Big data archive management full-life-cycle credible traceability data processing method

Abstract

The invention relates to the technical field of big data archive management, in particular to a big data archive management full-life-cycle credible tracing data processing method, which comprises the following steps of: obtaining digital object data or secure storage reference thereof corresponding to a specific cultural heritage digital archive version; obtaining preset content characteristics and key processing environment information of the version; creating a semantic processing procedure script (SAPS); creating a digital object content package (CH-DOCE) of the target cultural heritage; packaging the digital object data or the secure storage reference thereof, the predetermined content feature and the key processing environment information; a technical system capable of realizing full-life-cycle content authenticity, processing process reproducibility and derivative relation credible tracing of the cultural heritage digital archives is constructed, and the effect of effectively dealing with information attenuation and tracing problems caused by long-term storage and complex processing is achieved.

Description

Technical Field

[0001] The present invention relates to the field of big data archive management, and particularly to a method for processing trustworthy traceable data in the whole life cycle of big data archive management. Background Art

[0002] The national-level digital protection center for cultural heritage shoulders the heavy responsibility of systematically digitizing and long-term preserving a large number of precious and fragile ancient books and documents. The core of this work not only lies in creating digital copies for research and display, but more importantly, it is necessary to ensure that these digital archives can always maintain their authenticity and integrity as the equivalent of the information of the original cultural carrier during their long life cycle.

[0003] As the work progresses, digital archives enter the stage of diversified applications. Considering that digital ancient book archives need to be permanently or stored for an extremely long period (decades to centuries), it is almost impossible to maintain the original software and hardware environment (modeling software, image library, operating system, database interface, storage medium) unchanged. Future researchers face a double dilemma when consulting archives: First, it is impossible to ensure that the content of the retrieved historical data file is exactly the same as the state at the moment of the original log record (bit-by-bit matching). 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 an atomic, two-way verifiable strong binding relationship among the content of digital archives, the processing process, and the context of the processing environment, so it is difficult to cope with the information attenuation risk brought by long-term preservation and the challenges of environmental changes. Finally, at the end of the digital archive life cycle, if it is necessary to comply with regulations and destroy certain error or worthless versions generated during intermediate processes, the existing system must ensure that the destruction instruction accurately corresponds to the specific version of the target content and record the clear characteristics of the content to be destroyed. If the verification mechanism between the destruction record and the actual deleted data content is not strict, important versions may be deleted by mistake or it may be impossible to prove the accuracy and compliance of the operation during the audit.

[0004] It can be seen from this that when the existing technology processes multi-modal, multi-version, and highly correlated precious digital archives, it is not convenient to construct a data processing method that can form an atomic, indivisible, and two-way verifiable strong binding relationship among the content characteristics of digital archives at each key life cycle node, the corresponding operation records, and the crucial processing logic and environment context, so as to efficiently and credibly verify and trace the content authenticity, the compliance and reproducibility of the processing process, and the precise derivation context between different digital objects at any historical moment, thereby effectively coping with the information attenuation risk brought by long-term preservation and providing a solid content credibility foundation for future academic research and cultural dissemination.

[0005] In view of the above problems, the existing technology urgently needs to be improved. Summary of the Invention

[0006] The object of the present invention is to solve the drawbacks existing in the prior art, and a data processing method for trustworthy traceability of the entire life cycle of big data file management is proposed.

[0007] The present invention provides a data processing method for trustworthy traceability of the entire life cycle of big data file management, which is applied to a specific digital archive version of cultural heritage. The method includes the following steps: Obtain the digital object data corresponding to the specific digital archive version of cultural heritage or its secure storage reference, and obtain the predetermined content features and key processing environment information of this version; Create a semantic processing procedure script (SAPS), and the SAPS records the generation or modification operations on a target cultural heritage digital object content encapsulation body (CH-DOCE); Create the target cultural heritage digital object content encapsulation body (CH-DOCE); Encapsulate the digital object data or its secure storage reference, the predetermined content features, and the key processing environment information; Record the identifier of the SAPS and the first checksum into the target CH-DOCE.

[0008] The step of creating a semantic processing procedure script (SAPS) includes: Record operation information, and the operation information includes: the type of the operation, the processing tool information for the operation, and the key parameter configuration for the operation; If the operation uses an input CH-DOCE, record the identifier of the input CH-DOCE, and record the content feature checksum of the input CH-DOCE obtained before executing the operation; Record the identifier of the target CH-DOCE, and record the content feature checksum of the target CH-DOCE obtained after completing the operation; Calculate and record the first checksum of the SAPS itself.

[0009] Specifically, in this solution, for a specific version of the digital archive of cultural heritage, the core digital object data or its secure storage reference is first obtained, which is the basis of the archive content. At the same time, the predetermined content features of this version are obtained. These features are objective descriptions of the state of the archive content, such as the resolution of images, the encoding specifications of texts, etc. In addition, the key processing environment information relied on when generating this version is also obtained, such as the software tool version used, the type of operating system, key configuration parameters, etc. These information are crucial for understanding and reproducing the processing process. Then, a Semanticized Processing Procedure Script (SAPS) is created. This SAPS details the specific operations that led to the generation or modification of the current target Cultural Heritage Digital Object Content Encapsulation (CH-DOCE). The recorded operation information includes the operation type (such as scanning, repairing, format conversion), the information of the processing tools used (such as software name, version number), and the key parameter configuration of the operation. If the current operation is based on one or more existing CH-DOCEs as inputs, the SAPS will record the identifiers of these input CH-DOCEs, and obtain and record the current content feature checksums of them before the operation is executed. 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 this target CH-DOCE. This establishes an 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 time, which encapsulates the digital object data or its secure storage reference, the predetermined content features, and the key processing environment information. Finally, the first checksum of the SAPS itself is calculated and recorded, ensuring the integrity of the operation record itself. By recording its corresponding SAPS identifier and the first checksum of the SAPS in the target CH-DOCE, a two-way association and mutual verification between the archive content encapsulation and the processing process record that generated this encapsulation are achieved. The entire process forms a self-contained, verifiable, and traceable digital archive processing unit by integrating and associating the content, features, environment, operation records, and multi-level checksums.

[0010] Compared with the prior art, the present invention has the following beneficial effects: By introducing two core data structures, namely the Semanticized Processing Process Script (SAPS) and the Cultural Heritage Digital Object Content Encapsulation (CH-DOCE), and tightly binding and encapsulating digital object data, predefined content features, key processing environment information, operation information, identifiers and content feature checksums of input / output CH-DOCE, as well as the checksum of SAPS itself, a technical system capable of realizing the authenticity of the content of cultural heritage digital archives throughout the life cycle, the reproducibility of the processing process, and the trustworthy traceability of derivative relationships is constructed, achieving the effect of effectively addressing the problems of information attenuation and traceability brought about by long-term preservation and complex processing. Description of the Drawings

[0011] Figure 1 It is a flowchart of the method of the present invention. Detailed Embodiments

[0012] The following describes in detail the embodiments of the present invention. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0013] The terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless otherwise specifically defined.

[0014] As Figure 1 shown, the trustworthy traceability data processing method for the entire life cycle of big data file management is applied to a specific version of cultural heritage digital archives. The method includes the following steps: Obtain the digital object data corresponding to a specific version of cultural heritage digital archives or its secure storage reference, and obtain the predefined content features and key processing environment information of this version; Create a Semanticized Processing Process Script (SAPS), and SAPS records the generation or modification operations on a target Cultural Heritage Digital Object Content Encapsulation (CH-DOCE); Create a target Cultural Heritage Digital Object Content Encapsulation (CH-DOCE); Encapsulate the digital object data or its secure storage reference, predefined content features, and key processing environment information; Record the identifier and the first checksum of SAPS into the target CH-DOCE.

[0015] Among them, the Semantic Processing Process Script (SAPS) refers to a structured record used to describe in detail the generation or modification operations performed on the Cultural Heritage Digital Object Content Encapsulation (CH-DOCE), which can be implemented using data formats such as XML, JSON, or database records. The Cultural Heritage Digital Object Content Encapsulation (CH-DOCE) refers to a data structure or container used to encapsulate the digital object data of the cultural heritage digital archive, its secure storage reference, predefined content characteristics, and key processing environment information. It can adopt a specific file format (such as a ZIP or TAR-based packaging format), mainly to atomically bind the content, descriptive characteristics, and generation environment of the digital archive to form a traceable basic unit. The Content Feature Checksum refers to the checksum value calculated from the content feature data of the Cultural Heritage Digital Object Content Encapsulation (CH-DOCE) or its combination, which can be implemented using a hashing algorithm (such as SHA-256) or a more complex content analysis-based check method. Its main purpose is to provide a compact and reliable way to verify whether the content state of the CH-DOCE has changed before and after specific operations. The First Checksum refers to the checksum value calculated from the content of the Semantic Processing Process Script (SAPS) itself, which can be implemented using a hashing algorithm (such as SHA-256). Its main purpose is to ensure the integrity and non-tampering of the SAPS record itself.

[0016] Specifically, in this solution, for a specific version of the digital archive of cultural heritage, the core digital object data or its secure storage reference is first obtained, which is the basis of the archive content. At the same time, the predetermined content features of this version are obtained, which are objective descriptions of the content status of the archive, such as the resolution of images, the encoding specifications of texts, etc. In addition, the key processing environment information relied on when generating this version is also obtained, such as the software tool version used, the operating system type, the key configuration parameters, etc. These information are crucial for understanding and reproducing the processing process. Then, a Semanticized Processing Process Script (SAPS) is created. This SAPS details the specific operations that lead to the generation or modification of the current target Cultural Heritage Digital Object Content Encapsulation (CH-DOCE). The recorded operation information includes the operation type (such as scanning, repairing, format conversion), the processing tool information used (such as software name, version number), and the key parameter configuration of the operation. If the current operation is based on one or more existing CH-DOCEs as inputs, the SAPS will record the identifiers of these input CH-DOCEs, and obtain and record the current content feature checksum of them before the operation is executed, which establishes a clear association between the operation and the input data and verifies the status 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, which establishes an association between the operation and the output data and solidifies the status of the output data. The target CH-DOCE is created or updated at this time, and it encapsulates the digital object data or its secure storage reference, the predetermined content features, and the key processing environment information. Finally, the first checksum of the SAPS itself is calculated and recorded to ensure the integrity of the operation record itself. By recording its corresponding SAPS identifier and the first checksum of the SAPS in the target CH-DOCE, a two-way association and mutual verification between the archive content encapsulation and the processing process record that generates this encapsulation are achieved. The entire process forms a self - contained, verifiable, and traceable digital archive processing unit by integrating and associating the content, features, environment, operation records, and multi - level checksums.

[0017] The steps for creating a Semanticized Processing Process Script (SAPS) further proposed in this application include: Recording operation information, where the operation information includes: the type of the operation, the processing tool information used for the operation, and the key parameter configuration used for the operation; If the operation utilizes an input CH-DOCE, recording the identifier of the input CH-DOCE and the content feature checksum of the input CH-DOCE obtained before the operation is executed; Recording the identifier of the target CH-DOCE and the content feature checksum of the target CH-DOCE obtained after the operation is completed; Calculate and record the first checksum of the SAPS itself.

[0018] This application further proposes 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, including: Obtain the object type of the cultural heritage digital object corresponding to the target CH-DOCE; Obtain the operation type of the generation or modification operation performed on the target CH-DOCE; Based on the obtained object type and operation type, determine one or more content feature dimensions representing the content state of the target CH-DOCE after the operation; For the determined one or more content feature dimensions, calculate the content state representation data corresponding to each content feature dimension; Use the calculated content state representation data corresponding to each content feature dimension, or the combined check data generated based on the content state representation data corresponding to each content feature dimension, as the content feature checksum of the target CH-DOCE; Record the identifier of the target CH-DOCE and record the content feature checksum into the SAPS.

[0019] Among them, the object type refers to the category to which the cultural heritage digital object belongs, such as two-dimensional images, three-dimensional models, structured texts, audio, video, etc., which can be identified using a predefined classification system or custom tags. The operation type refers to the specific processing behavior performed on the target CH-DOCE, such as original acquisition, version repair, academic annotation, format conversion, data migration, simplification processing, etc., which can be represented using 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 the cultural heritage digital object. The content state representation data refers to the numerical or structured information obtained by analyzing, extracting, or calculating the data encapsulated in the CH-DOCE for a specific content feature dimension. The combined check data refers to the single check value or structured check information generated by integrating the content state representation data calculated for different content feature dimensions, which can be generated by concatenating multiple content state representation data and then calculating the hash value, or constructing a check structure containing check values for multiple dimensions, etc.

[0020] This solution can specifically determine one or more content feature dimensions that need attention based on the object type of the cultural heritage digital object and the type of operation performed on the target CH-DOCE. This is because different object types have different internal structures and information focuses, and different operation types will have different impacts on these focuses. In this way, blind calculation of all possible feature dimensions can be avoided, improving efficiency and pertinence. For the determined content feature dimensions, based on the digital object data encapsulated in the CH-DOCE, the predetermined content features, and the key processing environment information, calculate the content state representation data corresponding to each dimension. These representation data are refined descriptions of the state of the CH-DOCE after the operation. Finally, these refined representation data, or the combined verification data generated based on them, are used as the content feature checksum of the target CH-DOCE and recorded in the SAPS together with the identifier of the CH-DOCE. This way of selectively calculating the content feature checksum based on the object type and operation type enables the checksum to more accurately and meticulously reflect the content state change of the CH-DOCE after a specific operation, thus providing a more reliable basis for subsequent trustworthy traceability and verification. Compared with only recording an overall checksum, this method can distinguish the impacts of different operations on different content dimensions. This refined verification mechanism, combined with the operation information recorded in the SAPS, the input CH-DOCE checksum and other information, jointly constructs the basis for trustworthy traceability of the entire life cycle of the cultural heritage digital archive.

[0021] The present application further proposes that the steps for determining one or more content feature dimensions representing the content state of the target CH-DOCE after the operation based on the obtained object type and operation type include: Based on the obtained object type and operation type, obtain a set of initial content feature dimensions through a preset mapping logic; Evaluate the completeness of the set of initial content feature dimensions for representing the content state of the target CH-DOCE after the current operation to obtain a completeness evaluation result; If the completeness is insufficient, supplement or adjust the set of initial content feature dimensions to form an adjusted set of content feature dimensions, and use the adjusted set of content feature dimensions as the final set of content feature dimensions; If the completeness meets the requirements, use the set of initial content feature dimensions as the final set of content feature dimensions; Use the final set of content feature dimensions as one or more content feature dimensions representing the content state of the target CH-DOCE after the operation.

[0022] Among them, according to the obtained object type and operation type, a set of initial content feature dimensions are obtained through a preset mapping logic, which can be a lookup table or rule set storing the corresponding relationship between the combination of object type and operation type and the set of content feature dimensions. For example, for the object type of "3D model" and the operation type of "texture map modification", the preset mapping logic may map to initial content feature dimensions such as "geometric structure checksum", "texture data checksum", "material properties", "lighting parameters", etc. This step utilizes existing knowledge and experience to provide a quick starting point for the determination of content feature dimensions.

[0023] Furthermore, based on the knownness of the object type and operation type and the satisfaction degree of the representation requirements of the initial content feature dimension set, the completeness of the initial content feature dimension set is evaluated. This evaluation process can check whether the current object type and operation type are clearly defined in the preset mapping logic, or check whether the initial content feature dimension set contains the key feature dimensions necessary for the content and operation type of this specific cultural heritage digital object.

[0024] If the evaluation result indicates that the completeness of the initial content feature dimension set is insufficient, an artificial intervention mechanism is introduced to provide relevant information to users with predetermined operation permissions, allowing users to supplement or adjust the initial content feature dimension set. Users can select from a preset list containing various possible content feature dimensions of cultural heritage digital objects, or customize new feature dimensions according to their professional knowledge. This artificial intervention mechanism utilizes the knowledge of domain experts to make up for the deficiencies of the automated mapping logic. Finally, according to the completeness evaluation result, the initial content feature dimension set or the adjusted content feature dimension set is selected as the final content feature dimension set. If the initial set is evaluated as complete, it is directly adopted; if it is evaluated as incomplete and adjusted by the user, the adjusted set is adopted. Thus, it is ensured that the set of feature dimensions finally used to represent the content state of the target CH-DOCE is evaluated and optimized.

[0025] Through the above steps, the method of the present application first uses a preset mapping logic to quickly determine an initial set of content feature dimensions. On this basis, an evaluation mechanism is used to judge the completeness of this initial set and identify potential deficiencies. When deficiencies are found, users with professional knowledge are introduced for manual intervention, allowing users to supplement or adjust the feature dimensions according to specific circumstances, thereby forming a more comprehensive and accurate final set of content feature dimensions. This final set is used for subsequent calculation of content state representation data, such as checksums. This way of combining automated mapping, system evaluation, and expert manual intervention overcomes the limitations of simply relying on preset logic, ensuring that the content feature dimensions can fully and accurately represent the content state of cultural heritage digital objects after specific operations. This is crucial for subsequent generation of reliable content feature checksums, and further supports the traceability and verification of the authenticity of digital archive versions, the reproducibility of processing processes, and the derivative relationships in the entire big data archive management system. By ensuring the accuracy of the content state representation data, the system can more reliably verify the content consistency of digital archives at different lifecycle stages, trace their processing history, and reproduce the processing process when needed, which is of great significance for the long-term preservation and trustworthy utilization of cultural heritage digital archives.

[0026] The present application further proposes steps for evaluating the completeness of the initial set of content feature dimensions for representing the content state of the target CH-DOCE after the current operation. The steps for obtaining the completeness evaluation result include: According to the obtained object type and operation type, select the target criterion processing strategy corresponding to the object type and operation type from the preset set of criterion processing strategies; Apply the target criterion processing strategy to determine the priority of the evaluation criterion and generate a completeness evaluation result.

[0027] The priority of the evaluation criterion includes the evaluation result of the knownness of the object type, the evaluation result of the knownness of the operation type, and the relative importance or decision order of the evaluation result of the satisfaction of the representation requirements.

[0028] In some of the above embodiments of the present application, when evaluating the completeness of the initial content feature dimension set for characterizing the content state of the target CH-DOCE after the current operation, an object type knownness evaluation result can be obtained according to whether the object type is a known type in the preset mapping logic, an operation type knownness evaluation result can be obtained according to whether the operation type is a known operation in the preset mapping logic, and a representation requirement satisfaction evaluation result can be obtained according to whether the initial content feature dimension set meets the predefined representation requirements for the content of specific cultural heritage digital objects. In this way, the applicability of the initial content feature dimension set can be preliminarily judged. However, in the process of its implementation, there may be inconsistent situations among the three evaluation results of object type knownness, operation type knownness, and representation requirement satisfaction. Simply judging 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.

[0029] To solve the above problems, the present application proposes a more refined completeness evaluation method. Among them, the criterion processing strategy set refers to a predefined set of rules or algorithms for guiding how to make a comprehensive judgment or decision when the results of multiple evaluation criteria (such as object type knownness, operation type knownness, representation requirement satisfaction) are inconsistent. The target criterion processing strategy refers to the specific strategy selected from the criterion processing strategy set according to specific context information (such as the object type and operation type being processed currently) and applicable to the current evaluation task. The relative importance or decision order refers to the weights or priority orders given to different evaluation results when considering multiple evaluation results comprehensively, for guiding the final completeness judgment. The criterion processing strategy set can be stored in a database or a configuration file. The selection of the target criterion processing strategy can be implemented based on a lookup table or a matching algorithm. The determination of the relative importance or decision order can adopt logics such as weighted summation, priority sorting, or decision trees.

[0030] To address the possible inconsistency in evaluation results, this solution introduces a criterion processing strategy. When there is an inconsistency among the three evaluation results of object type knowability, operation type knowability, and representation requirement satisfaction, the system no longer simply makes a judgment based on a single criterion or fixed rule. Instead, it first obtains the object type of the cultural heritage digital object being processed and the operation type being executed. These two pieces of information are used as context to dynamically select a target criterion processing strategy that best suits the current situation from a preset set of criterion processing strategies. Different combinations of object types and operation types may correspond to different criterion processing strategies. Through the above method, when there is an inconsistency among the three evaluation results of object type knowability, operation type knowability, and representation requirement satisfaction, this solution can dynamically select and apply an appropriate criterion processing strategy based on the specific object type and operation type, thereby determining the relative importance or decision-making order of different evaluation results. Based on this dynamically adjusted judgment logic, a more accurate and reliable completeness evaluation result can be generated, effectively solving the problem that simple judgment cannot handle inconsistent evaluation results, improving the accuracy of content feature dimension selection, and further enhancing the reliability of trustworthy traceability of cultural heritage digital archives.

[0031] This application further proposes that when, for the combination of the obtained object type and operation type, multiple candidate criterion processing strategies that all meet the matching conditions are identified in the preset set of criterion processing strategies, and there are differences in the definitions of the relative importance or decision-making order of each candidate criterion processing strategy for the object type knowability evaluation result, operation type knowability evaluation result, and representation requirement satisfaction evaluation result, the following steps are specifically included: Obtain the definition of the preset cultural heritage protection principles related to the cultural heritage digital object corresponding to the target CH-DOCE; Obtain the setting of the specific task priority associated with the generation or modification operation performed on the target CH-DOCE; Determine a unique target criterion processing strategy from the multiple candidate criterion processing strategies based on the obtained definition of the preset cultural heritage protection principles, the setting of the specific task priority, and the definition of the evaluation criterion priority for each candidate criterion processing strategy among the multiple candidate criterion processing strategies; This unique target criterion processing strategy is the target criterion processing strategy corresponding to the object type and operation type selected from the preset set of criterion processing strategies.

[0032] Among them, the definition of the preset cultural heritage protection principle refers to the description of the pre-determined guidelines or specifications for guiding the processing and preservation of cultural heritage digital objects, which can be represented 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 given to the operations currently being performed that involve the generation or modification of the content encapsulation of the target cultural heritage digital object, which can be set in ways such as enumeration values (such as high, medium, low), numerical weights, or time constraints. The candidate criterion processing strategy refers to a set of alternative rules or algorithms found in the preset strategy set for a specific combination of object types and operation types, which can be used to determine the relative importance or decision order of the evaluation results of object type knowability, operation type knowability, and the satisfaction degree of representation requirements. Each candidate criterion processing strategy contains a set of logics that define how to weigh or prioritize these three evaluation results. The evaluation result of object type knowability refers to the judgment result on whether the object type of the cultural heritage digital object corresponding to the target cultural heritage digital object is a known type in the preset mapping logic. The evaluation result of operation type knowability refers to the judgment result on 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 evaluation result of the satisfaction degree of representation requirements refers to the judgment result on whether the initial content feature dimension set meets the pre-defined 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, it clearly stipulates the weights, priorities, or judgment orders of the evaluation results of object type knowability, operation type knowability, and the satisfaction degree of representation requirements when generating the completeness evaluation result. The target criterion processing strategy refers to the unique strategy finally selected from these candidate strategies to determine the relative importance or decision order of the evaluation results by comprehensively considering factors such as cultural heritage protection principles and task priorities when there are multiple candidate criterion processing strategies.

[0033] This application further proposes that the steps for determining the unique target criterion processing strategy from multiple candidate criterion processing strategies include: For each candidate criterion processing strategy among the multiple candidate criterion processing strategies: Evaluate the compliance degree of the defined evaluation criterion priorities in the candidate criterion processing strategy with the definitions of the preset cultural heritage protection principles and the settings of the specific task priorities obtained, and generate principle compliance information and task compliance information; Determine the unique target criterion processing strategy according to the preset decision rules.

[0034] This solution provides a mechanism for selecting a single target strategy from multiple candidate criterion processing strategies. This mechanism comprehensively considers the principles of cultural heritage protection and specific task priorities to ensure that the selected strategy not only conforms to the macroscopic protection goals but also meets the microscopic task requirements. First, for each candidate criterion processing strategy, its degree of compliance with the principles of cultural heritage protection is evaluated to generate principle compliance information. This is achieved by comparing the importance of evaluation results or decision order defined in the candidate strategy with the preset principles of cultural heritage protection, ensuring that the selected strategy does not conflict with the fundamental goals of cultural heritage protection. Second, the degree of compliance of each candidate criterion processing strategy with specific task priorities is evaluated to generate task compliance information. This ensures that the selected strategy can effectively serve the current task. For example, if the current task is urgent repair, a strategy that pays more attention to efficiency and speed should be selected. Finally, based on the preset decision rules, the principle compliance information, task compliance information of all candidate strategies, and their own defined relative importance or decision order are comprehensively considered to determine the single target criterion processing strategy. This comprehensive evaluation method ensures the comprehensiveness and objectivity of the selection process and avoids biases caused by single factors. When the solution identifies multiple candidate criterion processing strategies that meet the matching conditions and there are differences in the definition of their relative importance or decision order for the evaluation results, it provides a systematic decision-making method based on external constraints (principles and tasks) and internal logic (strategy definition), solves the problem that simple matching or random selection may lead to inapplicable strategies, thus ensuring the reliability of subsequent completeness evaluation results and further enhancing the credibility of the entire digital archive processing process.

[0035] This application further proposes that the steps for determining a single target criterion processing strategy according to the preset decision rules include: Define decision rules with priorities; Apply the decision rules with priorities to screen multiple candidate criterion processing strategies in sequence to obtain a set of candidate criterion processing strategies that meet the decision rules; Sort the set of candidate criterion processing strategies that meet the decision rules to obtain the sorted candidate criterion processing strategies; Determine the single target criterion processing strategy from the sorted candidate criterion processing strategies.

[0036] Among them, the decision rules with priorities refer to a set of judgment criteria arranged in a predetermined order of importance, which are used to comprehensively consider the principle compliance, task compliance, and the internal logic of the strategy of candidate criterion processing strategies to guide the strategy selection process.

[0037] This solution refines the selection of candidate criterion processing strategies by introducing decision rules with priorities and adopting a multi-stage process of applying the rules sequentially for screening, sorting the screening results, and finally determining a unique target strategy. First, a set of decision rules with different priorities are defined. These rules are designed to comprehensively consider the matching degree of candidate strategies with the principles of cultural heritage protection, task priorities, and the logic of the strategies themselves. These rules can reflect which factors (e.g., principle compliance, task compliance) are more critical in a specific context. Subsequently, the system sequentially applies these decision rules with priorities to evaluate and screen all candidate criterion processing strategies. The high-priority rules are applied first, and the strategies that do not meet the high-priority rules are excluded, resulting in a preliminary set of strategies. Then, the remaining strategies are further screened by applying the sub-priority rules. This process continues until all rules are applied, and finally, a set of candidate criterion processing strategies that meet all priority rules is obtained. The strategies in this set perform well at different importance levels. To further distinguish the advantages and disadvantages of these strategies, the screened set of strategies is sorted. The sorting can be based on a comprehensive score (e.g., a weighted score combining principle compliance and task compliance), the complexity of the strategy, or other predefined evaluation metrics. The sorting result provides an ordered list of strategies, and the strategies ranked higher are generally considered better choices. Finally, the unique target criterion processing strategy is determined from the sorted candidate criterion processing strategies. This can simply select the strategy with the highest rank, or among the top-ranked strategies, make a final decision by combining other factors or user input. This hierarchical and step-by-step decision-making process can make more comprehensive use of principle compliance information, task compliance information, and the logic of the strategies themselves, effectively handle potential conflicts between principles and priorities, and avoid suboptimal choices that may result from simple evaluations. Through screening and sorting, the system can more accurately identify the criterion processing strategy most suitable for the current object type and operation type, thereby improving the accuracy of subsequent content feature dimension determination and checksum calculation, and further enhancing the reliability of the entire big data file management full-life-cycle trustworthy traceability data processing method. This method, combined with the previous steps, forms a more robust and intelligent strategy selection mechanism, providing a more solid foundation for ensuring the long-term trustworthiness of cultural heritage digital archives.

[0038] The steps for further determining the unique target criterion processing strategy from the sorted candidate criterion processing strategies proposed in this application include: Identifying the strategies ranked higher in the sorted candidate criterion processing strategies as the recommended target criterion processing strategies; Provide a recommended target criterion processing strategy to users with predetermined operation permissions, and provide object type information, operation type information, principle compliance information, task compliance information related to the target CH-DOCE, as well as detailed information on all candidate criterion processing strategies; Receive a confirmation instruction or adjustment instruction from the user for the recommended target criterion processing strategy; If a confirmation instruction is received, determine the recommended target criterion processing strategy as the only target criterion processing strategy; If an adjustment instruction is received, determine the only target criterion processing strategy from the candidate criterion processing strategies according to the adjustment instruction; Record the user's confirmation instruction or adjustment instruction, as well as the corresponding reason information.

[0039] 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.

[0040] 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: Obtain the relative importance or decision-making order of the knownness evaluation result of the object type, the knownness evaluation result of the operation type, and the satisfaction evaluation result of the representation requirement defined in the candidate criterion processing strategy as the original logical information L_j_raw of the strategy; Obtain the definitions of each item of the preset cultural heritage protection principle as the original definition information D_k_raw of the principle; Obtain the importance weight α_k of each preset cultural heritage protection principle in the overall evaluation, where k is the principle index and the sum of all α_k is 1; Apply the preset strategy feature extraction and standardization mapping logic to convert the original logical information L_j_raw of the strategy into a strategy feature vector F_j. The strategy feature vector F_j includes relative importance feature components of the knownness of the object type, the knownness of the operation type, and the satisfaction of the representation requirement, as well as other feature components representing the characteristics of the strategy; For each of the preset cultural heritage protection principles, apply the preset principle ideal feature extraction and standardization mapping logic to convert the original definition information D_k_raw of the principle into a principle ideal feature vector F_ideal_k. The principle ideal feature vector F_ideal_k has the same feature dimension as the strategy feature vector F_j; 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 to obtain the single principle conformity Conformity_jk corresponding to the principle; aggregate all the single principle conformities Conformity_jk 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.

[0041] Through a series of steps, this solution realizes the quantitative evaluation of the degree of compliance between the candidate criterion processing strategy and the preset cultural heritage protection principles. First, obtain the original logical information of the candidate strategy, the original definitions of the cultural heritage protection principles, and their respective importance weights. These are the basic data for quantitative analysis. Next, apply the preset logic to convert the strategy logic and the principle definitions into feature vectors with the same dimension respectively. This conversion enables the originally unstructured information to be numerically represented, thus making it comparable. The strategy feature vector captures the characteristics of how the strategy weighs different evaluation results, while the principle ideal feature vector depicts the characteristics of the strategy expected by the principle. Subsequently, for each principle, calculate the similarity between the strategy feature vector and the ideal feature vector of this principle to obtain the single-principle compliance degree. This quantifies the degree of fit between the strategy and each specific principle. Finally, weight and aggregate these single-principle compliance degrees according to the importance weights of each principle to obtain a comprehensive principle compliance degree information. This comprehensive value reflects the overall compliance degree of the candidate strategy with the entire system of cultural heritage protection principles. In this way, this solution transforms the abstract cultural heritage protection principles into computable and comparable quantitative indicators, enabling an objective evaluation of the performance of each strategy in following the cultural heritage protection requirements when selecting among multiple candidate strategies. This, combined with the steps of identifying multiple candidate strategies and needing to determine a single strategy in the foregoing solution, provides a key quantitative basis for the decision-making process, helps to maximize compliance with the cultural heritage protection principles while meeting specific task priorities, thereby ensuring the compliance and credibility of the digital archive processing process.

[0042] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. A method for processing trustworthy traceable data in the entire life cycle of big data file management, which is applied to a specific digital file version of cultural heritage, is characterized in that, The method includes the following steps: Obtain the digital object data corresponding to the specific cultural heritage digital archive version or its secure storage reference, and obtain the predetermined content features and key processing environment information of this version; Create a semantic processing procedure script (SAPS), and the SAPS records the generation or modification operations on a target cultural heritage digital object content encapsulation body (CH-DOCE); Create the target cultural heritage digital object content encapsulation body (CH-DOCE); Encapsulate the digital object data or its secure storage reference, the predetermined content features, and the key processing environment information; Record the identifier of the SAPS and the first checksum into the target CH-DOCE.

2. The method for processing trustworthy traceable data in the whole life cycle of big data file management according to claim 1, wherein The step of creating a semantic processing procedure script (SAPS) includes: Record operation information, and the operation information includes: the type of the operation, the processing tool information for the operation, and the key parameter configuration for the operation; If the operation utilizes an input CH-DOCE, record the identifier of the input CH-DOCE, and record the content feature checksum of the input CH-DOCE obtained before executing the operation; Record the identifier of the target CH-DOCE, and record the content feature checksum of the target CH-DOCE obtained after completing the operation; Calculate and record the first checksum of the SAPS itself.

3. The method for processing trustworthy traceable data in the whole life cycle of big data file management according to claim 2, wherein, 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: Obtain the object type of the cultural heritage digital object corresponding to the target CH-DOCE; Obtain the operation type of the generation or modification operation performed on the target CH-DOCE; According to the obtained object type and operation type, determine one or more content feature dimensions representing the content state of the target CH-DOCE after the operation; For the determined one or more content feature dimensions, calculate the content state representation data corresponding to each content feature dimension; Use the calculated content state representation data corresponding to each content feature dimension, or the combined check data generated based on the content state representation data corresponding to each content feature dimension, as the content feature checksum of the target CH-DOCE; Record the identifier of the target CH-DOCE, and record the content feature checksum into the SAPS.

4. The method for processing trustworthy traceable data in the whole life cycle of big data file management according to claim 3, wherein The step of determining one or more content feature dimensions representing the content state of the target CH-DOCE after the operation according to the obtained object type and operation type includes: According to the obtained object type and operation type, obtain a set of initial content feature dimensions through a preset mapping logic; Evaluate the completeness of the set of initial content feature dimensions for representing the content state of the target CH-DOCE after the current operation, and obtain a completeness evaluation result; If the completeness is insufficient, supplement or adjust the initial content feature dimension set to form an adjusted content feature dimension set, and use the adjusted content feature dimension set as the final content feature dimension set. If the completeness meets the requirements, use the initial content feature dimension set as the final content feature dimension set; Use the final content feature dimension set as one or more content feature dimensions representing the content state of the target CH-DOCE after the operation.

5. The method for processing trustworthy traceable data in the whole life cycle of big data file management according to claim 4, characterized in that, The step of evaluating the completeness of the initial content feature dimension set for representing the content state of the target CH-DOCE after the current operation to obtain a completeness evaluation result includes: According to the obtained object type and operation type, select a target criterion processing strategy corresponding to the object type and operation type from a preset set of criterion processing strategies; Apply the target criterion processing strategy to determine the evaluation criterion priority and generate the completeness evaluation result.

6. The method for processing trustworthy traceable data in the whole life cycle of big data file management according to claim 5, wherein, The evaluation criterion priority includes the object type knowability evaluation result, the operation type knowability evaluation result, and the relative importance or decision order of the representation requirement satisfaction evaluation result.

7. The method for processing trustworthy traceable data in the whole life cycle of big data file management according to claim 6, wherein The step of evaluating the completeness of the initial content feature dimension set for representing the content state of the target CH-DOCE after the current operation to obtain a completeness evaluation result further includes the following steps: Obtain the definition of the preset cultural heritage protection principle and the setting of the specific task priority associated with the generation or modification operation performed on the target CH-DOCE; According to the obtained definition of the preset cultural heritage protection principle, the setting of the specific task priority, and the definition of the evaluation criterion priority for each candidate criterion processing strategy in a plurality of candidate criterion processing strategies, determine a unique target criterion processing strategy from the plurality of candidate criterion processing strategies; This unique target criterion processing strategy is the target criterion processing strategy corresponding to the object type and operation type selected from the preset set of criterion processing strategies.

8. The method for processing trustworthy traceable data in the whole life cycle of big data file management according to claim 7, characterized in that, The step of determining a unique target criterion processing strategy from the plurality of candidate criterion processing strategies includes: For each candidate criterion processing strategy in the plurality of candidate criterion processing strategies: Evaluate the degree of compliance of the evaluation criterion priority defined in the candidate criterion processing strategy with the definitions of the obtained preset cultural heritage protection principle and the settings of the obtained specific task priority, and generate principle compliance information and task compliance information; Determine the unique target criterion processing strategy according to a preset decision rule.

9. The method for processing trustworthy traceable data in the whole life cycle of big data file management according to claim 8, wherein The step of determining the unique target criterion processing strategy according to a preset decision rule includes: Define a decision rule with priority; Apply the decision rule with priority to screen the plurality of candidate criterion processing strategies in turn to obtain a set of candidate criterion processing strategies that meet the decision rule; Sort the set of candidate criterion processing strategies that meet the decision rule to obtain the sorted candidate criterion processing strategies; Determine the unique target criterion processing strategy from the sorted candidate criterion processing strategies.

10. The method for processing trustworthy traceable data in the whole life cycle of big data file management according to claim 9, wherein, The step of determining the unique target criterion processing strategy from the sorted candidate criterion processing strategies includes: Identify the strategies ranked higher among the sorted candidate criterion processing strategies as the proposed target criterion processing strategies; Provide the proposed target criterion processing strategies to a user with a predetermined operation authority, and provide the object type information, the operation type information, the principle compliance information, the task compliance information related to the target CH-DOCE, and the detailed information of all the candidate criterion processing strategies; Receive the confirmation instruction or adjustment instruction of the user for the proposed target criterion processing strategies; If the confirmation instruction is received, determine the proposed target criterion processing strategy as the unique target criterion processing strategy; If the adjustment instruction is received, determine the unique target criterion processing strategy from the candidate criterion processing strategies according to the adjustment instruction; Record the confirmation instruction or the adjustment instruction of the user, and the corresponding reason information.

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