Electronic file integrity detection method and device and electronic equipment

Abstract

The invention provides an electronic archive integrity detection method and device and electronic equipment, and belongs to the technical field of archive data management. The method comprises the following steps: acquiring element object information in a formed case, and extracting archive summary information based on the element object information; damage probability information is calculated based on preset feature elements in the element object information, and the archive summary information and the damage probability information are stored in a database in the form of files; determining an archive detection range, and reading archive summary information and damage probability information of the corresponding case from a database based on the archive detection range; sorting the corresponding files according to a preset sorting rule based on the damage probability information; verifying the archive summary information of the corresponding files according to the sequence, and obtaining a verification result; and determining the integrity of the electronic file based on the verification result. The efficiency and the accuracy of integrity detection of the electronic archive are remarkably improved, and the integrity and the reliability of archive data are ensured.

Description

Technical Field

[0001] The present application belongs to the technical field of archive data management, and specifically relates to an electronic archive integrity detection method, device and electronic equipment. Background Art

[0002] With the rapid development of information technology, electronic archives, as a vital vehicle for recording, transmitting, exchanging, utilizing, and sharing information, have gradually become an indispensable part of national archival resources. Electronic archive management involves the collection, maintenance, utilization, and disposal of electronic archives, and one of the key steps is ensuring their integrity. The "four-quality testing" of electronic archives—authenticity, integrity, availability, and security—is a series of verification and assessments of electronic documents or archives to ensure that these archives, in a digital environment, possess the same legal validity, documentary value, and historical value as their paper counterparts.

[0003] The integrity detection scheme of traditional electronic archive systems is often carried out by comparing summaries one by one. This method is inefficient when processing a large number of archive files and cannot detect damaged files in advance. Summary of the Invention

[0004] In order to solve at least one aspect of the technical problems existing in the background technology, the present application provides an electronic file integrity detection method. By pre-calculating the probability of file damage and optimizing the verification order accordingly, the efficiency and accuracy of electronic file integrity detection are significantly improved, ensuring the integrity and reliability of the archive data.

[0005] The technical solutions adopted in this application are:

[0006] The first embodiment of the present application provides a method for detecting the integrity of an electronic file, comprising:

[0007] Obtaining element object information from a formed case, and extracting archive summary information based on the element object information;

[0008] Calculating damage probability information based on preset characteristic elements in the element object information, and saving the archive summary information and the damage probability information in a database in the form of a file;

[0009] Determine a file detection range, and read the file summary information and the damage probability information of the corresponding case from the database based on the file detection range;

[0010] Sorting the corresponding files according to a preset sorting rule based on the damage probability information;

[0011] Verify the archive summary information of the corresponding files in order and obtain the verification results;

[0012] The integrity of the electronic file is determined based on the verification result.

[0013] According to the electronic archive integrity detection method provided by the first embodiment of the present application, element object information, including case bibliographic information, case electronic files, and archive file metadata, is first obtained from established cases. Based on this information, a summary algorithm is used to generate an archive summary, ensuring that the core characteristics of each archive file are quickly and accurately captured, providing a foundation for subsequent steps. Next, a logistic regression algorithm in machine learning is used to analyze preset feature elements such as file size and type to calculate the damage probability. The calculated damage probability information is stored together with the archive summary in a database. This method improves the foresight of potentially damaged files and optimizes the verification sequence for future integrity checks, thereby improving detection efficiency. The archive detection scope is then determined based on the needs of regular comprehensive inspections or routine case updates. Information on relevant archives is extracted from the database in a targeted manner, avoiding unnecessary full inspections, saving resources, and providing more flexible response to specific needs. Subsequently, files are sorted according to damage probability, prioritizing the most likely damaged files. These files are further sorted according to their importance level to accelerate problem detection and facilitate timely remediation measures. Next, the summary of each file is recalculated in sequence according to the sorting order and compared with the summary in the database for verification. Once a mismatch is found, an error message is immediately recorded and the integrity check of the current file is terminated to prevent the occurrence of invalid verification processes and ensure the authenticity and integrity of the file data. Finally, based on the verification results of all files, the overall integrity of the electronic file is comprehensively evaluated to confirm whether the file is intact or record abnormal situations for subsequent processing, providing a clear status view for the file manager, which is conducive to maintaining the long-term security and availability of the file. In summary, the electronic file integrity detection method provided in the embodiment of the first aspect of the present application significantly improves the efficiency and accuracy of electronic file integrity detection by pre-calculating the probability of file damage and optimizing the verification order accordingly, thereby ensuring the integrity and reliability of the file data.

[0014] According to one embodiment of the present application, the step of obtaining element object information from a formed case and extracting archive summary information based on the element object information is as follows:

[0015] The element object information includes case record information, case electronic files and archival file metadata;

[0016] The summary information of the archival record information, the case electronic file and the archival file metadata is extracted using a summary extraction algorithm.

[0017] According to an embodiment of the present application, the calculation of the damage probability information based on the preset characteristic elements in the element object information and the saving of the archive summary information and the damage probability information in the form of a file in a database are specifically as follows:

[0018] The preset characteristic elements include file size, file type and storage type;

[0019] The damage probability information is calculated based on the file size, file type and storage type using a machine learning logistic regression algorithm.

[0020] According to one embodiment of the present application, determining the file detection range and reading the file summary information and the damage probability information of the corresponding case from the database based on the file detection range are specifically as follows:

[0021] Determine the scope of the file detection based on a time plan or event trigger, and the scope of the file detection includes regular comprehensive detection and routine detection of case updates;

[0022] Based on regular comprehensive inspection, the inspection process is automatically started at preset time intervals, and the file summary information and damage probability information of all relevant cases are read from the database;

[0023] Based on routine detection of case updates, when a specific case is updated or changed, the case is marked as pending detection, and only the file summary information and damage probability information of the specific case are extracted from the database.

[0024] According to an embodiment of the present application, the corresponding files are sorted according to a preset sorting rule based on the damage probability information, specifically:

[0025] Sort the corresponding files from high to low according to the probability of damage;

[0026] When the damage probabilities are the same, the files are sorted based on their importance levels.

[0027] According to an embodiment of the present application, the archive summary information of the corresponding files is verified in sequence and the verification result is obtained, specifically:

[0028] For the file to be verified, recalculate its archive summary information and compare it with the archive summary information stored in the database;

[0029] If the comparison result is a match, the verification result is that the file is not damaged; if the comparison result is a mismatch, the verification result is that the file is damaged and the verification failure information is recorded.

[0030] According to one embodiment of the present application, the method further includes:

[0031] An integrity check report is generated based on the verification results of all files. The integrity check report includes verification status information of the files, abnormality information, and abnormality occurrence location information.

[0032] A second embodiment of the present application provides an electronic file integrity detection device, comprising:

[0033] an extraction module adapted to obtain element object information from a formed case and extract archive summary information based on the element object information;

[0034] a calculation module adapted to calculate damage probability information based on preset characteristic elements in the element object information, and save the archive summary information and the damage probability information in a database in the form of a file;

[0035] a reading module, adapted to determine a file detection range, and read the file summary information and the damage probability information of the corresponding case from the database based on the file detection range;

[0036] A sorting module, adapted to sort the corresponding files according to a preset sorting rule based on the damage probability information;

[0037] The verification module is adapted to verify the archive summary information of the corresponding files in sequence and obtain the verification results;

[0038] The determination module is adapted to determine the integrity of the electronic file based on the verification result.

[0039] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the electronic file integrity detection method described in any of the embodiments of the first aspect is implemented.

[0040] An embodiment of the present application also provides a non-volatile computer storage medium having computer executable instructions stored thereon. When the computer program is executed by a processor, the electronic file integrity detection method in any embodiment of the first aspect described above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0042] Figure 1 A flowchart of the electronic file integrity detection method provided in an embodiment of the present application;

[0043] Figure 2A schematic diagram of the structure of an electronic file integrity detection device provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0045] Reference numerals:

[0046] 110, extraction module; 120, calculation module; 130, reading module; 140, sorting module; 150, inspection module; 160, determination module;

[0047] 810 , processor; 820 , communication interface; 830 , memory; 840 , communication bus. DETAILED DESCRIPTION

[0048] In order to more clearly illustrate the overall concept of the present application, a detailed description is given below in an illustrative manner in conjunction with the accompanying drawings.

[0049] The following description sets forth many specific details to facilitate a thorough understanding of the present application. However, the present application may also be implemented in other ways than those described herein, and therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below. It should be noted that the embodiments of the present application and the features of each embodiment may be combined with each other unless there is a conflict.

[0050] In this application, unless otherwise expressly specified and limited, a first feature "above" or "below" a second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0051] like Figure 1 As shown, the first embodiment of the present application provides an electronic file integrity detection method, comprising:

[0052] Step 100: Obtain element object information from the formed case, and extract archive summary information based on the element object information.

[0053] Step 200: Calculate damage probability information based on preset characteristic elements in element object information, and save the archive summary information and damage probability information in the form of a file in a database.

[0054] Step 300: Determine the file detection range, and read the file summary information and damage probability information of the corresponding case from the database based on the file detection range.

[0055] Step 400: Sort the corresponding files according to a preset sorting rule based on the damage probability information.

[0056] Step 500: Verify the archive summary information of the corresponding file in order and obtain the verification result.

[0057] Step 600: Determine the integrity of the electronic file based on the verification result.

[0058] In step 100, element object information is first obtained from the established case, and archival summary information is extracted based on this information. This step involves identifying and extracting key data points from a large number of electronic documents or database records, such as basic information such as file type, creation date, and author, as well as any key case-specific details. This process not only simplifies subsequent data processing but also ensures that the extracted information is highly relevant and accurate.

[0059] By accurately identifying and extracting key information, a solid foundation can be laid for subsequent analysis. Furthermore, this structured data processing approach can significantly improve information retrieval efficiency, enabling users to quickly locate the required information, thereby enhancing work efficiency and service quality.

[0060] In step 200, damage probability information is calculated based on predefined characteristic elements within the element object information. The archive summary and damage probability information are then stored in a database as a file. These "predefined characteristic elements" may include indicators such as file size, modification timestamp, and hash value, used to determine file integrity or tampering. By analyzing these characteristic elements, the system can assess the damage risk level of each archive.

[0061] By quantifying the likelihood of archive damage, institutions can prioritize those files at higher risk of loss or damage, effectively preventing potential data loss. Storing relevant information in a database facilitates long-term monitoring and management, helping to maintain the long-term integrity of archives.

[0062] In step 300, the scope of file inspection is determined. Based on this scope, the corresponding case's file summary information and damage probability information are retrieved from the database. Selecting the appropriate inspection scope is crucial to ensuring inspection effectiveness. Depending on specific needs, it can be set to the entire database, files within a specific time period, or all relevant documents for a specific project. A clear scope definition helps focus resources and conduct detailed inspections on the areas that require the most attention.

[0063] By focusing on a specific set of files, organizations can allocate resources more efficiently and avoid wasting resources on unnecessary full scans. This approach also increases the speed and accuracy of problem detection, allowing for timely action to repair damaged files.

[0064] In step 400, the corresponding files are sorted according to preset sorting rules based on the damage probability information. Sorting rules can be customized based on actual business needs, such as sorting by damage probability from high to low to prioritize files most likely to cause problems, or sorting by importance to prioritize files related to critical business operations. This sorting mechanism helps optimize workflows and make the most efficient use of limited human and technical resources.

[0065] By prioritizing, the team can address the most pressing issues first, reducing losses caused by delays. Furthermore, an organized workflow improves overall work quality and client satisfaction, ensuring that all records are properly managed and protected.

[0066] In step 500, the archive summary information of the corresponding file is verified in sequence and the verification results are obtained. During the verification process, the system compares the original summary information with the current summary information to determine whether the file has maintained its original integrity and authenticity. If inconsistency is found, it indicates that the file may have been modified or damaged.

[0067] The core value of this step lies in providing strong evidence supporting the authenticity and integrity of the document. Accurate verification results are crucial for both preserving evidence in legal cases and managing internal corporate documents. They not only ensure the credibility of the information but also provide a reliable basis for subsequent decision-making.

[0068] Finally, in step 600, the integrity of the electronic archive is determined based on the verification results. Based on the data collected and analyzed in the previous steps, the system makes a final determination: which archives are intact and which are at risk and require further review or repair. This process comprehensively considers multiple factors, such as the probability of damage and verification results, to form a comprehensive assessment conclusion.

[0069] The ultimate goal of this work is to ensure the continued integrity and availability of electronic records throughout their lifecycle. By regularly performing such checks and taking appropriate corrective actions, organizations can significantly reduce the risk of data loss and ensure business continuity and regulatory compliance.

[0070] According to the electronic archive integrity detection method provided by the first embodiment of the present application, element object information, including case bibliographic information, case electronic files, and archive file metadata, is first obtained from established cases. Based on this information, a summary algorithm is used to generate an archive summary, ensuring that the core characteristics of each archive file are quickly and accurately captured, providing a foundation for subsequent steps. Next, a logistic regression algorithm in machine learning is used to analyze preset feature elements such as file size and type to calculate the damage probability. The calculated damage probability information is stored together with the archive summary in a database. This method improves the foresight of potentially damaged files and optimizes the verification sequence for future integrity checks, thereby improving detection efficiency. The archive detection scope is then determined based on the needs of regular comprehensive inspections or routine case updates. Information on relevant archives is extracted from the database in a targeted manner, avoiding unnecessary full inspections, saving resources, and providing more flexible response to specific needs. Subsequently, files are sorted according to damage probability, prioritizing the most likely damaged files. These files are further sorted according to their importance level to accelerate problem detection and facilitate timely remediation measures. Next, the summary of each file is recalculated in sequence according to the sorting order and compared with the summary in the database for verification. Once a mismatch is found, an error message is immediately recorded and the integrity check of the current file is terminated to prevent the occurrence of invalid verification processes and ensure the authenticity and integrity of the file data. Finally, based on the verification results of all files, the overall integrity of the electronic file is comprehensively evaluated to confirm whether the file is intact or record abnormal situations for subsequent processing, providing a clear status view for the file manager, which is conducive to maintaining the long-term security and availability of the file. In summary, the electronic file integrity detection method provided in the embodiment of the first aspect of the present application significantly improves the efficiency and accuracy of electronic file integrity detection by pre-calculating the probability of file damage and optimizing the verification order accordingly, thereby ensuring the integrity and reliability of the file data.

[0071] In some embodiments of the present application, element object information is obtained from a formed case, and archive summary information is extracted based on the element object information, specifically:

[0072] Element object information includes case record information, case electronic files, and archival file metadata;

[0073] Abstract extraction algorithms are used to extract summary information from archival bibliographic information, case electronic files, and archival file metadata. Case bibliographic information typically refers to descriptive data directly related to the case, such as the case number, names of the parties involved, and the case filing date. This information is crucial for identifying and locating specific case records.

[0074] Electronic case files refer to all digital documents generated during the case process, such as indictments, evidence materials, and verdicts. These files not only contain the core content of the case but also serve as one of the main bases for verifying the integrity of the files.

[0075] Archival metadata refers to data about electronic files, including but not limited to the file's creation time, last modification time, file size, and author. Metadata provides important clues about the file's status and helps determine whether the file has been tampered with or lost.

[0076] In order to extract useful archive summary information from the above three types of element object information, a variety of summary extraction algorithms can be used:

[0077] Text summarization technology: For case bibliographic information and electronic case files, we can use text summarization algorithms from natural language processing (NLP) technology, such as extractive summarization based on TF-IDF or generative summarization based on sequence-to-sequence models. This method can automatically extract the most important and representative parts of the document as a summary.

[0078] Metadata analysis tools: For archival metadata, specialized analysis tools are needed to extract relevant information. This may involve writing scripts or using existing record management system APIs to read and parse metadata fields, and then filter out the most valuable parts for assessing archival integrity based on predefined criteria.

[0079] Through this method, the system can effectively extract concise and comprehensive archival summaries from massive amounts of case data. This not only facilitates a quick understanding of the case overview but also lays the foundation for subsequent damage probability calculation, sorting, and verification. Furthermore, high-quality summaries improve data retrieval efficiency, enabling users to quickly locate the information they need within vast amounts of data, significantly enhancing work efficiency and service quality.

[0080] In some embodiments of the present application, damage probability information is calculated based on preset characteristic elements in element object information, and the archive summary information and damage probability information are saved in the database in the form of a file, specifically:

[0081] The preset characteristic elements include file size, file type and storage type;

[0082] The damage probability information is calculated based on file size, file type, and storage type using a machine learning-based logistic regression algorithm.

[0083] File size refers to the amount of storage space a single file takes up. Changes in file size may indicate whether the file has been tampered with or is missing some data.

[0084] File type refers to the format of the file, such as PDF, DOCX, JPEG, etc. Different file types may have different integrity and security requirements.

[0085] Storage type refers to how files are stored, such as on a local hard drive, in the cloud, or through other network storage solutions. Different storage media have different impacts on the security and integrity of files.

[0086] The damage probability information is calculated using a logistic regression algorithm from machine learning. This is a statistical analysis method used to predict the probability of an event occurring. In this context, the logistic regression model estimates the probability of a given file being damaged based on its characteristics (file size, file type, and storage type) based on patterns learned from historical data.

[0087] Data preparation and feature engineering: First, we need to collect a large amount of sample data on file size, file type, and storage type, and label these samples as corrupted. We then perform feature engineering to convert the raw data into a form suitable for processing by the logistic regression model.

[0088] Model training: A logistic regression model is trained using a labeled dataset. The model learns how to predict the output (i.e., the probability of corruption) based on input features (file size, file type, storage type). During training, model parameters are adjusted to minimize prediction error.

[0089] Calculating corruption probability: Once the model is trained, it can be used to calculate the corruption probability of new files. For each file to be evaluated, its relevant feature values are fed into the model, and the model will output an estimated probability that the file is corrupted.

[0090] Finally, the extracted archive summary information and the calculated damage probability information are saved as files in the database. This step ensures that all relevant information can be effectively managed and retrieved by the system, facilitating subsequent steps such as determining archive inspection scope, sorting, and verification.

[0091] This approach not only improves the intelligence level of the archive management system, but also enhances the ability to foresee potential risks, enabling organizations to discover and deal with possible problems at an early stage, thereby ensuring the long-term security and integrity of electronic archives.

[0092] In some embodiments of the present application, the file detection range is determined, and the file summary information and damage probability information of the corresponding case are read from the database based on the file detection range, specifically:

[0093] Determine the scope of file inspection based on time plan or event trigger, including regular comprehensive inspection and routine inspection of case updates;

[0094] Based on regular comprehensive inspection, the inspection process is automatically started at preset time intervals, and the file summary information and damage probability information of all relevant cases are read from the database;

[0095] Based on routine detection of case updates, when a specific case is updated or changed, the case is marked as pending detection, and only the file summary information and damage probability information of the specific case are extracted from the database.

[0096] Scheduled or event-triggered, file inspections can be initiated based on a set schedule or specific event triggers. This approach allows for flexible adjustment of inspection frequency and scope based on actual needs, ensuring timely inspection of important documents while avoiding unnecessary waste of resources.

[0097] Periodic full testing means that the system will automatically perform a complete file testing process according to a predetermined schedule (such as weekly, monthly, etc.). This method is suitable for situations where a regular health check of all files is required.

[0098] During a comprehensive inspection, the system retrieves and processes all relevant case records in the database. This ensures that even files that have not been accessed or modified for a long time are properly maintained and monitored, reducing the risk of data loss.

[0099] During routine case update monitoring, when a specific case is updated or changed, the case is marked as pending for monitoring. For those files that are frequently changed or recently added to the system, a more targeted monitoring method can be adopted. Once a case has been updated or changed, it will be marked as "pending for monitoring" so that it can be reviewed separately.

[0100] Furthermore, only the file summary information and damage probability information of the specific case are extracted from the database, that is, by executing the detection process only for the cases with changes, not only the efficiency can be improved, but also the potential problems can be located more accurately.

[0101] The biggest advantage of regular, comprehensive testing is that it provides a systematic, comprehensive security mechanism, ensuring that every file undergoes a periodic health check. Furthermore, since all files are tested, even the slightest changes are not overlooked, helping to identify problems early and take action.

[0102] Routine case update testing is more efficient and flexible. It can quickly respond to changes in file content and immediately verify the affected areas, reducing the time required to wait for the next scheduled testing cycle. This approach also reduces the demand on system resources because it only focuses on the content that truly needs to be checked, rather than scanning the entire database every time.

[0103] In some embodiments of the present application, the corresponding files are sorted according to a preset sorting rule based on the damage probability information, specifically:

[0104] Sort the corresponding files from high to low according to the probability of damage;

[0105] When the corruption probability is the same, the files are sorted based on their importance level.

[0106] Sort by probability of damage from high to low:

[0107] First, the system sorts all files to be checked from the highest to the lowest probability of damage based on the previously calculated probability of damage for each file. This means that the files that are considered most likely to be damaged or lost are placed at the top of the list.

[0108] This prioritization ensures that when resources are limited, those archives at the highest risk can be focused on and processed first, minimizing the possibility of data loss.

[0109] When the corruption probability is the same, the files are sorted based on their importance level:

[0110] If multiple files have the same probability of being damaged, they are further sorted based on their importance level. The importance level can be determined based on factors such as the criticality of the file content and its relevance to the business.

[0111] For example, in a legal case, certain evidentiary materials may be more important than other supporting documents; in corporate document management, documents involving financial reports or strategic planning may be more important than routine meeting minutes.

[0112] By prioritizing high-risk files, organizations can use their human and technical resources more effectively and avoid wasting too much energy on low-risk or unimportant files. Once a potential problem is discovered, especially for files that are both highly likely to be damaged and extremely important, a quick response can significantly reduce the actual extent of the damage and protect the safety of core assets. This sorting method not only helps identify files that are currently facing the greatest threats, but also provides a basis for long-term risk management strategies, such as strengthening protection measures for specific types of files or conducting regular reviews. By guiding the order of work through reasonable sorting rules, team members can carry out tasks in a more orderly manner, reduce confusion and duplication of work, and thus improve overall work efficiency and service quality.

[0113] In some embodiments of the present application, the archive summary information of the corresponding file is verified in sequence, and the verification result is obtained, specifically:

[0114] For the file to be verified, recalculate its file summary information and compare it with the file summary information stored in the database;

[0115] If the comparison result is a match, the verification result is that the file is not damaged; if the comparison result is a mismatch, the verification result is that the file is damaged and the verification failure information is recorded.

[0116] For each file to be verified, the system needs to recalculate its archive summary information. This usually involves using the same algorithm (such as a hash function) to generate new summary information, which is a compact representation of the file content and can be used to verify whether the file has been modified or damaged. The newly calculated archive summary information is compared with the summary information of the corresponding file previously stored in the database. This process is designed to check whether there are any changes to the file since the last record. If the newly calculated summary information exactly matches the information stored in the database, it means that the file has not changed during this period, and the verification result is "file is not damaged". If the comparison result shows that the two are inconsistent, it means that the file may have been tampered with, damaged, or lost some data. In this case, the verification result is "file is damaged", and relevant information about the verification failure should be recorded.

[0117] By recalculating and validating archive summary information periodically or based on event triggers, changes in file status can be effectively monitored, allowing for the timely identification and resolution of potential data integrity issues. This approach not only detects accidental data corruption but also identifies unauthorized modifications, enhancing the overall document management system's ability to defend against internal and external threats. Once a file is confirmed to be corrupted, the recorded verification failure information helps administrators quickly identify the problem and take appropriate recovery measures, such as restoring the original file from backup.

[0118] In some embodiments of the present application, the method further comprises:

[0119] Generates an integrity check report based on the verification results of all files. The integrity check report includes the file's verification status information, abnormality information, and abnormality location information.

[0120] The report should include the verification status of each file, clearly indicating which files have passed the verification (i.e., not damaged) and which files have failed (i.e., damaged). This clear status identification helps to quickly identify problem files. For those files that fail the verification, the report needs to record the specific circumstances of the anomaly in detail. For example, whether the file has been tampered with, the extent of data loss, whether there is irreversible data damage, etc. This information is crucial for assessing the severity of the problem and formulating a response strategy. In order to locate the problem more accurately, the report should also provide specific location information where the anomaly occurred. This may include specific parts or paragraphs within the file, storage locations in the database, etc. Accurate location information can greatly simplify the repair process and reduce the time cost of finding the source of the problem.

[0121] By generating detailed integrity test reports, organizations can gain a comprehensive understanding of the overall health of their archives. The status of each document is recorded for easy future query and audit. When anomalies occur, the detailed information provided by the report can help management make more informed decisions. For example, whether immediate measures need to be taken to repair damaged documents, or to adjust existing archive management policies to prevent similar problems from recurring. After understanding which documents have problems and the specific nature of the problems, the team can allocate resources more specifically for repair work and avoid blind operations that lead to inefficiency. Demonstrating the integrity and reliability of the system to stakeholders, especially for enterprises or institutions that rely on electronic archives for business operations, regularly publishing integrity test reports can significantly enhance user trust and satisfaction.

[0122] like Figure 2 As shown, the second embodiment of the present application provides an electronic file integrity detection device, comprising:

[0123] The extraction module 110 is adapted to obtain element object information from a formed case and extract archive summary information based on the element object information;

[0124] A calculation module 120 is adapted to calculate damage probability information based on preset characteristic elements in the element object information, and save the archive summary information and the damage probability information in a database in the form of a file;

[0125] The reading module 130 is adapted to determine a file detection range and read file summary information and damage probability information of the corresponding case from the database based on the file detection range;

[0126] A sorting module 140 is adapted to sort the corresponding files according to a preset sorting rule based on the damage probability information;

[0127] The verification module 150 is adapted to verify the archive summary information of the corresponding file in sequence and obtain the verification result;

[0128] The determination module 160 is adapted to determine the integrity of the electronic file based on the verification result.

[0129] The electronic file integrity detection device provided in the second embodiment of the present application can implement the electronic file integrity detection method in any embodiment of the first aspect above, and thus can achieve any technical effect in the above electronic file integrity detection method, which will not be repeated here.

[0130] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the electronic file integrity detection method of any of the embodiments of the first aspect is implemented.

[0131] Figure 3 An example of a physical structure diagram of an electronic device is shown below. Figure 3 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the electronic file integrity detection method in any embodiment of the first aspect described above, which includes:

[0132] Step 100: Obtain element object information from the formed case, and extract archive summary information based on the element object information.

[0133] Step 200: Calculate damage probability information based on preset characteristic elements in element object information, and save the archive summary information and damage probability information in the form of a file in a database.

[0134] Step 300: Determine the file detection range, and read the file summary information and damage probability information of the corresponding case from the database based on the file detection range.

[0135] Step 400: Sort the corresponding files according to a preset sorting rule based on the damage probability information.

[0136] Step 500: Verify the archive summary information of the corresponding file in order and obtain the verification result.

[0137] Step 600: Determine the integrity of the electronic file based on the verification result.

[0138] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0139] In another aspect, the present invention further provides a computer program product, comprising a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the electronic archive integrity detection method provided by the above methods, which includes:

[0140] Step 100: Obtain element object information from the formed case, and extract archive summary information based on the element object information.

[0141] Step 200: Calculate damage probability information based on preset characteristic elements in element object information, and save the archive summary information and damage probability information in the form of a file in a database.

[0142] Step 300: Determine the file detection range, and read the file summary information and damage probability information of the corresponding case from the database based on the file detection range.

[0143] Step 400: Sort the corresponding files according to a preset sorting rule based on the damage probability information.

[0144] Step 500: Verify the archive summary information of the corresponding file in order and obtain the verification result.

[0145] Step 600: Determine the integrity of the electronic file based on the verification result.

[0146] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the electronic archive integrity detection method provided by the above methods is implemented, and the method includes:

[0147] Step 100: Obtain element object information from the formed case, and extract archive summary information based on the element object information.

[0148] Step 200: Calculate damage probability information based on preset characteristic elements in element object information, and save the archive summary information and damage probability information in the form of a file in a database.

[0149] Step 300: Determine the file detection range, and read the file summary information and damage probability information of the corresponding case from the database based on the file detection range.

[0150] Step 400: Sort the corresponding files according to a preset sorting rule based on the damage probability information.

[0151] Step 500: Verify the archive summary information of the corresponding file in order and obtain the verification result.

[0152] Step 600: Determine the integrity of the electronic file based on the verification result.

[0153] Finally, the present invention also provides a non-volatile computer storage medium having computer executable instructions stored thereon. When the computer program is executed by a processor, the method for detecting the integrity of an electronic archive provided by the above methods is implemented. The method includes:

[0154] Step 100: Obtain element object information from the formed case, and extract archive summary information based on the element object information.

[0155] Step 200: Calculate damage probability information based on preset characteristic elements in element object information, and save the archive summary information and damage probability information in the form of a file in a database.

[0156] Step 300: Determine the file detection range, and read the file summary information and damage probability information of the corresponding case from the database based on the file detection range.

[0157] Step 400: Sort the corresponding files according to a preset sorting rule based on the damage probability information.

[0158] Step 500: Verify the archive summary information of the corresponding file in order and obtain the verification result.

[0159] Step 600: Determine the integrity of the electronic file based on the verification result.

[0160] Anything not described in this application can be achieved by adopting or drawing on existing technologies.

[0161] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0162] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included in the protection scope of the present application.

Claims

1. A method for detecting the integrity of an electronic file, characterized in that: include: Obtaining element object information from a formed case, and extracting archive summary information based on the element object information; Calculating damage probability information based on preset characteristic elements in the element object information, and saving the archive summary information and the damage probability information in a database in the form of a file; Determine a file detection range, and read the file summary information and the damage probability information of the corresponding case from the database based on the file detection range; Sorting the corresponding files according to a preset sorting rule based on the damage probability information; Verify the archive summary information of the corresponding files in order and obtain the verification results; The integrity of the electronic file is determined based on the verification result.

2. The electronic file integrity detection method according to claim 1, characterized in that: The step of obtaining element object information from a formed case and extracting archive summary information based on the element object information is as follows: The element object information includes case record information, case electronic files and archival file metadata; The summary information of the archival record information, the case electronic file and the archival file metadata is extracted using a summary extraction algorithm.

3. The electronic file integrity detection method according to claim 1, characterized in that: The calculation of the damage probability information based on the preset characteristic elements in the element object information and the storage of the file summary information and the damage probability information in the database in the form of a file are specifically as follows: The preset characteristic elements include file size, file type and storage type; The damage probability information is calculated based on the file size, file type and storage type using a machine learning logistic regression algorithm.

4. The electronic file integrity detection method according to claim 1, characterized in that: The determining of the file detection range and reading the file summary information and the damage probability information of the corresponding case from the database based on the file detection range are specifically as follows: Determine the scope of the file detection based on a time plan or event trigger, and the scope of the file detection includes regular comprehensive detection and routine detection of case updates; Based on regular comprehensive inspection, the inspection process is automatically started at preset time intervals, and the file summary information and damage probability information of all relevant cases are read from the database; Based on routine detection of case updates, when a specific case is updated or changed, the case is marked as pending detection, and only the file summary information and damage probability information of the specific case are extracted from the database.

5. The electronic file integrity detection method according to claim 1, characterized in that: The corresponding files are sorted according to a preset sorting rule based on the damage probability information, specifically: Sort the corresponding files from high to low according to the probability of damage; When the damage probabilities are the same, the files are sorted based on their importance levels.

6. The electronic file integrity detection method according to claim 5, characterized in that: The file summary information of the corresponding file is verified in order, and the verification result is obtained, specifically: For the file to be verified, recalculate its archive summary information and compare it with the archive summary information stored in the database; If the comparison result is a match, the verification result is that the file is not damaged; if the comparison result is a mismatch, the verification result is that the file is damaged and the verification failure information is recorded.

7. The electronic file integrity detection method according to any one of claims 1 to 6, characterized in that: The method also includes: An integrity check report is generated based on the verification results of all files. The integrity check report includes verification status information of the files, abnormality information, and abnormality occurrence location information.

8. An electronic file integrity detection device, characterized in that: include: an extraction module adapted to obtain element object information from a formed case and extract archive summary information based on the element object information; a calculation module adapted to calculate damage probability information based on preset characteristic elements in the element object information, and save the archive summary information and the damage probability information in a database in the form of a file; a reading module, adapted to determine a file detection range, and read the file summary information and the damage probability information of the corresponding case from the database based on the file detection range; A sorting module, adapted to sort the corresponding files according to a preset sorting rule based on the damage probability information; The verification module is adapted to verify the archive summary information of the corresponding files in sequence and obtain the verification results; The determination module is adapted to determine the integrity of the electronic file based on the verification result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the electronic file integrity detection method according to any one of claims 1 to 7 is implemented.

10. A non-volatile computer storage medium having computer executable instructions stored thereon, characterized in that: When the computer program is executed by a processor, the electronic file integrity detection method according to any one of claims 1 to 7 is implemented.

Images