A method for processing trusted traceability data throughout the life cycle of big data archive management

Abstract

The present invention relates to the technical field of big data archive management, and in particular to a data processing method for trusted traceability throughout the entire life cycle of big data archive management. The method comprises the following steps: obtaining digital object data corresponding to a specific cultural heritage digital archive version or a secure storage reference thereof, and obtaining predetermined content features and key processing environment information of the version; creating a semantic processing process script (SAPS); creating a target cultural heritage digital object content encapsulation (CH-DOCE); encapsulating the digital object data or its secure storage reference, the predetermined content features, and the key processing environment information; and constructing a technical system capable of achieving content authenticity, processing process reproducibility, and trusted traceability of derivative relationships throughout the entire life cycle of cultural heritage digital archives, thereby effectively addressing the information attenuation and traceability problems caused by long-term preservation and complex processing.

Description

Technical Field

[0001] The present invention relates to the field of big data archive management, and in particular to a method for processing trusted traceability data throughout the entire life cycle of big data archive management. Background Art

[0002] The National Cultural Heritage Digital Preservation Center is tasked with the systematic digitization and long-term preservation of a vast collection of precious and fragile ancient texts. This work hinges not only on creating digital copies for research and display, but also, crucially, on ensuring that these digital archives maintain their authenticity and integrity as information equivalents of their original cultural heritage throughout their long lifecycles.

[0003] As digital archives advance, they are entering a phase of diverse applications. Given the need for permanent or extremely long-term preservation (decades to centuries), preserving the original hardware and software environments (modeling software, image libraries, operating systems, database interfaces, and storage media) is virtually impossible. Future researchers face a dual dilemma when accessing archives: First, there is no guarantee that the content of retrieved historical data files is completely consistent with their state at the time of the original log recording (bit-for-bit). Second, the lack of a complete record of the data processing environment (software dependencies, versions, configurations, hardware characteristics, etc.) makes it impossible to reproduce the original analysis process or correctly interpret the data. Currently, it is difficult to establish a strong, atomic, and bidirectionally verifiable binding between digital archive content, processing processes, and the processing environment context, making it difficult to address the information decay risks and environmental changes associated with long-term preservation. Finally, at the end of the digital archive lifecycle, if erroneous or worthless versions generated during certain intermediate processes need to be destroyed in compliance, existing systems must ensure that destruction instructions accurately correspond to specific versions of the target content and record the clear characteristics of the destroyed content. Without a rigorous verification mechanism between destruction records and the actual deleted data content, important versions may be accidentally deleted, or the accuracy and compliance of operations cannot be proven during audits.

[0004] It can be seen from this that when dealing with multimodal, multi-version, and highly correlated precious digital archives, existing technologies are not convenient for constructing a data processing method that can form an atomic, inseparable, and bidirectionally verifiable strong binding relationship between the content characteristics, corresponding operation records, and critical processing logic and environmental context of digital archives at each key life cycle node, so as to efficiently and reliably verify and trace the content authenticity of a specific digital archive version, the compliance and reproducibility of the processing process, and the precise derivative context between different digital objects at any historical moment, thereby effectively dealing with the information attenuation risk brought about by long-term preservation and providing a solid content credibility foundation for future academic research and cultural communication.

[0005] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0006] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a reliable traceability data processing method for big data archive management throughout its life cycle.

[0007] The present invention provides a method for processing trusted traceable data throughout the life cycle of big data archive management, which is applied to a specific cultural heritage digital archive version. The method comprises the following steps:

[0008] Obtaining digital object data corresponding to the specific cultural heritage digital archive version or a securely stored reference thereof, as well as obtaining predetermined content features and key processing environment information of the version;

[0009] Creating a semantic processing script (SAPS), wherein the SAPS records the generation or modification operation of a target cultural heritage digital object content package (CH-DOCE);

[0010] Creating the target cultural heritage digital object content package (CH-DOCE);

[0011] encapsulating the digital object data or a secure storage reference thereof, the predetermined content characteristics, and the key processing environment information;

[0012] The identifier of the SAPS and the first checksum are recorded in the target CH-DOCE.

[0013] The steps of creating a semantic processing script (SAPS) include:

[0014] Recording operation information, the operation information including: the type of the operation, information about a processing tool used for the operation, and key parameter configurations used for the operation;

[0015] If the operation utilizes an input CH-DOCE, recording the identifier of the input CH-DOCE and recording the content feature checksum of the input CH-DOCE obtained before performing the operation;

[0016] Recording the identifier of the target CH-DOCE and recording the content feature checksum of the target CH-DOCE obtained after completing the operation;

[0017] Calculate and record the first checksum of the SAPS itself.

[0018] Specifically, this solution first targets a specific cultural heritage digital archive version and retrieves its core digital object data or its securely stored reference, which forms the foundation of the archive's content. Simultaneously, it obtains the predefined content characteristics of this version, which objectively describe the state of the archive's content, such as image resolution and text encoding specifications. Furthermore, it obtains key information about the processing environment used to generate this version, such as the software tool versions, operating system type, and key configuration parameters. This information is crucial for understanding and reproducing the processing. Next, it creates a semantically defined processing script (SAPS). This SAPS details the specific operations that led to the generation or modification of the target cultural heritage digital object content encapsulation (CH-DOCE). This recorded operation information includes the operation type (e.g., scanning, repair, format conversion), information about the processing tools used (e.g., software name and version number), and the key parameter configurations of the operation. If the current operation relies on one or more existing CH-DOCEs as input, the SAPS records the identifiers of these input CH-DOCEs and obtains and records their current content characteristic checksums before executing the operation. This establishes a clear association between the operation and the input data and verifies the state of the input data. After the operation is completed, the SAPS records the identifier of the generated target CH-DOCE and obtains and records the content feature checksum of the target CH-DOCE. This establishes the association between the operation and the output data and solidifies the state of the output data. The target CH-DOCE is created or updated at this point, encapsulating the digital object data or its secure storage reference, predetermined content features, and key processing environment information. Finally, the SAPS calculates and records its own first checksum, ensuring the integrity of the operation record itself. By recording the corresponding SAPS identifier and the SAPS first checksum in the target CH-DOCE, a bidirectional association and mutual verification is achieved between the archive content package and the processing record that generated it. The entire process integrates and associates content, features, environment, operation records, and multi-level checksums, forming a self-contained, verifiable, and traceable digital archive processing unit.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] By introducing two core data structures, the semantic processing process script (SAPS) and the cultural heritage digital object content encapsulation (CH-DOCE), and tightly binding and encapsulating digital object data, predetermined content features, key processing environment information, operation information, input / output CH-DOCE identifiers and content feature checksums, as well as the SAPS's own checksum, a technical system has been constructed that can achieve content authenticity, reproducibility of processing processes, and trusted traceability of derivative relationships throughout the entire life cycle of cultural heritage digital archives, effectively addressing the information attenuation and traceability problems caused by long-term preservation and complex processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0022] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention.

[0023] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0024] like Figure 1 The illustrated method for processing trusted traceable data throughout the life cycle of big data archive management is applied to a specific cultural heritage digital archive version and includes the following steps:

[0025] Obtaining digital object data corresponding to a specific cultural heritage digital archive version or its securely stored reference, as well as obtaining the predetermined content characteristics and key processing environment information of that version;

[0026] Create a semantic processing script (SAPS) that records the generation or modification operations on a target cultural heritage digital object content package (CH-DOCE);

[0027] Create a target cultural heritage digital object content package (CH-DOCE);

[0028] Encapsulate digital object data or its secure storage reference, predetermined content characteristics, and key processing environment information;

[0029] Record the SAPS identifier and the first checksum into the target CH-DOCE.

[0030] A semantic processing script (SAPS) is a structured record that details the generation or modification operations performed on a cultural heritage digital object content encapsulation (CH-DOCE). It can be implemented using data formats such as XML and JSON, or database records. A cultural heritage digital object content encapsulation (CH-DOCE) is a data structure or container that encapsulates the digital object data of a cultural heritage digital archive, its secure storage reference, predetermined content characteristics, and key processing environment information. It can use a specific file format (such as a ZIP or TAR-based packaging format). Its primary purpose is to atomically bind the digital archive's content, descriptive characteristics, and generation environment into a traceable basic unit. A content feature checksum is a checksum value calculated based on the content feature data or a combination of them within a cultural heritage digital object content encapsulation (CH-DOCE). It can be implemented using a hash algorithm (such as SHA-256) or more complex content analysis-based verification methods. Its primary purpose is to provide a compact and reliable method for verifying whether the content state of a CH-DOCE has changed before and after a specific operation. The first checksum refers to the checksum value calculated for the content of the semantic processing script (SAPS) itself, which can be implemented using a hash algorithm (such as SHA-256). Its main purpose is to ensure the integrity of the SAPS record itself and that it has not been tampered with.

[0031] Specifically, this solution first targets a specific cultural heritage digital archive version and retrieves its core digital object data or its securely stored reference, which forms the foundation of the archive's content. Simultaneously, it obtains the predefined content characteristics of this version, which objectively describe the state of the archive's content, such as image resolution and text encoding specifications. Furthermore, it obtains key information about the processing environment used to generate this version, such as the software tool versions, operating system type, and key configuration parameters. This information is crucial for understanding and reproducing the processing. Next, it creates a semantically defined processing script (SAPS). This SAPS details the specific operations that led to the generation or modification of the target cultural heritage digital object content encapsulation (CH-DOCE). This recorded operation information includes the operation type (e.g., scanning, repair, format conversion), information about the processing tools used (e.g., software name and version number), and the key parameter configurations of the operation. If the current operation relies on one or more existing CH-DOCEs as input, the SAPS records the identifiers of these input CH-DOCEs and obtains and records their current content characteristic checksums before executing the operation. This establishes a clear association between the operation and the input data and verifies the state of the input data. After the operation is completed, the SAPS records the identifier of the generated target CH-DOCE and obtains and records the content feature checksum of the target CH-DOCE. This establishes the association between the operation and the output data and solidifies the state of the output data. The target CH-DOCE is created or updated at this point, encapsulating the digital object data or its secure storage reference, predetermined content features, and key processing environment information. Finally, the SAPS calculates and records its own first checksum, ensuring the integrity of the operation record itself. By recording the corresponding SAPS identifier and the SAPS first checksum in the target CH-DOCE, a bidirectional association and mutual verification is achieved between the archive content package and the processing record that generated it. The entire process integrates and associates content, features, environment, operation records, and multi-level checksums, forming a self-contained, verifiable, and traceable digital archive processing unit.

[0032] This application further proposes that the steps of creating a semantic processing script (SAPS) include:

[0033] Recording operation information, including: the type of operation, information about the processing tools used for the operation, and key parameter configurations used for the operation;

[0034] If the operation utilizes an input CH-DOCE, then record the identifier of the input CH-DOCE and the content feature checksum of the input CH-DOCE obtained before performing the operation;

[0035] Record the identifier of the target CH-DOCE and the content feature checksum of the target CH-DOCE obtained after the operation is completed;

[0036] Calculate and record the first checksum of the SAPS itself.

[0037] The present application further proposes that the steps of recording the identifier of the target CH-DOCE and recording the content feature checksum of the target CH-DOCE obtained after the operation is completed include:

[0038] Obtain the object type of the cultural heritage digital object corresponding to the target CH-DOCE;

[0039] Get the operation type of the create or modify operation performed on the target CH-DOCE;

[0040] Determine one or more content feature dimensions that characterize the content state of the target CH-DOCE after the operation based on the acquired object type and operation type;

[0041] For the determined one or more content feature dimensions, calculating content state representation data corresponding to each content feature dimension;

[0042] The calculated content status representation data corresponding to each content feature dimension, or the combined verification data generated based on the content status representation data corresponding to each content feature dimension, is used as the content feature checksum of the target CH-DOCE;

[0043] Record the identifier of the target CH-DOCE and record the content feature checksum in SAPS.

[0044] The object type refers to the category to which a cultural heritage digital object belongs, such as 2D images, 3D models, structured text, audio, or video. It can be identified using a predefined classification system or custom tags. The operation type refers to the specific processing performed on the target CH-DOCE, such as original acquisition, version restoration, academic annotation, format conversion, data migration, or simplified processing. It can be represented by a standardized operation code or descriptive string. The content feature dimension refers to the specific attribute or aspect used to describe the content state of a cultural heritage digital object. Content state representation data refers to numerical or structured information obtained by analyzing, extracting, or calculating data encapsulated by the CH-DOCE for a specific content feature dimension. Combined check data refers to a single check value or structured check information generated by integrating content state representation data calculated for different content feature dimensions. This can be generated by concatenating multiple content state representation data and calculating a hash value, or by constructing a check structure containing check values ​​for multiple dimensions.

[0045] This solution uses the object type of the cultural heritage digital object and the type of operation performed on the target CH-DOCE to specifically identify one or more content feature dimensions of interest. This is because different object types have different inherent structures and informational foci, and different operation types have different impacts on these foci. This approach avoids blindly calculating all possible feature dimensions, improving efficiency and targeting. For each identified content feature dimension, content state representation data corresponding to each dimension is calculated based on the digital object data encapsulated by the CH-DOCE, predetermined content features, and key processing environment information. This representation data provides a refined description of the CH-DOCE's state after the operation. Finally, this refined representation data, or combined checksum data generated based on it, is used as the content feature checksum of the target CH-DOCE and recorded in the SAPS along with the CH-DOCE identifier. This selective content feature checksum calculation based on object type and operation type enables the checksum to more accurately and meticulously reflect the changes in the CH-DOCE's content state after a specific operation, providing a more reliable basis for subsequent trusted traceability and verification. Compared to simply recording a single overall checksum, this approach can distinguish the impact of different operations on different content dimensions. This refined verification mechanism, combined with the operational information recorded by SAPS, input CH-DOCE checksum and other information, together builds the foundation for trusted traceability of cultural heritage digital archives throughout their life cycle.

[0046] This application further proposes that the steps of determining one or more content feature dimensions characterizing the content state of the target CH-DOCE after the operation based on the acquired object type and operation type include:

[0047] Based on the acquired object type and operation type, a set of initial content feature dimensions is obtained through preset mapping logic;

[0048] Evaluate the completeness of the initial content feature dimension set for characterizing the content state of the target CH-DOCE after the current operation, and obtain the completeness evaluation result;

[0049] If the completeness is insufficient, the initial content feature dimension set is supplemented or adjusted to form an adjusted content feature dimension set, and the adjusted content feature dimension set is used as the final content feature dimension set;

[0050] If the completeness meets the requirements, the initial content feature dimension set is used as the final content feature dimension set;

[0051] The final set of content feature dimensions is adopted as one or more content feature dimensions that characterize the content state of the target CH-DOCE after the operation.

[0052] Based on the acquired object type and operation type, a set of initial content feature dimensions is obtained through preset mapping logic. This preset mapping logic can be a lookup table or rule set that stores the correspondence between object type and operation type combinations and content feature dimension sets. For example, for the "3D model" object type and the "texture map modification" operation type, the preset mapping logic may map to initial content feature dimensions such as "geometry checksum," "texture data checksum," "material properties," and "lighting parameters." This step leverages existing knowledge and experience to provide a quick starting point for determining content feature dimensions.

[0053] Furthermore, based on the known object types and operation types and the degree to which the initial content feature dimension set meets the representation requirements, the completeness of the initial content feature dimension set is evaluated. This evaluation process can check whether the current object types and operation types are clearly defined in the preset mapping logic, or whether the initial content feature dimension set contains the key feature dimensions required for the specific cultural heritage digital object content and operation types.

[0054] If the evaluation results indicate that the initial set of content feature dimensions is insufficiently complete, a manual intervention mechanism is introduced to provide relevant information to users with pre-defined operational permissions, allowing them to supplement or adjust the initial set. Users can select from a pre-set list of possible content feature dimensions for cultural heritage digital objects or customize new dimensions based on their expertise. This manual intervention mechanism leverages the knowledge of domain experts and compensates for the shortcomings of the automated mapping logic. Finally, based on the completeness evaluation results, either the initial set of content feature dimensions or the adjusted set is selected as the final set of content feature dimensions. If the initial set is assessed as complete, it is adopted directly; if it is assessed as incomplete and has been adjusted by the user, the adjusted set is adopted. This ensures that the final set of feature dimensions used to represent the content state of the target CH-DOCE is evaluated and optimized.

[0055] Through the above steps, the method of this application first uses preset mapping logic to quickly determine a set of initial content feature dimensions. Based on this, an evaluation mechanism is used to determine the completeness of this initial set and identify potential deficiencies. When deficiencies are identified, users with specialized knowledge are brought in for manual intervention, allowing them to supplement or adjust the feature dimensions based on their specific circumstances, thereby forming a more comprehensive and accurate final set of content feature dimensions. This final set is used to subsequently calculate content state representation data, such as checksums. This combination of automated mapping, system evaluation, and expert manual intervention overcomes the limitations of relying solely on preset logic, ensuring that the content feature dimensions fully and accurately represent the content state of cultural heritage digital objects after specific operations. This is crucial for the subsequent generation of reliable content feature checksums, which in turn supports the traceability and verification of digital archive version authenticity, reproducibility of processing processes, and derivative relationships within the entire big data archive management system. By ensuring the accuracy of content state representation data, the system can more reliably verify the content consistency of digital archives at different stages of their lifecycle, trace their processing history, and replay the processing process when necessary. This is of great significance for the long-term preservation and trusted use of cultural heritage digital archives.

[0056] This application further proposes evaluating the completeness of the initial content feature dimension set for characterizing the content state of the target CH-DOCE after the current operation, and the steps of obtaining the completeness evaluation result include:

[0057] According to the acquired object type and operation type, a target criterion processing strategy corresponding to the object type and operation type is selected from a preset criterion processing strategy set;

[0058] Apply the target criterion processing strategy to determine the evaluation criterion priority and generate the completeness evaluation results.

[0059] The evaluation criterion priority includes the object type knownness evaluation result, the operation type knownness evaluation result, and the relative importance or decision order of the requirement satisfaction evaluation result.

[0060] In some of the aforementioned embodiments of the present application, when evaluating the completeness of the initial content feature dimension set for representing the content state of the target CH-DOCE after the current operation, an object type knowledge evaluation result can be obtained based on whether the object type is a known type in the preset mapping logic, an operation type knowledge evaluation result can be obtained based on whether the operation type is a known operation in the preset mapping logic, and a representation requirement satisfaction evaluation result can be obtained based on whether the initial content feature dimension set meets the predefined representation requirements for specific cultural heritage digital object content. This can preliminarily determine the applicability of the initial content feature dimension set. However, during its implementation, the three evaluation results of object type knowledge, operation type knowledge, and representation requirement satisfaction may be inconsistent. Simply determining whether the initial content feature dimension set meets the predefined representation requirements cannot effectively solve the problem of how to accurately evaluate the completeness of the initial content feature dimension set when the three are inconsistent.

[0061] In order to solve the above problems, this application proposes a more refined completeness assessment method. Among them, the criterion processing strategy set refers to a pre-defined set of rules or algorithms used to guide how to make comprehensive judgments or decisions when the results of multiple evaluation criteria (such as object type knowledge, operation type knowledge, and representation requirement satisfaction) are inconsistent. The target criterion processing strategy refers to a specific strategy selected from the criterion processing strategy set based on specific context information (such as the object type and operation type currently being processed) and applicable to the current evaluation task. Relative importance or decision order refers to the weight or priority order assigned to different evaluation results when considering multiple evaluation results in a comprehensive manner, in order to guide the final completeness judgment. The criterion processing strategy set can be stored in a database or configuration file, the selection of the target criterion processing strategy can be implemented based on a lookup table or matching algorithm, and the determination of relative importance or decision order can adopt logic such as weighted summation, priority sorting or decision tree.

[0062] This solution addresses the potential for inconsistent assessment results by introducing a criterion processing strategy. When inconsistencies arise between the three assessment results—object type knowledge, operation type knowledge, and representation requirement satisfaction—the system no longer relies solely on a single criterion or fixed rule. Instead, it first obtains the object type and the type of operation being performed on the currently processed cultural heritage digital object. This information serves as context, dynamically selecting a target criterion processing strategy from a pre-set set of criterion processing strategies that best suits the current situation. Different combinations of object type and operation type may correspond to different criterion processing strategies. Through this approach, when inconsistencies arise between the three assessment results—object type knowledge, operation type knowledge, and representation requirement satisfaction—this solution dynamically selects and applies the appropriate criterion processing strategy based on the specific object and operation type, thereby determining the relative importance or decision-making order of the different assessment results. This dynamically adjusted judgment logic generates more accurate and reliable completeness assessment results, effectively resolving the issue of simple judgments failing to address inconsistent assessment results. This improves the accuracy of content feature dimension selection, thereby enhancing the reliability of trusted traceability for cultural heritage digital archives.

[0063] The present application further proposes that when, for an acquired combination of an object type and an operation type, multiple candidate criterion processing strategies that all meet the matching conditions are identified in a preset criterion processing strategy set, and the multiple candidate criterion processing strategies each have different definitions of the relative importance or decision order of the object type knownness evaluation result, the operation type knownness evaluation result, and the characterization requirement satisfaction evaluation result, the following steps are specifically included:

[0064] Obtaining the definition of pre-set cultural heritage protection principles related to the cultural heritage digital object corresponding to the target CH-DOCE;

[0065] Obtaining a setting of a specific task priority associated with a create or modify operation performed on a target CH-DOCE;

[0066] Determining a unique target criterion processing strategy from the plurality of candidate criterion processing strategies based on the obtained definitions of the preset cultural heritage protection principles, the setting of the specific task priorities, and the definition of the evaluation criterion priorities of each candidate criterion processing strategy among the plurality of candidate criterion processing strategies;

[0067] The unique target criterion processing strategy is a target criterion processing strategy selected from a preset criterion processing strategy set and corresponding to the object type and the operation type.

[0068] The definition of pre-defined cultural heritage protection principles refers to the description of predetermined criteria or specifications related to cultural heritage digital objects that guide their processing and preservation. These can be expressed in the form of text descriptions, structured metadata, or ontology models. The setting of specific task priorities refers to the identification of the importance or urgency of currently executing operations involving the generation or modification of the target cultural heritage digital object's content package. These priorities can be set using enumerated values ​​(such as high, medium, and low), numerical weights, or time constraints. Candidate criterion processing strategies refer to multiple alternative rules or algorithms found in a pre-defined set of strategies that can be used to determine the relative importance or decision order of the object type knowledgeability assessment results, operation type knowledgeability assessment results, and requirement satisfaction assessment results for a specific object type and operation type combination. Each candidate criterion processing strategy includes a set of logic that defines how to weigh or prioritize these three assessment results. The object type knowledgeability assessment result refers to the determination of whether the object type of the cultural heritage digital object corresponding to the target cultural heritage digital object is a known type in the pre-defined mapping logic. The result of the operation type knownness assessment refers to the judgment result of whether the operation type of the generation or modification operation performed on the target cultural heritage digital object is a known operation in the preset mapping logic. The result of the representation requirement satisfaction assessment refers to the judgment result of whether the initial content feature dimension set meets the predefined representation requirements for the content of a specific cultural heritage digital object. The definition of relative importance or decision order means that within each candidate criterion processing strategy, the weight, priority or judgment order of the object type knownness assessment result, the operation type knownness assessment result and the representation requirement satisfaction assessment result when generating the completeness assessment result is clearly specified. The target criterion processing strategy refers to the only strategy that is ultimately selected from these candidate criterion processing strategies by comprehensively considering factors such as cultural heritage protection principles and task priorities when there are multiple candidate criterion processing strategies, and is used to determine the relative importance or decision order of the assessment results.

[0069] The present application further proposes that the steps of determining a unique target criterion processing strategy from a plurality of candidate criterion processing strategies include:

[0070] For each candidate criterion processing strategy in the plurality of candidate criterion processing strategies:

[0071] Evaluate the degree of conformity of the evaluation criteria priorities defined in the candidate criteria processing strategy with the definitions of the acquired preset cultural heritage protection principles and with the acquired specific task priorities, and generate principle conformity information and task conformity information;

[0072] Based on the preset decision rules, determine the unique target criterion processing strategy.

[0073] This solution provides a mechanism for selecting a single target strategy from multiple candidate criterion processing strategies. This mechanism comprehensively considers cultural heritage protection principles and specific task priorities, ensuring that the selected strategy meets both macro-level protection objectives and micro-level task requirements. First, each candidate criterion processing strategy is evaluated for its compliance with cultural heritage protection principles, generating principle compliance information. This is achieved by comparing the importance of the evaluation results or the decision-making order defined in the candidate strategy with the pre-defined cultural heritage protection principles, ensuring that the selected strategy does not conflict with the fundamental goals of cultural heritage protection. Second, each candidate criterion processing strategy is evaluated for its compliance with specific task priorities, generating task compliance information. This ensures that the selected strategy effectively serves the current task. For example, if the current task is emergency repair, a strategy that prioritizes efficiency and speed should be selected. Finally, based on pre-defined decision rules, a single target criterion processing strategy is determined by comprehensively considering the principle compliance information, task compliance information, and the relative importance or decision-making order defined by all candidate strategies. This comprehensive evaluation method ensures a comprehensive and objective selection process, avoiding bias due to a single factor. When multiple candidate criterion processing strategies that meet the matching conditions are identified and these strategies have different definitions of the relative importance of the evaluation results or the decision-making order, this scheme provides a systematic decision-making method based on external constraints (principles and tasks) and internal logic (strategy definition). It solves the problem that simple matching or random selection may lead to strategy inapplicability, thereby ensuring the reliability of subsequent completeness evaluation results and improving the credibility of the entire digital archive processing process.

[0074] This application further proposes that the steps of determining a unique target criterion processing strategy based on a preset decision rule include:

[0075] Define decision rules with priorities;

[0076] Applying decision rules with priorities in sequence, screening multiple candidate criterion processing strategies, and obtaining a set of candidate criterion processing strategies that meet the decision rules;

[0077] Sorting the set of candidate criterion processing strategies that meet the decision rule to obtain sorted candidate criterion processing strategies;

[0078] A unique target criterion processing strategy is determined from the sorted candidate criterion processing strategies.

[0079] Among them, the decision-making rules with priority refer to a set of judgment criteria arranged in a predetermined order of importance. These criteria are used to comprehensively consider the principle compliance, task compliance and the logic of the candidate criterion processing strategy to guide the strategy selection process.

[0080] This solution refines the selection of candidate criterion processing strategies by introducing prioritized decision rules and employing a multi-stage process that sequentially applies the rules to screen, ranks the results, and ultimately determines a single target strategy. First, a set of decision rules with varying priorities is defined. These rules are designed to comprehensively consider the alignment of candidate strategies with cultural heritage conservation principles, task priorities, and the strategy's own logic. These rules reflect which factors (e.g., principle alignment, task alignment) are more critical in a given context. The system then sequentially applies these prioritized decision rules to evaluate and screen all candidate criterion processing strategies. High-priority rules are applied first, and strategies that do not meet the high-priority rules are eliminated, resulting in a preliminary set of strategies. The remaining strategies are then further screened using lower-priority rules. This process continues until all rules have been applied, ultimately resulting in a set of candidate criterion processing strategies that meet all priority rules. The strategies in this set perform well at different levels of importance. To further distinguish these strategies, the selected strategies are ranked. Ranking can be based on a comprehensive score (e.g., a weighted score combining principle and task compliance), strategy complexity, or other pre-defined evaluation metrics. The ranking results provide an ordered list of strategies, with the highest-ranked strategies generally considered the preferred options. Finally, a single target criterion processing strategy is determined from the ranked candidate criterion processing strategies. This can be done by simply selecting the highest-ranked strategy, or by incorporating other factors or user input into the final decision among the top strategies. This hierarchical, step-by-step, refined decision-making process more comprehensively utilizes principle and task compliance information, as well as the strategy's inherent logic, effectively addressing potential conflicts between principles and priorities and avoiding suboptimal selections that may result from simplistic evaluation. Through screening and ranking, the system can more accurately identify the criterion processing strategy most appropriate for the current object type and operation type, thereby improving the accuracy of subsequent content feature dimension determination, verification, and calculation, and thus enhancing the reliability of the entire lifecycle trusted traceability data processing method for big data archive management. This approach, combined with the preceding steps, forms a more robust and intelligent strategy selection mechanism, providing a stronger foundation for ensuring the long-term credibility of cultural heritage digital archives.

[0081] The present application further proposes that the steps of determining a unique target criterion processing strategy from the sorted candidate criterion processing strategies include:

[0082] Identify the top-ranked candidate criterion processing strategies as the recommended target criterion processing strategy;

[0083] Provide recommended target criterion processing strategies to users with predetermined operation permissions, and provide object type information, operation type information, principle compliance information, task compliance information, and detailed information of all candidate criterion processing strategies related to the target CH-DOCE;

[0084] Receiving a user's confirmation instruction or adjustment instruction for the proposed target criterion processing strategy;

[0085] If a confirmation instruction is received, the suggested target criterion processing strategy is determined as the only target criterion processing strategy;

[0086] If an adjustment instruction is received, determining a unique target criterion processing strategy from the candidate criterion processing strategies according to the adjustment instruction;

[0087] Record the user's confirmation instructions or adjustment instructions, as well as the corresponding reason information.

[0088] First, the top-ranked candidate criterion processing strategies are identified from the sorted candidate criterion processing strategies as recommended target criterion processing strategies. This utilizes the initial screening results of pre-set rules to provide users with a valuable reference point and reduce their decision-making burden. Then, the recommended target criterion processing strategies are provided to users with pre-defined operational permissions. Information on the object type, operation type, principle compliance, and task compliance associated with the target CH-DOCE, as well as detailed information on all candidate criterion processing strategies, is provided. This enables users to fully understand the decision-making context and the pros and cons of various strategies, enabling them to make more informed decisions. The object type and operation type information allow users to evaluate the applicability of strategies based on specific archival content and processing operations. Principle compliance and task compliance information allow users to understand the degree to which strategies align with cultural heritage conservation principles and specific task priorities. Detailed information on all candidate criterion processing strategies is provided, enabling users to conduct more in-depth comparisons and analyses. Next, the system receives user confirmation or adjustment instructions for the recommended target criterion processing strategies. This empowers users with final decision-making authority, allowing them to confirm or adjust strategies based on their own judgment. If a confirmation instruction is received, the recommended target criterion processing strategy is determined as the sole target criterion processing strategy, indicating that the user agrees with the system's recommended strategy and can adopt it directly. If an adjustment instruction is received, a sole target criterion processing strategy is determined from the candidate criterion processing strategies based on the adjustment instruction. This indicates that the user believes the recommended strategy is suboptimal and requires adjustment. The system then selects the final strategy based on the user's adjustment instruction. Finally, the user's confirmation or adjustment instruction, along with the corresponding justification information, is recorded. This serves to document the user's decision-making process and rationale, providing a basis for subsequent strategy optimization and knowledge accumulation. By recording user justification information, the reasons for the user's strategy adjustment can be analyzed, thereby improving the preset rules and recommendation algorithm. By introducing user interaction, this solution adds the flexibility and expertise of human intervention to automated decision-making. This allows the final criterion processing strategy to better balance the requirements of automated rules, cultural heritage protection principles, task priorities, and practical operational experience. This improves the accuracy and adaptability of the content feature dimension determination process, thereby enhancing the credibility of subsequent content state representation and verification.

[0089] The present application further proposes that the step of evaluating the relative importance or decision order of the object type knownness evaluation result, the operation type knownness evaluation result, and the representation requirement satisfaction evaluation result, defined in the candidate criterion processing strategy, and their conformity with the obtained definitions of the preset cultural heritage protection principles, and generating principle conformity information corresponding to the candidate criterion processing strategy includes:

[0090] Obtaining the relative importance or decision order of the object type knownness evaluation result, the operation type knownness evaluation result, and the representation requirement satisfaction evaluation result, as the strategy raw logic information L_j_raw, as defined in the candidate criterion processing strategy;

[0091] Obtaining various definitions of the preset cultural heritage protection principles as the original definition information D_k_raw of the principles;

[0092] Obtain the importance weight α_k of each preset cultural heritage protection principle in the overall assessment, where k is the principle index and the sum of all α_k is 1;

[0093] Applying a preset policy feature extraction and standardization mapping logic, the policy raw logic information L_j_raw is converted into a policy feature vector F_j, wherein the policy feature vector F_j includes feature components of the relative importance of the object type knownness, the operation type knownness, and the satisfaction of the requirement, as well as other feature components that characterize policy characteristics;

[0094] For each of the preset cultural heritage protection principles, apply the preset principle ideal feature extraction and standardization mapping logic to convert the principle original definition information D_k_raw into a principle ideal feature vector F_ideal_k, where the principle ideal feature vector F_ideal_k has the same feature dimension as the strategy feature vector F_j;

[0095] For each of the preset cultural heritage protection principles, apply the preset similarity calculation logic to calculate the similarity between the strategy feature vector F_j and the principle ideal feature vector F_ideal_k, and obtain the single principle conformity Conformity_jk corresponding to the principle; all the single principle conformities Conformity_jk are weightedly aggregated according to the importance weight α_k of each preset cultural heritage protection principle to obtain the principle conformity information PrincipleConformity_j, where PrincipleConformity_j = Σ[k=1 to N_p] (α_k *Conformity_jk), and N_p is the total number of preset cultural heritage protection principles.

[0096] This solution implements a quantitative assessment of the degree of conformity of candidate criterion processing strategies with pre-defined cultural heritage protection principles through a series of steps. First, the original logical information of the candidate strategy, as well as the original definitions and respective importance weights of the cultural heritage protection principles, are obtained. This serves as the foundational data for quantitative analysis. Next, a pre-defined logic is applied to convert the strategy logic and principle definitions into feature vectors of the same dimension. This conversion enables numerical representation of the previously unstructured information, making it comparable. The strategy feature vector captures how the strategy weighs different evaluation outcomes, while the principle ideal feature vector describes the desired policy characteristics as defined by the principle. Next, for each principle, the similarity between the strategy feature vector and the principle ideal feature vector is calculated to obtain the single-principle conformity score. This quantifies the degree of conformity of the strategy with each specific principle. Finally, these single-principle conformity scores are weighted and aggregated according to the importance weight of each principle to obtain a comprehensive principle conformity score. This comprehensive score reflects the candidate strategy's overall conformity with the entire system of cultural heritage protection principles. In this way, this solution transforms abstract cultural heritage protection principles into calculable and comparable quantitative indicators, enabling an objective assessment of each strategy's performance in adhering to cultural heritage protection requirements when selecting from multiple candidate strategies. This, combined with the steps of identifying multiple candidate strategies and then determining a single strategy in the aforementioned solution, provides a key quantitative basis for the decision-making process, helping to maximize adherence to cultural heritage protection principles while meeting specific task priorities, thereby ensuring the compliance and credibility of the digital archive processing process.

[0097] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. Various changes and improvements are possible without departing from the spirit and scope of the present invention, and such changes and improvements fall within the scope of the invention as claimed.

Claims

1. A method for processing trusted traceable data throughout the life cycle of big data archive management, applied to a specific cultural heritage digital archive version, characterized by: The method comprises the following steps: Obtaining digital object data corresponding to the specific cultural heritage digital archive version or a securely stored reference thereof, as well as obtaining predetermined content features and key processing environment information of the version; Creating a semantic processing script SAPS, wherein the SAPS records the generation or modification operation of a target cultural heritage digital object content package CH-DOCE; Creating the target cultural heritage digital object content package CH-DOCE; encapsulating the digital object data or a secure storage reference thereof, the predetermined content characteristics, and the key processing environment information; Record the SAPS identifier and the first checksum into the target CH-DOCE; The steps of creating a semantic processing script SAPS include: Recording operation information, the operation information including: the type of the operation, information about a processing tool used for the operation, and key parameter configurations used for the operation; If the operation utilizes an input CH-DOCE, recording the identifier of the input CH-DOCE and recording the content feature checksum of the input CH-DOCE obtained before performing the operation; Recording the identifier of the target CH-DOCE and recording the content feature checksum of the target CH-DOCE obtained after completing the operation; Calculate and record a first checksum of the SAPS itself; The step of recording the identifier of the target CH-DOCE and recording the content feature checksum of the target CH-DOCE obtained after completing the operation includes: Obtaining the object type of the cultural heritage digital object corresponding to the target CH-DOCE; Acquire the operation type of the generation or modification operation performed on the target CH-DOCE; Determining, based on the acquired object type and the acquired operation type, one or more content feature dimensions that characterize the content state of the target CH-DOCE after the operation; For the determined one or more content feature dimensions, calculating content state representation data corresponding to each content feature dimension; Using the calculated content status representation data corresponding to each content feature dimension, or the combined verification data generated based on the content status representation data corresponding to each content feature dimension, as the content feature checksum of the target CH-DOCE; The identifier of the target CH-DOCE is recorded, and the content feature checksum is recorded in the SAPS.

2. The method for processing trusted traceable data for the entire life cycle of big data archive management according to claim 1 is characterized in that: The step of determining, based on the acquired object type and the acquired operation type, one or more content feature dimensions characterizing the content state of the target CH-DOCE after the operation comprises: Based on the acquired object type and operation type, a set of initial content feature dimensions is obtained through preset mapping logic; Evaluating the completeness of the initial content feature dimension set for characterizing the content state of the target CH-DOCE after the current operation, and obtaining a completeness evaluation result; If the completeness is insufficient, the initial content feature dimension set is supplemented or adjusted to form an adjusted content feature dimension set, and the adjusted content feature dimension set is used as the final content feature dimension set. If the completeness meets the requirements, the initial content feature dimension set is used as the final content feature dimension set; The final content feature dimension set is adopted as the one or more content feature dimensions characterizing the content state of the target CH-DOCE after the operation.

3. The method for processing trusted traceable data for the entire life cycle of big data archive management according to claim 2 is characterized in that: The step of evaluating the completeness of the initial content feature dimension set for characterizing the content state of the target CH-DOCE after the current operation to obtain a completeness evaluation result includes: Selecting a target criterion processing strategy corresponding to the object type and the operation type from a preset criterion processing strategy set according to the acquired object type and the operation type; The target criterion processing strategy is applied to determine the evaluation criterion priority and generate the completeness evaluation result.

4. The method for processing trusted traceable data for the entire life cycle of big data archive management according to claim 3 is characterized in that: The evaluation criterion priority includes the object type knownness evaluation result, the operation type knownness evaluation result, and the relative importance or decision order of the requirement satisfaction evaluation result.

5. The method for processing trusted traceable data for the entire life cycle of big data archive management according to claim 4 is characterized in that: The step of evaluating the completeness of the initial content feature dimension set for characterizing the content state of the target CH-DOCE after the current operation to obtain a completeness evaluation result further includes the following steps: Obtaining a definition of a preset cultural heritage protection principle and obtaining a setting of a specific task priority associated with the generating or modifying operation performed on the target CH-DOCE; Determining a unique target criterion processing strategy from the plurality of candidate criterion processing strategies based on the obtained definition of the preset cultural heritage protection principle, the setting of the priority of the specific task, and the definition of the priority of the evaluation criterion for each candidate criterion processing strategy in the plurality of candidate criterion processing strategies; The unique target criterion processing strategy is the target criterion processing strategy selected from the preset criterion processing strategy set and corresponding to the object type and the operation type.

6. The method for processing trusted traceable data throughout the life cycle of big data archive management according to claim 5 is characterized in that: The step of determining a unique target criterion processing strategy from a plurality of candidate criterion processing strategies comprises: For each candidate criterion processing strategy in the plurality of candidate criterion processing strategies: Evaluate the priority of the evaluation criteria defined in the candidate criterion processing strategy, the degree of conformity with the definitions of the obtained preset cultural heritage protection principles, and the degree of conformity with the settings of the obtained specific task priorities, and generate principle conformity information and task conformity information; According to the preset decision rules, the unique target criterion processing strategy is determined.

7. The method for processing trusted traceable data for the entire life cycle of big data archive management according to claim 6 is characterized in that: The step of determining the unique target criterion processing strategy according to the preset decision rule includes: Define decision rules with priorities; Applying the decision rules with priorities in sequence to screen the multiple candidate criterion processing strategies to obtain a set of candidate criterion processing strategies that meet the decision rules; Sorting the set of candidate criterion processing strategies that meet the decision rule to obtain sorted candidate criterion processing strategies; The unique target criterion processing strategy is determined from the ranked candidate criterion processing strategies.

8. The method for processing trusted traceable data for the entire life cycle of big data archive management according to claim 7 is characterized in that: The step of determining the unique target criterion processing strategy from the sorted candidate criterion processing strategies comprises: Identify the top-ranked candidate criterion processing strategies as the recommended target criterion processing strategy; Providing the recommended target criterion processing strategy to a user with predetermined operation authority, and providing the object type information, the operation type information, the principle compliance information, the task compliance information, and detailed information of all candidate criterion processing strategies related to the target CH-DOCE; receiving a confirmation instruction or an adjustment instruction from the user regarding the proposed target criterion processing strategy; If the confirmation instruction is received, determining the recommended target criterion processing strategy as the only target criterion processing strategy; If the adjustment instruction is received, determining the unique target criterion processing strategy from the candidate criterion processing strategies according to the adjustment instruction; The confirmation instruction or the adjustment instruction of the user and the corresponding reason information are recorded.

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