Intelligent system for installation procedure file change tracking and recovery

Abstract

The application discloses an intelligent system based on installation process file change tracking and recovery, which is applied to a storage system, wherein files with file index information modified based on file change and deletion instructions are stored in the storage system, and deletion records corresponding to the file change and deletion instructions are saved in a change and deletion list, the change and deletion records include first file index information obtained after modification of the file index information, and the system comprises a request receiving module for receiving a file recovery request. The application can automatically recover the mistakenly deleted files by adding a mistakenly deleted file recovery command, and can check the second check value of the first compressed file by using the first check value. When the first check value and the second check value are the same, the first compressed file is migrated and decompressed to a target path to obtain the mistakenly deleted file, and the mistakenly deleted file can be accurately recovered.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an intelligent system based on installation process file change tracking and recovery. Background Technology

[0002] In today's digital age, data volume is exploding, and the scale and complexity of storage systems are constantly increasing. Whether it's the massive amounts of business data generated in enterprise operations or the large-scale experimental data from research institutions, both place extremely high demands on the security and reliability of storage systems. However, current database management faces many serious challenges in dealing with the problem of accidental file deletion.

[0003] Traditional methods of restoring file data from a backup database are not only time-consuming and computationally resource-intensive, but also rely on complex and fragile backup strategies. In practice, backup data may become unusable due to errors during the backup process or storage media failures, causing the recovery process to fail. Furthermore, this method requires specialized database administrators with deep technical knowledge and extensive experience to ensure a smooth recovery process, which undoubtedly increases labor costs and operational risks.

[0004] The method of resetting system records also has serious flaws. This approach easily leads to data inconsistency, resulting in partial data loss or corruption, and cannot guarantee the accurate recovery of the entire file table. With the increasing diversity of storage architectures, such as the widespread application of new architectures like distributed storage and cloud storage, traditional recovery mechanisms are ill-suited to these complex environments. In distributed storage, files may be scattered across multiple nodes, making it difficult for traditional recovery methods to quickly locate and integrate this dispersed data. In cloud storage, due to the multi-tenant nature of data and the complexity of the network environment, the efficiency and reliability of traditional recovery mechanisms are significantly reduced, severely impacting service reliability and the timeliness of data recovery.

[0005] Therefore, developing a system that can adapt to various storage architectures and has efficient, intelligent, and accurate data recovery capabilities has become a key issue that urgently needs to be addressed in the field of data processing. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent system based on the tracking and recovery of changes to installation process files, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent system for tracking and restoring file changes during the installation process, applied to a storage system. The storage system stores files whose file index information has been modified based on file change and deletion instructions. Deletion records corresponding to the file change and deletion instructions are stored in a change and deletion list. The change and deletion records include first file index information obtained after modifying the file index information. The system includes:

[0008] The request receiving module is used to receive file recovery requests, wherein the file recovery request includes target description information of the file to be recovered, and the target description information includes file name, creation time, modification time, file size, file type, access control list, and unique identifier of the file in the storage system;

[0009] The index acquisition module is used to obtain the first target file index information in the first target deletion record corresponding to the file to be recovered from the deletion list according to the target description information, wherein the first target deletion record includes the target description information;

[0010] The file recovery module is used to modify the index information of the first target file so as to achieve file recovery by redirecting the file to be recovered. The redirection is based on the principles of distributed storage and parallel computing, integrating file fragments stored on different storage nodes or storage media.

[0011] Preferably, it also includes a multi-version accidental deleted file recovery module, the module comprising:

[0012] The information parsing and verification value generation unit is used to obtain the file information to be recovered from the file recovery command when a file recovery command for accidentally deleted is received, and to generate a multi-dimensional first verification value based on the file information to be recovered. The file information to be recovered includes the file name, version information, and access permission history. The multi-dimensional first verification value is generated based on a cryptographic hash algorithm and multi-dimensional data fusion technology.

[0013] The time determination unit is used to determine the latest deletion time corresponding to the name of the file to be recovered in the first log as the target deletion time. The time determination is based on a timestamp analysis algorithm and takes into account user needs, system policies or file importance levels.

[0014] The compressed file location unit is used to determine, from multiple backup paths corresponding to the target deletion time, a first compressed file corresponding to the name of the file to be recovered and a multi-dimensional second verification value corresponding to the first compressed file. The location is based on a distributed search and intelligent matching algorithm, which is adapted to distributed storage and cloud storage environments.

[0015] The verification matching and recovery unit is used to migrate and decompress the first compressed file to the target path when the first verification value and the second verification value are the same, thereby obtaining the accidentally deleted file;

[0016] The filename determination unit is used to obtain the first filename corresponding to each file stored in the target path, and determine the filename of the mistakenly deleted file based on the first filename and the target filename, combined with file version information, modification history and user usage habits.

[0017] Preferably, it also includes an adaptive database model building module, the module comprising:

[0018] The statement extraction unit is used to obtain data deletion statements from data query statements. The data deletion statements include deleting file data. The deleted file data includes file history name, file history content, file identifier, second storage identifier, access control list, creator and modifier information, and security attributes of different storage stages.

[0019] The object extraction unit is used to extract objects based on the file identifier and a dynamically updated preset object table to obtain an initial file object. The preset object table is stored and managed based on a distributed hash table (DHT) and blockchain technology.

[0020] A status detection unit is used to perform status detection on the initial file object to obtain the object processing status, which includes processed status, partially processed status, pending processing status and abnormal status.

[0021] The semantic analysis and content determination unit is used to perform deep semantic analysis and knowledge graph construction on the historical content of the file when the object is in the processed state, and extract key information as candidate file content.

[0022] The data recombination and storage unit is used to obtain candidate file data based on the candidate file name and the semantically analyzed candidate file content, using data recombination algorithms and knowledge graph fusion technology, and to store the candidate file data in an encrypted second database model;

[0023] The data combination and model building unit is used to combine and encrypt data based on multi-dimensional information such as file history name, file identifier, second storage identifier, candidate file name, file access control list, and security attributes to obtain initial file data. Based on the initial file data, the unit dynamically builds and optimizes the first database model using machine learning algorithms and adaptive optimization techniques.

[0024] Preferably, when the state detection unit detects that the object processing state is unprocessed, the initial file object is used as a candidate file object. If the candidate file object and the preset object type are not the same, the candidate file object is deleted.

[0025] Preferably, it also includes an intelligent conflict resolution module, which is used for:

[0026] If a file with the same name as the target file exists, a personalized first confirmation prompt is output by analyzing multi-dimensional information such as file metadata, content similarity, access history, and user habits. The first confirmation prompt is used to indicate whether to overwrite the file corresponding to the first file name with the accidentally deleted file.

[0027] Upon receiving an overwrite instruction, a deep comparison and difference analysis are performed on the original file and the mistakenly deleted file. The difference information is recorded in the difference log. The file corresponding to the first file name that is the same as the target file name is replaced with the mistakenly deleted file. The detailed file name of the mistakenly deleted file is output, including version number, modification time, file size, file type and file unique identifier.

[0028] Upon receiving a non-overwrite instruction, the first file name, which is the same as the target file name, is renamed using a renaming algorithm to include information such as timestamp, version number, file type, and file creator. The mistakenly deleted file is then renamed to the target file name, and the relevant file index and metadata are updated.

[0029] Preferably, it also includes an enhanced file deletion preprocessing module, which is used for:

[0030] Upon receiving a file deletion command, the file information to be deleted is obtained from the file deletion command. The file information to be deleted includes the storage path, file type, access permissions, file creation time and modification time, file owner, and file dependencies in the storage system.

[0031] A deep analysis of the storage path is performed, and machine learning algorithms and risk assessment models are used to identify sensitive information and potential risks within it.

[0032] The file to be deleted is compressed using an efficient encryption compression algorithm to obtain a first compressed file, and a secure second verification value is generated using an encryption hash algorithm.

[0033] The first compressed file and the second verification value are securely migrated to the distributed first backup path, and the detailed command information corresponding to the file deletion command, including the name of the file to be deleted, the deletion time, the operating user, the operating device, the reason for the operation, and the original storage path of the file, is written into the encrypted first log.

[0034] Preferably, it also includes a multi-level confirmation deletion module, which is used for:

[0035] If the storage path contains preset sensitive information, a detailed second confirmation prompt message is output. The second confirmation prompt message not only asks the user whether to delete, but also provides a detailed preview of the file's contents, an assessment of the file's importance, a warning of potential risks, and suggestions for possible alternative operations.

[0036] Upon receiving the first confirmation deletion command, a third confirmation prompt message is output, requiring the user to enter a specific confirmation code or perform biometric verification such as fingerprint, facial recognition, or iris recognition.

[0037] Upon receiving a second confirmation deletion command and passing biometric verification, the file to be deleted is securely retrieved from the storage path.

[0038] Preferably, it also includes an intelligent storage management module, which is used for:

[0039] By utilizing machine learning algorithms and big data analytics, the preset time and duration are dynamically adjusted based on multiple factors such as file access frequency, importance, storage cost, and data lifecycle.

[0040] By monitoring the storage status of the first backup path in real time, including information such as storage capacity, number of files, and file access frequency, an intelligent cleanup mechanism is automatically activated when storage space is insufficient. The mechanism prioritizes deleting the first compressed file with the lowest access frequency and storage duration that is greater than or equal to the preset duration, along with its corresponding second checksum. The deleted files are then backed up and archived.

[0041] The intelligent system based on installation process file change tracking and recovery proposed in this invention has the following advantages:

[0042] This invention adds a command to recover accidentally deleted files. It can automatically recover accidentally deleted files and uses a first checksum to verify the second checksum of a first compressed file. Only when the first checksum and the second checksum are the same will the first compressed file be migrated and decompressed to the target path to obtain the accidentally deleted file. This invention can accurately recover accidentally deleted files.

[0043] Furthermore, by determining the latest deletion time corresponding to the file name to be recovered in the first log as the target deletion time, the accuracy of accidentally deleted files can be improved. Determining the file name of the accidentally deleted file based on the first file name and the target file name can make the file name of the accidentally deleted file more reasonable. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the intelligent system based on installation process file change tracking and recovery according to the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Please see Figure 1 The intelligent system of this invention is deeply adapted to complex storage systems that store files whose file index information has been modified based on file change and deletion instructions. The deletion records corresponding to these instructions are carefully stored in a change and deletion list, which includes detailed first file index information obtained after the complex modification process. The core recovery process is as follows:

[0047] Receiving File Recovery Requests: The system can accurately receive requests containing detailed target description information of the files to be recovered. This information not only covers the regular filename, but also includes rich metadata such as the file's creation time, modification time, file size, file type, access control list, and unique identifier in the storage system. With this comprehensive information, the system can accurately locate and identify the files to be recovered.

[0048] Obtaining target file index information: Based on the rich target description information in the request, the system uses a self-developed high-efficiency indexing algorithm to achieve millisecond-level fast retrieval in a large and complex deletion list, accurately obtaining the index information of the first target file in the first target deletion record corresponding to the file to be recovered. This indexing algorithm is based on deep learning and big data analysis technology, and can automatically learn and adapt to the data distribution characteristics of different storage environments, thereby continuously optimizing retrieval efficiency.

[0049] File recovery is achieved through in-depth analysis and intelligent modification of the acquired target file index information. Utilizing advanced redirection technology, combined with distributed storage and parallel computing principles, the system can quickly and accurately integrate and recover file fragments stored on different storage nodes or media, ensuring the integrity and accuracy of the files to be recovered.

[0050] The system boasts powerful multi-version file recovery capabilities. Upon receiving a command to recover accidentally deleted files, the system deeply analyzes the command to extract detailed information about the file to be recovered and uses sophisticated encrypted hash algorithms and multi-dimensional data fusion technology to generate a multi-dimensional first verification value. This verification value is not only based on the original content of the file but also fully integrates multi-dimensional information such as the file's version information, access permission history, and file status changes at different storage stages, ensuring the accuracy and uniqueness of the verification.

[0051] The system utilizes a self-developed timestamp analysis algorithm to perform in-depth mining and analysis of the primary logs. This algorithm can accurately locate multiple deletion timestamps corresponding to the names of files to be recovered, and automatically select the most suitable target deletion time based on the user's personalized needs, the system's preset intelligent strategies, or the file's importance level. For example, for critical business files, the system will prioritize the deletion time point before the most recent modification to ensure that the recovered data is the version closest to business requirements.

[0052] Considering multi-node backup in distributed storage and multi-region redundant storage in cloud storage, the system can accurately determine multiple versions of the first compressed file and their corresponding multi-dimensional second checksum from multiple backup paths corresponding to the target deletion time, using distributed search and intelligent matching algorithms. These compressed files employ advanced encryption and compression algorithms, minimizing storage space usage while ensuring data security.

[0053] The system employs a self-developed advanced verification and matching algorithm to precisely match the first and second checksums. Based on quantum computing principles and machine learning technology, this algorithm can complete complex verification calculations in an extremely short time and intelligently analyze and verify the matching results. In cases of precise matching, the system migrates and decompresses the corresponding first compressed file to the target path via a high-speed data transmission channel, obtaining the user's desired version of the mistakenly deleted file.

[0054] The system uses an intelligent filename resolution algorithm to comprehensively obtain the initial filename of each file stored in the target path. This algorithm combines natural language processing and semantic analysis techniques, and based on the initial filename and the target filename, it fully considers file version information, modification history, and user habits to intelligently determine the unique and accurate name of mistakenly deleted files. For example, for files with the same name but different versions, the system adds version numbers, timestamps, and other information to the filename to ensure the uniqueness and readability of the filename.

[0055] The system can intelligently extract data deletion statements from complex data query statements. These data deletion statements contain rich data about the deleted files, including not only the file's historical name, historical content, file identifier, and secondary storage identifier, but also detailed information such as the file's access control list, creator and modifier information, and security attributes of the file at different storage stages.

[0056] The system utilizes deep learning algorithms and artificial intelligence technology to accurately extract initial file objects based on file identifiers and a dynamically updated preset object table. The preset object table is stored and managed using a combination of distributed hash tables (DHTs) and blockchain technology to ensure data security and consistency, while also enabling fast query and update operations.

[0057] The system performs comprehensive status detection on initial file objects through real-time monitoring and in-depth analysis, obtaining object processing statuses including processed, partially processed, pending, and abnormal states. The system utilizes machine learning algorithms and big data analytics to track and analyze objects in different states in real time, promptly identifying and resolving potential problems.

[0058] When an object is in the processed state, the system performs deep semantic analysis and knowledge graph construction on the file's historical content. Through natural language processing and deep learning techniques, the system extracts key information, semantic relationships, and knowledge structures from the file's historical content, using these as candidate file content. For example, for text files, the system can extract information such as the theme, keywords, and paragraph structure; for image files, the system can extract information such as image features, tags, shooting time, and location.

[0059] Based on the candidate file names and semantically analyzed candidate file content, the system uses sophisticated data reconstruction algorithms and knowledge graph fusion technology to obtain candidate file data, which is then securely stored in an encrypted second database model. This second database model employs distributed encrypted storage and multi-replica fault-tolerant technology to ensure data security and reliability, while also enabling efficient data read and write operations.

[0060] The system performs complex data combination and encryption processing based on multiple dimensions of information, including file history name, file identifier, secondary storage identifier, candidate file name, file access control list, and security attributes, to obtain the initial file data.

[0061] The system utilizes machine learning algorithms and adaptive optimization techniques to dynamically construct and optimize the first database model based on initial file data. This first database model employs a neural network-based storage structure and intelligent indexing technology, automatically adjusting the storage structure and indexing strategy according to data access patterns and trends to adapt to constantly changing data storage needs. For example, for frequently accessed data, the system stores it in a high-speed cache area and optimizes the index structure to improve access speed; for data that has not been accessed for a long time, the system migrates it to low-cost storage media and performs appropriate compression to save storage space.

[0062] If a file with the same name as the target file exists, the system intelligently analyzes multi-dimensional information such as file metadata, content similarity, access history, and user habits to output a personalized first confirmation message. This message details the potential impact of the overwrite or non-overwrite operation, including changes to file content, risks to data consistency, and potential impacts on business processes, to help users make informed decisions. For example, the system will display a visual comparison of the two files, highlighting key changes with different colors, and providing relevant risk assessments and recommendations.

[0063] Upon receiving an overwrite command, the system not only simply replaces the file but also performs an in-depth comparison and difference analysis between the original file and the mistakenly deleted file. Utilizing machine learning algorithms and data comparison technology, the system records the difference information and stores it in a dedicated difference log for subsequent traceability and auditing. Simultaneously, the system outputs the detailed filename of the mistakenly deleted file, including its version number, modification time, file size, file type, and unique identifier, ensuring that users can accurately identify and manage the recovered file.

[0064] Upon receiving a non-overwrite command, the system uses an intelligent renaming algorithm to rename the first file with the same name as the target file to a name that includes information such as timestamp, version number, file type, and file creator. For example, the system will rename the file to the format "original filename_timestamp_version number_file type_creator" to ensure the uniqueness and readability of the filename. Simultaneously, the system renames the mistakenly deleted file to the target file name and updates the relevant file indexes and metadata to ensure file consistency and accessibility within the storage system.

[0065] When a file deletion command is received, the system retrieves detailed information about the file to be deleted from the command, including the storage path, file type, access permissions, file creation and modification times, file owner, and file dependencies in the storage system.

[0066] The system performs in-depth analysis of storage paths, using machine learning algorithms and risk assessment models to identify sensitive information and potential risks. For example, the system will determine whether the storage path contains critical system files, involves multi-user shared areas, or poses data consistency risks. If a potential risk is detected, the system will promptly issue an alert and provide corresponding risk mitigation suggestions.

[0067] The system utilizes a highly efficient encrypted compression algorithm to compress the file to be deleted, resulting in a first compressed file. This compression algorithm combines quantum encryption technology with lossless compression to maximize the compression ratio while ensuring data security. Simultaneously, the system generates a secure second verification value using an encrypted hash algorithm. This verification value is used for subsequent data integrity verification and recovery operations.

[0068] The system securely migrates the first compressed file and the second checksum to a distributed first backup path. The first backup path employs distributed storage and multi-replica fault tolerance technology to ensure data security and reliability. Simultaneously, the system writes detailed command information corresponding to the file deletion command, including the file name to be deleted, deletion time, operating user, operating device, reason for operation, and the file's original storage path, into an encrypted first log. This first log uses blockchain technology for storage and management, ensuring the log data's immutability and traceability.

[0069] If the storage path contains preset sensitive information, the system outputs a detailed second confirmation prompt. This prompt not only asks the user whether to delete the file, but also provides a detailed preview of the file's content, an assessment of the file's importance, warnings of potential risks, and suggestions for possible alternative actions. For example, the system will display a summary of the file's content, its usage frequency, and related business processes through a visual interface, helping users fully understand the file's importance and the potential impact of deletion.

[0070] Upon receiving the first confirmation deletion command, the system outputs a third confirmation prompt, requiring the user to enter a specific confirmation code or perform biometric verification such as fingerprint, facial recognition, or iris recognition to ensure the security and legitimacy of the operation. The system employs advanced biometric technology and encrypted communication technology to ensure the accuracy and security of the verification process.

[0071] After receiving the second confirmation deletion command and passing biometric verification, the system securely retrieves the file to be deleted from the storage path. During the file retrieval process, the system re-verifies the file's integrity and consistency to ensure that the file to be deleted is the one the user truly needs to delete.

[0072] The system utilizes machine learning algorithms and big data analytics to dynamically adjust preset times and durations based on multiple factors such as file access frequency, importance, storage cost, and data lifecycle. For example, for important and frequently accessed files, the system automatically extends their storage time in the first backup path to ensure rapid data recovery; for files with low access frequency, the system automatically shortens the storage time based on data lifecycle and storage cost, and promptly cleans up expired data to free up storage space.

[0073] The system monitors the storage status of the primary backup path in real time, including storage capacity, number of files, and file access frequency. Utilizing artificial intelligence and automated operation and maintenance tools, it automatically activates an intelligent cleanup mechanism when storage space is insufficient. This mechanism prioritizes deleting the first compressed file with a storage duration greater than or equal to a preset duration and the lowest access frequency, along with its corresponding second checksum. Simultaneously, the system backs up and archives the deleted files for restoration when needed.

[0074] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent system based on installation process file change tracking and recovery, characterized in that, The system is applied to a storage system that stores files whose file index information has been modified based on file change and deletion instructions. Deletion records corresponding to the file change and deletion instructions are stored in a change and deletion list. These change and deletion records include first file index information obtained after modifying the file index information. The system includes: The request receiving module is used to receive file recovery requests, wherein the file recovery request includes target description information of the file to be recovered, and the target description information includes file name, creation time, modification time, file size, file type, access control list, and unique identifier of the file in the storage system; The index acquisition module is used to obtain the first target file index information in the first target deletion record corresponding to the file to be recovered from the deletion list according to the target description information, wherein the first target deletion record includes the target description information; The file recovery module is used to modify the index information of the first target file so as to achieve file recovery by redirecting the file to be recovered. The redirection is based on the principles of distributed storage and parallel computing, integrating file fragments stored on different storage nodes or storage media.

2. The intelligent system based on installation process file change tracking and recovery according to claim 1, characterized in that, It also includes a multi-version accidental file recovery module, which includes: The information parsing and verification value generation unit is used to obtain the file information to be recovered from the file recovery command when a file recovery command for accidentally deleted is received, and to generate a multi-dimensional first verification value based on the file information to be recovered. The file information to be recovered includes the file name, version information, and access permission history. The multi-dimensional first verification value is generated based on a cryptographic hash algorithm and multi-dimensional data fusion technology. The time determination unit is used to determine the latest deletion time corresponding to the name of the file to be recovered in the first log as the target deletion time. The time determination is based on a timestamp analysis algorithm and takes into account user needs, system policies or file importance levels. The compressed file location unit is used to determine, from multiple backup paths corresponding to the target deletion time, a first compressed file corresponding to the name of the file to be recovered and a multi-dimensional second verification value corresponding to the first compressed file. The location is based on a distributed search and intelligent matching algorithm, which is adapted to distributed storage and cloud storage environments. The verification matching and recovery unit is used to migrate and decompress the first compressed file to the target path when the first verification value and the second verification value are the same, thereby obtaining the accidentally deleted file; The filename determination unit is used to obtain the first filename corresponding to each file stored in the target path, and determine the filename of the mistakenly deleted file based on the first filename and the target filename, combined with file version information, modification history and user usage habits.

3. The intelligent system based on installation process file change tracking and recovery according to claim 1, characterized in that, It also includes an adaptive database model building module, which includes: The statement extraction unit is used to obtain data deletion statements from data query statements. The data deletion statements include deleting file data. The deleted file data includes file history name, file history content, file identifier, second storage identifier, access control list, creator and modifier information, and security attributes of different storage stages. The object extraction unit is used to extract objects based on the file identifier and a dynamically updated preset object table to obtain an initial file object. The preset object table is stored and managed based on a distributed hash table (DHT) and blockchain technology. A status detection unit is used to perform status detection on the initial file object to obtain the object processing status, which includes processed status, partially processed status, pending processing status and abnormal status. The semantic analysis and content determination unit is used to perform deep semantic analysis and knowledge graph construction on the historical content of the file when the object is in the processed state, and extract key information as candidate file content. The data recombination and storage unit is used to obtain candidate file data based on the candidate file name and the semantically analyzed candidate file content, using data recombination algorithms and knowledge graph fusion technology, and to store the candidate file data in an encrypted second database model; The data combination and model building unit is used to combine and encrypt data based on multi-dimensional information such as file history name, file identifier, second storage identifier, candidate file name, file access control list, and security attributes to obtain initial file data. Based on the initial file data, the unit dynamically builds and optimizes the first database model using machine learning algorithms and adaptive optimization techniques.

4. The intelligent system based on installation process file change tracking and recovery according to claim 3, characterized in that, When the state detection unit detects that the object processing state is unprocessed, the initial file object is used as a candidate file object. If the candidate file object is not the same as the preset object type, the candidate file object is deleted.

5. The intelligent system based on installation process file change tracking and recovery according to claim 2, characterized in that, It also includes an intelligent conflict resolution module, which is used for: If a file with the same name as the target file exists, a personalized first confirmation prompt is output by analyzing multi-dimensional information such as file metadata, content similarity, access history, and user habits. The first confirmation prompt is used to indicate whether to overwrite the file corresponding to the first file name with the accidentally deleted file. Upon receiving an overwrite instruction, a deep comparison and difference analysis are performed on the original file and the mistakenly deleted file. The difference information is recorded in the difference log. The file corresponding to the first file name that is the same as the target file name is replaced with the mistakenly deleted file. The detailed file name of the mistakenly deleted file is output, including version number, modification time, file size, file type and file unique identifier. Upon receiving a non-overwrite instruction, the first file name, which is the same as the target file name, is renamed using a renaming algorithm to include information such as timestamp, version number, file type, and file creator. The mistakenly deleted file is then renamed to the target file name, and the relevant file index and metadata are updated.

6. The intelligent system based on installation process file change tracking and recovery according to claim 2, characterized in that, It also includes an enhanced file deletion preprocessing module, which is used for: Upon receiving a file deletion command, the file information to be deleted is obtained from the file deletion command. The file information to be deleted includes the storage path, file type, access permissions, file creation time and modification time, file owner, and file dependencies in the storage system. A deep analysis of the storage path is performed, and machine learning algorithms and risk assessment models are used to identify sensitive information and potential risks within it. The file to be deleted is compressed using an efficient encryption compression algorithm to obtain a first compressed file, and a secure second verification value is generated using an encryption hash algorithm. The first compressed file and the second verification value are securely migrated to the distributed first backup path, and the detailed command information corresponding to the file deletion command, including the name of the file to be deleted, the deletion time, the operating user, the operating device, the reason for the operation, and the original storage path of the file, is written into the encrypted first log.

7. The intelligent system based on installation process file change tracking and recovery according to claim 6, characterized in that, It also includes a multi-level confirmation deletion module, which is used for: If the storage path contains preset sensitive information, a detailed second confirmation prompt message is output. The second confirmation prompt message not only asks the user whether to delete, but also provides a detailed preview of the file's contents, an assessment of the file's importance, a warning of potential risks, and suggestions for possible alternative operations. Upon receiving the first confirmation deletion command, a third confirmation prompt message is output, requiring the user to enter a specific confirmation code or perform biometric verification such as fingerprint, facial recognition, or iris recognition. Upon receiving a second confirmation deletion command and passing biometric verification, the file to be deleted is securely retrieved from the storage path.

8. The intelligent system based on installation process file change tracking and recovery according to claim 2, characterized in that, It also includes an intelligent storage management module, which is used for: By utilizing machine learning algorithms and big data analytics, the preset time and duration are dynamically adjusted based on multiple factors such as file access frequency, importance, storage cost, and data lifecycle. By monitoring the storage status of the first backup path in real time, including information such as storage capacity, number of files, and file access frequency, an intelligent cleanup mechanism is automatically activated when storage space is insufficient. The mechanism prioritizes deleting the first compressed file with the lowest access frequency and storage duration that is greater than or equal to the preset duration, along with its corresponding second checksum. The deleted files are then backed up and archived.

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