsequently, to reduce network transmission overhead, the system adopts a differentiated synchronization strategy, synchronizing only metadata entries that are updated or added compared to the previous synchronization.

[0035] The synchronized records are entered into the "public metadata pool" of the centralized directory service, and the global resource index is updated.

[0036] Desensitization and conversion: (5) Differentiated synchronization: (6) in This is a desensitization function used to delete or hash sensitive fields, retaining only publicly available metadata content. This is the de-identified public layer metadata record. For nodes in time A collection of public layer metadata generated at any given time. This is the time it took for the node to be last synchronized to the central directory. This is for the incremental data (differential dataset) that needs to be uploaded for this synchronization, in order to reduce network overhead.

[0037] Step 4: Unified Query Request Reception and Standardization Data users submit query requests through the "Unified Query Interface," along with their DID identity credentials. The centralized directory service first verifies the queryer's signature to ensure the request originates from a legitimate entity. It then parses the query statement, standardizing it into a structured query expression so that the subsequent routing engine can analyze its requirement hierarchy and complexity.

[0038] Signature verification: (7) Query standardization: (8) in This is the public key used by the data user. A digital signature for the query request by the data user. The original query expression (which may be a keyword, a structured statement, or natural language). This is a set of attributes for the user (roles, organizations, permission domains, etc.). This is a digital signature verification function used to verify the legitimacy of the request subject. The query normalization function transforms the input query into a uniform format (AST / field-value pairs, etc.). This is a standardized query expression, which facilitates route analysis.

[0039] Step 5: Intelligent Query Routing Decision The query routing engine analyzes the normalized query. The system determines whether a query only requires public metadata, or whether it involves business layer or sensitive information. Then, considering factors such as query complexity, number of nodes, and access permissions, it selects from three query paths: centralized query, federated query, and hybrid query. This mechanism uses a cost function to determine the optimal route, ensuring a balance between efficiency, security, and coverage.

[0040] Cost function: (9) Optimal route selection: (10) in Candidate routing patterns include For central directory search mode, For the federated node query mode, This is a mixed query mode. This is the normalized query expression. In routing mode Execute query below The estimated delay. This represents the coverage of the query requirements for this routing pattern (from 0 to 1). and This is a weighting parameter used to control the proportion of efficiency and coverage in the routing process. In the pattern Execute query below The overall cost. The optimal query routing pattern that minimizes cost.

[0041] Step Six: Distributed Identity Authentication and Fine-Grained Authorization For query requests that require access to metadata in the business layer or sensitive layers, the centralized directory service forwards the query to the relevant participating nodes. The DID authentication module on the node side verifies the identity of the query initiator and makes authorization decisions based on local access policies (such as attributes based on roles, organizations, and data domains). Only authorized records are returned, thus achieving distributed, fine-grained access control.

[0042] Access strategy function: (11) Authorization determination: (12) in For resources The access strategy function performs authorization judgment based on attribute-based access control (ABAC). This is a set of attributes for the user, including information such as roles, organizations, and business domain licenses. To match user attributes against a policy, return allow / deny. For participating nodes to record metadata The authorization result is displayed. After authorization is successful, the node returns the corresponding business layer or sensitive layer metadata.

[0043] Step 7: Results Aggregation and Response Return Based on the query route type, the system merges query results from the central directory and multiple participating nodes. It performs deduplication, sorting, and reliability weighting operations on records from different sources, and then returns the final results to the data user. In a hybrid mode, the system first performs initial screening using the central directory, then performs fine-grained queries and aggregations on relevant nodes.

[0044] Federal outcome merging: (13) Mixed mode result aggregation: (14) in For from participating nodes The set of query results. This is a collection of query results from multiple participating nodes. This is the collection of query results from the central directory. This is a set union operation used for summarizing federated results. This is a result fusion function for the mixed mode, which includes deduplication, sorting, relevance scoring, and other processing. The final result set is returned to the data user.

[0045] The combination of these steps constitutes the hybrid directory synchronization and management method described in this invention, achieving the technical effect of data resources being both "efficiently discoverable" and "securely controllable" in the complex automotive industry ecosystem.

[0046] The following are examples: This embodiment simulates a data collaboration scenario between a large automotive OEM and a Tier 1 component supplier (Supplier A). The system is deployed in the "Automotive Industry Trusted Data Space" environment.

[0047] 1. System Environment Deployment Centralized directory service node: Deployed on the cloud of the industry data space operator, configured with a global resource index library (based on ElasticSearch) and a public metadata pool.

[0048] Participating Node (Supplier A): Deploys a federated catalog agent node in the local data center, connecting its MES (Manufacturing Execution System) and PLM (Product Lifecycle Management System).

[0049] The specific implementation steps are as follows: Step 1: Metadata Collection and Preprocessing. Supplier A's local node periodically scans the MES system logs via a connector. Assume that a raw log entry related to "brake caliper batch testing" is collected. : The original data content includes: {ID:"Batch_20250104_001",Product:"Brake_Caliper_X1",ProductionLine:"Line_A",DefectRate:"0.02%",ProcessParams:"{Temp:240,Press:1500}",ManagerIP:"192.168.1.10"}.

[0050] Using feature extraction functions Standardize the system, remove useless redundant characters, and extract key feature vectors. .

[0051] Step Two: Automatic Metadata Hierarchy. The local node's metadata hierarchy module automatically classifies metadata. Perform sensitivity calculations. Set a sensitivity threshold. (Public boundaries), (Sensitive boundary). Using formulas calculate: Feature “Product Name”: , point Feature "Defect Rate": , point Feature "Process Paramals": , point Total Score .because This metadata entry is marked as "sensitive layer".

[0052] To support discovery, the system generates a corresponding summary metadata entry (containing only ID and Product), which scores 10 points, less than [a score not specified in the original text]. It is marked as "public layer".

[0053] Step 3: Differentiated Synchronization.

[0054] Complete records of the "sensitive layer" are kept in vendor A's local federated directory and are not uploaded.

[0055] For the summary records in the "public layer", execute the desensitization function. Ensure that any traces such as IP addresses are removed before generating. .

[0056] The system calculates incremental data. The public summary will be synchronized to the "public metadata pool" of the centralized directory service.

[0057] Step 4: Query Request and Normalization. The OEM's data analyst wants to query "defect rate data for all brake calipers".

[0058] Submit query "Query the DefectRate of Brake_Caliper_X1".

[0059] The central node verifies the OEM's DID signature. pass.

[0060] Queries are standardized In structured SQL format: SELECT DefectRate FROM DataSpace WHERE Product = 'Brake_Caliper_X1' Step 5: Intelligent Query Routing Decision. The routing engine analyzes the query request. : Route determination: The request involves a specific "DefectRate" (which belongs to the business / sensitive field), while the central directory only contains the "Product" index, without a specific value.

[0061] Cost calculation: The inability to achieve the required coverage comes at an immeasurable cost.

[0062] It requires broadcast queries, resulting in higher latency, but this is acceptable in scenarios with only a known supplier.

[0063] The optimal choice is to first locate "Brake_Caliper_X1" belonging to "Supplier A" in the central directory, and then directly initiate a targeted federated query to the node of "Supplier A".

[0064] Step Six: Distributed Authorization and Execution.

[0065] The central directory forwards targeted queries to the node of supplier A.

[0066] Supplier A node triggers authorization determination : Check the OEM's attribute set Does it include the "supply chain partner" label, and has a data access agreement been signed for this batch?

[0067] After successful verification, the local node decrypts the "DefectRate" field in the "sensitive layer" data.

[0068] Step 7: Result Aggregation. The system aggregates the real-time defect rate data returned by Supplier A with the basic product descriptions in the central directory and returns the aggregated data to the OEM analyst.

[0069] The relevant performance analysis is as follows: To verify the effectiveness of the hybrid catalog management method described in this embodiment in a supply chain collaboration scenario, the inventors built a simulation test environment to conduct comparative experiments.

[0070] 1. Experimental Environment Setup Hardware environment: Configure 1 central server (simulating an industry credible party, 8-core CPU, 32G memory) and 50 distributed nodes (simulating suppliers at different levels, 4-core CPU, 16G memory).

[0071] Network environment: The central server and distributed nodes are connected to simulate a wide area network, with the average network latency set to 20ms.

[0072] Dataset: Generates 1 million records of simulated automotive component metadata. Of these, 30% contains sensitive manufacturing parameters (requiring local storage), and 70% consists of general product descriptions (which can be publicly disclosed).

[0073] 2. Comparison of Three Modes: This experiment selected three modes for comparison: Option A (Pure Federated Mode): No central index; queries need to be broadcast to all 50 nodes.

[0074] Option B (purely centralized mode): All metadata (including sensitive data) is uploaded to the central database.

[0075] Option C (hybrid mode of this invention): Public metadata synchronization center, local storage of sensitive metadata, and intelligent routing.

[0076] 3. Experimental Results and Analysis Test Metric 1: Average Query Response Time (QueryResponseTime) This measures the average time taken for the system to return results from initiating a query to receiving results, considering different levels of concurrent query requests (QueryQPS). The query content included a mix of "query only publicly available models" and "query specific batch sensitive parameters".

[0077] Table 1 Average time for concurrent queries

[0078] Test Metric 2: Network Bandwidth Consumption Test measures the network traffic required for the system to synchronize metadata when data is updated.

[0079] Table 2. Traffic Required for Metadata Synchronization

[0080] Results Analysis: Solution A involves no synchronization but incurs high query costs. Compared to Solution B, this invention utilizes metadata hierarchical classification and anonymization, only synchronizing and publishing summary information, thus reducing bandwidth consumption by approximately 97%.

[0081] Test Indicator 3: Sensitive Data Leakage Risk Simulation scenario where the central server is attacked and compromised.

[0082] Option B: 100% leakage of raw data (including process parameters).

[0083] Solution C (the present invention): Only the anonymized public index (such as the product ID) is disclosed, while the core process parameters are still securely stored on the local nodes of each supplier, with a leakage risk of 0.

[0084] Experimental data show that the hybrid catalog management method proposed in this invention improves query efficiency by 1-2 orders of magnitude compared to the traditional pure federated solution in supply chain collaboration scenarios. Compared to the pure centralized solution, it maintains near-its query performance (performance loss <20%) while effectively resolving the contradiction between data sovereignty protection and large-scale data discovery by reducing synchronization bandwidth consumption by 97% and realizing the localization of sensitive data.

[0085] In summary, this invention effectively overcomes the shortcomings of pure federated directories in global discovery and query efficiency, as well as the inherent defects of pure centralized directories in data sovereignty and flexibility, by constructing a hybrid architecture of federated and centralized directories and introducing intelligent synchronization and management mechanisms.

[0086] The technical effects achievable by this invention are as follows: This invention designs a data catalog management method for the automotive industry's trusted data space based on a hybrid architecture of federated and centralized catalogs. It solves the contradiction between the low query efficiency of the pure federated mode and the lack of data sovereignty in the pure centralized mode, and realizes efficient data resource discovery and collaboration under the premise of protecting the data sovereignty of all parties.

[0087] This invention introduces a metadata hierarchical classification and intelligent routing mechanism to automatically classify metadata and execute differentiated synchronization strategies during directory synchronization. This improves global query efficiency while ensuring localized control of sensitive metadata, thereby enhancing the system's security and adaptability.

[0088] This invention constructs a distributed access policy management framework based on digital identity and attribute encryption, realizing fine-grained data resource access control and dynamic authorization. It solves the single point of failure and policy consistency problems of traditional centralized permission management in a distributed environment, and improves the reliability and security of the system.

[0089] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0090] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0091] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the hybrid directory management methods for trusted data spaces in the automotive industry described in the above embodiments.

[0092] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0093] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0094] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0095] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0096] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A hybrid directory management method for trusted data spaces in the automotive industry, characterized in that, Based on a hybrid directory system architecture comprising participating nodes, a centralized directory service, and data consumers, the method is characterized by the following steps: S1. Participating nodes collect raw metadata from automotive industry-related business systems and generate metadata objects with feature vectors through standardized preprocessing. S2. The metadata classification module of the participating node performs sensitivity calculation on the metadata object, divides it into public layer, business layer and sensitive layer according to the preset threshold, and generates public layer summary metadata as needed for the business layer or sensitive layer metadata. S3. Participating nodes perform desensitization processing on public layer metadata and public layer summary metadata, and then use a differentiated synchronization strategy to synchronize the desensitized incremental data to the centralized directory service. The centralized directory service stores it in the public metadata pool and updates the global resource index to provide data support for global queries. S4. Data consumers submit query requests carrying DID identity credentials through a unified query interface. The centralized directory service first verifies the legality of the request signature, and then standardizes the legal original query statement into a structured query expression, providing a unified analysis basis for routing decisions. S5. The centralized directory service's query routing engine performs hierarchical analysis on the structured query expression, and calculates the comprehensive cost of the three routing modes (centralized, federated, and hybrid) by combining multiple dimensions such as query complexity, number of nodes, and access permissions, and selects the optimal routing mode with the lowest comprehensive cost. S6. If the optimal routing mode involves access to metadata of the business layer or sensitive layer, the centralized directory service will forward the query request to the corresponding participating node. The participating node will verify the identity and attribute set of the data consumer through the DID identity authentication module, and then perform authorization judgment based on the attribute-based access control policy. Access to the corresponding level of metadata will only be granted to requests that have passed the authorization. S7. Based on the optimal routing mode, collect query results from centralized directory services and / or authorized participating nodes, perform deduplication, sorting, and credibility weighting fusion processing on the multi-source results, and finally return the aggregated unified query results to the data consumer through a unified query interface.

2. The hybrid directory management method for trusted data spaces in the automotive industry according to claim 1, characterized in that: S1 specifically includes, On the participating node side, the local federated catalog collects raw metadata from business systems such as manufacturing, supply chain, and vehicle operation; for unstructured / semi-structured data from different business sources, it performs standardization processing, including field parsing, format unification, noise cleaning, and structure extraction, to generate metadata objects in a unified format; The original metadata set is: (1) The features are structured as follows: (2) in For the participating node numbers, For nodes The collected number One original metadata record, The amount of raw metadata collected; For nodes The original metadata set; This provides functions for metadata standardization and feature extraction, including field parsing, noise reduction, and structured transformation processing steps. raw metadata The resulting feature vector, composed of multiple feature dimensions, is used for subsequent sensitivity calculation and classification.

3. The hybrid directory management method for trusted data spaces in the automotive industry according to claim 2, characterized in that: S2 specifically includes, The metadata classification module within the participating node performs sensitivity assessment on each piece of metadata and determines its classification based on whether it contains publicly available information. The classification is divided into three categories: public layer, business layer, and sensitive layer. The classification is achieved by calculating the sensitivity of metadata feature vectors and making classification decisions automatically based on set thresholds. Sensitivity rating: (3) Grading rules: (4) in Features The One eigenvalue; This is a feature sensitivity mapping function that maps feature values ​​to sensitivity contributions. Sensitivity weighting coefficient is used to reflect the degree of influence of different features on sensitivity; Score the overall sensitivity of metadata; and These serve as the upper and lower bounds of the sensitivity threshold, used to distinguish between the public layer, the business layer, and the sensitive layer. The final classification level for metadata records.

4. The hybrid directory management method for trusted data spaces in the automotive industry according to claim 3, characterized in that: S3 specifically includes, Metadata classified as "public layer" is anonymized and field-filtered, retaining only non-sensitive identifiers and business tags; subsequently, the system adopts a differentiated synchronization strategy, synchronizing only metadata entries that are updated or added compared to the previous synchronization. The synchronized records are entered into the "public metadata pool" of the centralized directory service, and the global resource index is updated; Desensitization and conversion: (5) Differentiated synchronization: (6) in This is a desensitization function used to delete or hash sensitive fields, retaining only publicly available metadata content; This is the de-identified public layer metadata record; For nodes in time The collection of public layer metadata generated at any time; This is the time when the node was last synchronized to the central directory; This refers to the incremental data that needs to be uploaded during this synchronization.

5. The hybrid directory management method for trusted data spaces in the automotive industry according to claim 4, characterized in that: S4 specifically includes, Data users submit query requests through the "unified query interface" along with their DID identity credentials. The centralized directory service first verifies the queryer's signature to ensure that the request comes from a legitimate entity; then it parses the query statement and normalizes it into a structured query expression so that the subsequent routing engine can analyze its requirement level and complexity. Signature verification: (7) Query standardization: (8) in The public key for the data user; A digital signature for the query request by the data user; This is the original query expression; For the user's attribute set; This is a digital signature verification function used to verify the legitimacy of the request subject; To normalize the query function, the input query is converted into a uniform format; This is the normalized standard query expression.

6. The hybrid directory management method for trusted data spaces in the automotive industry according to claim 5, characterized in that: S5 specifically includes, The query routing engine analyzes the normalized query. The system determines whether only metadata needs to be disclosed, or whether it involves business layer or sensitive information. Then, it considers factors such as query complexity, number of nodes, and access permissions to select from three types of query paths: centralized query, federated query, and hybrid query. The optimal route is determined by calculating the cost function to ensure a balance between efficiency, security, and coverage. Cost function: (9) Optimal route selection: (10) in Candidate routing patterns include For central directory search mode, For the federated node query mode, This is a mixed query mode; This is the normalized query expression; In routing mode Execute query below The estimated delay; This refers to the coverage of the routing pattern for query requirements. and These are weight parameters used to control the proportion of efficiency and coverage in the routing process; In the pattern Execute query below The overall cost; The optimal query routing pattern that minimizes cost.

7. The hybrid directory management method for trusted data spaces in the automotive industry according to claim 6, characterized in that: S6 specifically includes, For query requests that require access to metadata in the business layer or sensitive layer, the centralized directory service forwards the query to the relevant participating nodes; the DID identity authentication module on the node side verifies the identity of the query initiator and makes authorization determination based on the local access policy; Only authorized records are returned, thus achieving distributed, fine-grained access control; Access strategy function: (11) Authorization determination: (12) in For resources The access strategy function performs authorization judgment based on attributes; This is a set of attributes for the user, including information such as roles, organizations, and business domain licenses; To match user attributes against a policy, return allow / deny; For participating nodes to record metadata The authorization result; Once authorization is granted, the node returns the corresponding business layer or sensitive layer metadata.

8. The hybrid directory management method for trusted data spaces in the automotive industry according to claim 7, characterized in that: S7 specifically includes, Based on the query route type, the system merges query results from the central directory and multiple participating nodes; performs deduplication, sorting, and credibility weighting operations on records from different sources, and returns the final results to the data user in a unified manner. If a hybrid mode is adopted, the system first performs an initial screening using the central directory, and then performs fine-grained queries and aggregations on the relevant nodes; Federal outcome merging: (13) Mixed mode result aggregation: (14) in For participating nodes The set of query results; This is a collection of query results from multiple participating nodes; This is the collection of query results from the central directory; This is a set union operation used for summarizing federated results; This is the result fusion function in the hybrid mode, which includes deduplication, sorting, and relevance scoring. The final result set is returned to the data user.

9. A hybrid directory management system for a trusted data space in the automotive industry, used to implement the hybrid directory management method for a trusted data space in the automotive industry as described in any one of claims 1-8, characterized in that: It consists of participating nodes, centralized directory services, and data consumers; The participating nodes deploy local federated directories, metadata hierarchical modules, and DID identity authentication modules. The centralized directory service deploys global resource indexes, public metadata pools, query routing engines, and policy decision points. The data consumers configure unified query interfaces and DID identity credentials.

10. A hybrid directory management system for a trusted data space in the automotive industry according to claim 9, characterized in that: Local federated directory: Used to store complete business metadata of the data provider on its local machine. This metadata contains sensitive information and is maintained only within the local node. Metadata classification module: used to automatically classify local business metadata into public layer, business layer and sensitive layer, so as to realize the classification and management of metadata according to sensitivity level; DID identity authentication module: used to verify the identity of the data consumer who initiates the data access request, and to complete access permission authorization and authentication according to preset rules; Global Resource Index: Used to uniformly maintain data resource information and data location information of all participating nodes in the entire network, forming a globally searchable resource catalog; Public metadata pool: used to aggregate and store public layer metadata synchronized from each participating node, does not contain sensitive information, and supports global public query; Query routing engine: intelligently selects the execution path of central query or federated query based on the complexity of the current query request, data distribution and permission conditions; Policy decision point: Used to formulate and execute globally unified access control policies, and to perform permission judgment and policy control on cross-node data access; Unified query interface: Provides a standardized and unified data resource query portal to support data consumers in retrieving and accessing global data resources; DID (Digital Identity Credential): A digital identity identifier used by data consumers to submit identity authentication, request signatures, and verify permissions to the system.

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