Archive management system based on big data technology

Abstract

The invention relates to the technical field of archive management, in particular to an archive management system based on a big data technology. The system comprises a confidential grade evaluation module, an archive storage path making module and a decryption rule definition module. According to the invention, the confidential grade evaluation module is combined with the confidentiality standard to perform confidential grade evaluation on the obtained to-be-managed data, the file storage path making module is used to make the matched file storage path according to the confidential grade evaluation result of the to-be-managed data, and the to-be-managed data is subjected to regionalized storage. A regional decryption rule is established through a decryption rule definition module, automatic secret-related recognition processing is achieved, public data encryption processing and file management resource waste are avoided, meanwhile, a corresponding storage area is matched according to the data secret-related degree, decryption planning is conducted according to a storage path in the decryption process, and the decryption efficiency is improved. The automatic management of the whole archive management is ensured, the archive management effect of different data is improved, and the adaptability of the whole system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of file management, and more specifically, to a file management system based on big data technology. Background Art

[0002] File management refers to the management behavior of sorting, classifying, and refining the materials formed during the process of a research project, such as the design, implementation, and results of the project, so as to record, standardize, and guide the research project and regulate the research process. The key to file management work lies in the current status of file management informatization.

[0003] With the development of social modernization and the emergence of things such as office automation and paperless work, more and more data is no longer recorded using traditional physical objects, but is stored online through a file library.

[0004] Traditional online file management mainly adopts a process-based storage method, and its specific steps are as follows: First step: Manually obtain the types of current data to be stored, and divide the storage area according to the data types; Second step: Encrypt and store the data according to the user's requirements, and inform the manager and the user of the decryption key; Third step: The manager and the user decrypt according to the decryption key to obtain the corresponding stored data.

[0005] In the specific management process, due to different confidentiality standards in different industries, for file managers, they need to encrypt the corresponding data according to industry requirements. Therefore, not all data needs to be stored encrypted. For users themselves, since some users' data has been made public before storage or they did not know in advance that their data is confidential, it results in the fact that confidential data is not kept confidential according to the standard requirements during storage, and some data that has already been made public still requires encryption during the management process, resulting in a significant reduction in the resource utilization rate of file management, inability to standardize data storage, and affecting the overall management effect of file management.

[0006] In order to address the above problems, there is an urgent need for a file management system based on big data technology. Summary of the Invention

[0007] The purpose of the present invention is to provide a file management system based on big data technology to solve the problems raised in the above background art.

[0008] To achieve the above purpose, a file management system based on big data technology is provided, including a data to be managed acquisition module, a confidentiality big data storage platform, a classified level assessment module, a file storage path determination module, and a decryption rule definition module; The to-be-managed data acquisition module sets up a to-be-managed data transmission channel for acquiring the content of the to-be-managed data; The confidentiality big data storage platform collects confidentiality agreements within the industry, establishes a big data storage database, and collects and stores confidentiality standards within the industry; The classified level assessment module combines the confidentiality standards to assess the classified level of the acquired to-be-managed data; The file storage path determination module determines a matching file storage path according to the classified level assessment result of the to-be-managed data, and performs regional storage on the to-be-managed data; The decryption rule definition module establishes regional decryption rules, performs classified processing on the to-be-managed data stored in different regions, and matches the corresponding decryption rules according to the file storage path.

[0009] As a further improvement of this technical solution, the method for collecting and storing confidentiality standards within the industry in the confidentiality big data storage platform includes the following steps: S1. Obtain the classified agreements within the industry, and extract the sensitive words therein; S2. Classify the classified agreements according to the influence degree of the classified agreements; S3. Bind the classified agreements after classification and the corresponding sensitive words.

[0010] As a further improvement of this technical solution, the classified level assessment module includes a sensitive word extraction unit, a sensitivity calculation unit, and a sensitive word database; Among them, the sensitive word database is used to store the classified agreements after classification in S3 and the corresponding sensitive words; The sensitive word extraction unit combines the content stored in the sensitive word database, compares it with the content of the to-be-managed data, obtains the overlapping sensitive words therein, and marks them as matching sensitive words; The sensitivity calculation unit combines the quantity of matching sensitive words and the level of the matching classified agreements to calculate the sensitivity of the to-be-managed data.

[0011] As a further improvement of this technical solution, the method for calculating the sensitivity of the to-be-managed data in the sensitivity calculation unit includes the following steps: S10. Set a unit coincidence rate threshold , obtain the comparison results of the sensitive words of the to-be-managed data with each classified agreement, and obtain the coincidence rate of each sensitive word ; S20. Compare the coincidence rates of each sensitive word with the unit coincidence rate threshold ; When the sensitive word coincidence rate ≥ the unit coincidence rate threshold , then the sensitive word coincidence rate The corresponding classified protocol is marked as the relevant classified protocol; When the sensitive word coincidence rate <Unit coincidence rate threshold , then the sensitive word coincidence rate The corresponding classified protocol is marked as an irrelevant classified protocol and is not used as a reference; S30. Count each relevant classified protocol, compare the levels of each relevant classified protocol, mark the relevant classified protocol with the highest classified level as the matching classified protocol, and its corresponding classified level is the classified level of the currently to-be-managed data.

[0012] As a further improvement of this technical solution, the method for formulating a matching file storage path in the file storage path formulation module includes the following steps: S100. Divide the file storage repository according to the classified level, generate a partitioned file storage repository, and mark the area number of the partitioned file storage repository through the classified level; S200. Obtain the classified levels of different contents of the to-be-managed data, and match the corresponding partitioned file storage repository according to the classified level; S300. Extract the area numbers corresponding to each matched partitioned file storage repository, and mark and generate the storage path of the to-be-managed data.

[0013] As a further improvement of this technical solution, the decryption rule definition module includes a partition encryption key generation unit and a partition extraction decryption key matching unit. The partition encryption key generation unit generates a corresponding partition encryption key in combination with the partitioned file storage repository divided by the to-be-managed data and the storage time. The partition extraction decryption key matching unit formulates a corresponding partition decryption key according to the encryption rule of the partition encryption key.

[0014] As a further improvement of this technical solution, the decryption rule definition module further includes an extraction code partition formulation unit. The extraction code partition formulation unit generates an extraction code for the content stored in different partitioned file storage repositories of the to-be-managed data in combination with the partition encryption key and the storage path of the to-be-managed data.

[0015] As a further improvement of this technical solution, the method for generating an extraction code for the content stored in different partitioned file storage repositories of the to-be-managed data in the extraction code partition formulation unit includes the following steps: S1000. Obtain each partitioned file storage repository where the currently to-be-managed data is stored, and obtain the corresponding partition encryption key; S2000. Form an extraction serial number in ascending order of the classified levels of each partitioned file storage repository; S3000. Use the classified level of the partitioned file repository to be extracted as the suffix of the serial number, and combine it with the corresponding partition encryption password to form the final extraction code.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: In the file management system based on big data technology, through the classified level evaluation module combined with the confidentiality standard, the classified level of the data to be managed obtained is evaluated. Through the file storage path determination module, according to the classified level evaluation result of the data to be managed, a matching file storage path is determined, and the data to be managed is stored in a regionalized manner. And through the decryption rule definition module, a regional decryption rule is established to realize automatic classified identification processing, avoid encrypting public data and wasting file management resources. At the same time, according to the classified degree of the data, the corresponding storage area is matched, and in the decryption process, the decryption plan is carried out according to the storage path, which not only ensures the automatic management of the overall file management, but also improves the file management effect of different data, and improves the adaptability of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is the overall structural block diagram of the present invention; Figure 2 is the structural block diagram of the decryption rule definition module of the present invention; Figure 3 is the method step diagram for collecting and storing the confidentiality standards in the industry of the present invention; Figure 4 is the method step diagram for calculating the sensitivity of the data to be managed of the present invention; Figure 5 is the method step diagram for determining the matching file storage path of the present invention; Figure 6 is the method step diagram for generating the extraction code of the content stored in different partitioned file repositories of the data to be managed of the present invention.

[0018] The meanings of the various reference numerals in the figure are as follows: 10. Data acquisition module to be managed; 20. Confidential big data storage platform; 30. Classified level evaluation module; 310. Sensitive word extraction unit; 320. Sensitivity calculation unit; 330. Sensitive word database; 40. File storage path determination module; 50. Decryption rule definition module; 510. Partition encryption password generation unit; 520. Partition extraction decryption password matching unit; 530. Extraction code partition determination unit. DETAILED DESCRIPTION OF THE INVENTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Please refer to Figure 1 As shown, a file management system based on big data technology is provided, including a to-be-managed data acquisition module 10, a confidentiality big data storage platform 20, a classified level evaluation module 30, a file storage path determination module 40, and a decryption rule definition module 50; The to-be-managed data acquisition module 10 builds a to-be-managed data transmission channel for acquiring the content of the to-be-managed data; The confidentiality big data storage platform 20 collects confidentiality agreements in the industry, establishes a big data storage database, and collects and stores confidentiality standards in the industry; The classified level evaluation module 30 combines the confidentiality standards to evaluate the classified level of the acquired to-be-managed data; The file storage path determination module 40 formulates a matching file storage path according to the classified level evaluation result of the to-be-managed data, and stores the to-be-managed data in a regionalized manner; The decryption rule definition module 50 establishes regional decryption rules, performs classified processing on the to-be-managed data stored in different regions, and matches the corresponding decryption rules according to the file storage path.

[0021] During specific use, in the process of file management, first, the to-be-managed data acquisition module 10 builds a to-be-managed data transmission channel for acquiring the content of the to-be-managed data, that is, provides a data entry window through the built management data transmission platform. For example, SQL statements are used to enter data in the database; After completing the acquisition of the to-be-managed data, in order to adapt to the classified standards of different industries, it is necessary to collect confidentiality agreements in the industry through the confidentiality big data storage platform 20, establish a big data storage database, and collect and store confidentiality standards in the industry. For example, the language description in the to-be-managed data involves privacy in the industry, which is used as a standard for evaluating the classified level. The classified level evaluation module 30 combines the confidentiality standards to evaluate the classified level of the acquired to-be-managed data. Different storage methods correspond to to-be-managed data with different classified levels because of different categories and degrees of classification; Since the problems involved in the content of different data to be managed vary, and the confidentiality levels at different locations are different. Also, even if the entire data has confidentiality issues, some data still needs to be published for later reference and citation to prove that archival management has been carried out, such as the abstract part in a paper. Therefore, for differential management of data, adaptive storage management is carried out according to different confidentiality levels. During the later storage process, the archival storage path formulation module 40 needs to formulate a matching archival storage path according to the evaluation result of the confidentiality level of the data to be managed, and store the data to be managed in different regions; After the storage work is completed, to ensure the confidentiality of the entire archival management, improve the resource utilization rate of archival management, and adapt to the storage work of different data to be managed, it is necessary to establish a regional decryption rule through the decryption rule definition module 50, perform confidentiality processing on the data to be managed stored in different regions, match the corresponding decryption rule according to the archival storage path, realize automatic confidentiality identification processing, avoid encrypting publicly available data and wasting archival management resources. At the same time, match the corresponding storage region according to the data confidentiality level, and perform decryption planning according to the storage path during decryption, which not only ensures the automatic management of the overall archival management, but also improves the archival management effect of different data and the adaptability of the overall system.

[0022] In addition, as Figure 3 shown, the method for collecting and storing the confidentiality standards in the industry in the confidentiality big data storage platform 20 includes the following steps: S1. Obtain the confidential agreements in the industry and extract the sensitive words therein; S2. Classify the confidential agreements according to the impact degree of the confidential agreements; S3. Bind the classified confidential agreements and the corresponding sensitive words.

[0023] The confidentiality level evaluation module 30 includes a sensitive word extraction unit 310, a sensitivity calculation unit 320, and a sensitive word database 330; Among them, the sensitive word database 330 is used to store the classified confidential agreements and the corresponding sensitive words after S3; The sensitive word extraction unit 310 combines the content stored in the sensitive word database 330 and compares it with the content of the data to be managed to obtain the overlapping sensitive words and marks them as matching sensitive words; The sensitivity calculation unit 320 calculates the sensitivity of the data to be managed in combination with the quantity of matching sensitive words and the level of the matching confidential agreement.

[0024] Furthermore, as Figure 4 shown, the method for calculating the sensitivity of the data to be managed in the sensitivity calculation unit 320 includes the following steps: S10. Formulate a threshold for the coincidence rate per unit Obtain the comparison results of the sensitive words between the data to be managed and each classified protocol, and calculate the coincidence rate of each sensitive word. ; S20. Compare the coincidence rates of each sensitive word with the coincidence rate threshold of the unit ; When the coincidence rate of the sensitive word ≥ the coincidence rate threshold of the unit , then the classified protocol corresponding to this coincidence rate of the sensitive word is marked as the relevant classified protocol; When the coincidence rate of the sensitive word < the coincidence rate threshold of the unit , then the classified protocol corresponding to this coincidence rate of the sensitive word is marked as an irrelevant classified protocol and is not used as a reference; S30. Count each relevant classified protocol, compare the levels of each relevant classified protocol, mark the relevant classified protocol with the highest classified level as the matching classified protocol, and its corresponding classified level is the classified level of the current data to be managed.

[0025] In specific use, during the process of evaluating the level of the data to be managed, when collecting industry standards in the early stage, it is necessary to classify the collected classified protocols in advance. First, obtain the classified protocols in the industry, extract the sensitive words in them, and each classified protocol is represented by the corresponding sensitive word as the reference basis for later matching. After completing the sensitive word extraction work, in order to further determine the classified levels of each classified protocol, it is necessary to classify the classified protocols according to the impact degree of the classified protocols, such as the impact on profits, industry impact, and the user's own investment situation, etc. These are all used as the impact degrees of different classified protocols and are used to classify the classified protocols. Finally, bind the classified protocols after classification and the corresponding sensitive words.

[0026] In specific evaluation, first store the classified protocols after classification and the corresponding sensitive words in the sensitive word database 330. Since the levels of each classified protocol are different, it is necessary to compare them separately with the data to be managed. That is, the sensitive word extraction unit 310 combines the content stored in the sensitive word database 330 and compares it with the content of the data to be managed to obtain the overlapping sensitive words and mark them as matching sensitive words. Finally, the sensitivity calculation unit 320 combines the quantity of matching sensitive words and the classified level of the matching classified protocol to calculate the sensitivity of the data to be managed. In specific calculation, since a classified protocol consists of multiple sensitive words, the coincidence of a small number of sensitive words does not mean that the current data to be managed matches the classified protocol. It is necessary to set a coincidence rate threshold of the unit in advance , and obtain the comparison results of the sensitive words between the data to be managed and each classified protocol, and calculate the coincidence rate of each sensitive word , only when the sensitive word coincidence rate ≥ the unit coincidence rate threshold , then the sensitive word coincidence rate The corresponding classified protocol is marked as a relevant classified protocol, and conversely, the corresponding classified protocol is marked as an irrelevant classified protocol. At the same time, since the data to be managed is likely to meet multiple classified protocols at the same time, when encountering the above problems, it is necessary to count each relevant classified protocol and compare the levels of each relevant classified protocol. Mark the relevant classified protocol with the highest classified level as the matching classified protocol, and its corresponding classified level is the classified level of the current data to be managed.

[0027] Furthermore, as Figure 5 shown, the method for formulating a matching file storage path in the file storage path formulation module 40 includes the following steps: S100. Divide the file storage repository according to the classified level, generate a partitioned file storage repository, and mark the area number of the partitioned file storage repository through the classified level; S200. Obtain the classified levels of different contents of the data to be managed, and match the corresponding partitioned file storage repository according to the classified level; S300. Extract the area numbers corresponding to each matching partitioned file storage repository, and mark and generate the storage path of the data to be managed.

[0028] Specifically, as Figure 2 shown, the decryption rule definition module 50 includes a partition encryption key generation unit 510 and a partition extraction decryption key matching unit 520. The partition encryption key generation unit 510 combines the partitioned file storage repository divided by the data to be managed and the storage time to generate a corresponding partition encryption key. The partition extraction decryption key matching unit 520 formulates a corresponding partition decryption key according to the encryption rule of the partition encryption key.

[0029] In addition, as Figure 2 shown, the decryption rule definition module 50 further includes an extraction code partition formulation unit 530. The extraction code partition formulation unit 530 combines the partition encryption key and the storage path of the data to be managed to generate an extraction code for the content stored in different partitioned file storage repositories of the data to be managed.

[0030] Further, as Figure 6 shown, the method for generating an extraction code for the content stored in different partitioned file storage repositories of the data to be managed in the extraction code partition formulation unit 530 includes the following steps: S1000. Obtain each partitioned file storage repository where the current data to be managed is stored, and obtain the corresponding partition encryption key; S2000. Compose an extraction serial number from low to high according to the classified levels of each partitioned file storage repository; S3000. Use the classified level of the partitioned file repository to be extracted as the serial number suffix, and combine it with the corresponding partition encryption password to form the final extraction code.

[0031] During specific use, in the process of encrypting and decrypting the stored data to be managed, in order to further improve the classification management effect of the overall file management system and distinguish data with different classified levels, it is first necessary to divide the file repository according to the classified level to generate a partitioned file repository, and mark the area number of the partitioned file repository through the classified level, that is, divide multiple partitioned file repositories. Each partitioned file repository has a different classified level and stores different data contents. For example, a certain data A to be managed includes and and so on. Among them, is Class-I confidential data with the highest classified level. The corresponding partitioned file repository for storage also has the highest classified level, and its area number is 001. And is Class-III confidential data, and the corresponding partitioned file repository for storage also has a classified level of three, and its area number is 003. is Class-II confidential data, and the corresponding partitioned file repository for storage also has a classified level of two, and its area number is 002. is Class-II confidential data, and the corresponding partitioned file repository for storage also has a classified level of four, and its area number is 004. Each partitioned file repository stores data of the corresponding level, and extracts the area numbers corresponding to each matching partitioned file repository, and marks and generates the storage path of the data to be managed, which is 001-002-003-004; After completing the formulation of the storage path, the partition encryption password generation unit 510 generates the corresponding partition encryption password in combination with the partitioned file repository divided by the data to be managed and the storage time. In the present invention, the encryption method can adopt the existing encryption method, such as the asymmetric encryption algorithm. During the encryption process, a public key is randomly generated, which is the above-mentioned partition encryption password. During decryption, the partition extraction decryption password matching unit 520 formulates the corresponding partition decryption password, that is, the private key, as the corresponding partition decryption password according to the encryption rule of the partition encryption password. Finally, the extraction code partition formulation unit 530 generates the extraction code for the content of the data to be managed stored in different partitioned file repositories in combination with the partition encryption password and the storage path of the data to be managed. During the generation process, it is first necessary to obtain each partitioned file repository where the current data to be managed is stored, and obtain the corresponding partition encryption password as the prefix of the data to be managed stored in the current partitioned file repository. Subsequently, an extraction serial number is formed according to the classified levels of each partitioned file repository from low to high. Use the classified level of the partitioned file repository to be extracted as the serial number suffix, and combine it with the corresponding partition encryption password to form the final extraction code. For example, It is Class III confidential data, with the corresponding area code being 003 and the corresponding extraction code being the partition plus password - 001002003, which serves as the instruction to extract the current confidential data. However, at this time, the entire confidential data is still in an encrypted form, and the extractor needs to input the correct partition decryption password to obtain the final confidential data.

[0032] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. The archive management system based on big data technology is characterized by: It includes a module for acquiring data to be managed (10), a confidentiality big data storage platform (20), a confidentiality level assessment module (30), an archive storage path formulation module (40) and a decryption rule definition module (50); The to-be-managed data acquisition module (10) builds a to-be-managed data transmission channel for acquiring the content of the to-be-managed data; The confidentiality big data storage platform (20) collects confidentiality agreements within the industry, establishes a big data storage database, and collects and stores confidentiality standards within the industry; The confidentiality level assessment module (30) assesses the confidentiality level of the acquired data to be managed in combination with the confidentiality standard; The archive storage path formulation module (40) formulates a matching archive storage path according to the confidentiality level assessment result of the data to be managed, and performs regional storage of the data to be managed; The decryption rule definition module (50) establishes regional decryption rules, performs confidentiality processing on the data to be managed stored in different areas, and matches the corresponding decryption rules according to the archive storage path.

2. The file management system based on big data technology according to claim 1 is characterized by: The method for collecting confidentiality standards in the storage industry in the confidentiality big data storage platform (20) comprises the following steps: S1. Obtain confidential protocols in the industry and extract sensitive confidential words; S2. Classify confidential protocols according to their impact; S3. Bind the classified confidential protocols and the corresponding sensitive words.

3. The file management system based on big data technology according to claim 2 is characterized by: The confidentiality level assessment module (30) comprises a sensitive word extraction unit (310), a sensitivity calculation unit (320) and a sensitive word database (330); The sensitive word database (330) is used to store the confidential protocols and corresponding sensitive words after the S3 performs level classification; The sensitive word extraction unit (310) combines the content stored in the sensitive word database (330) and compares it with the content of the data to be managed, obtains the overlapping sensitive words therein, and marks them as matching sensitive words; The sensitivity calculation unit (320) calculates the sensitivity of the data to be managed by combining the amount of matched sensitive words and the level of matched confidentiality-related protocols.

4. The file management system based on big data technology according to claim 3 is characterized by: The method for calculating the sensitivity of the data to be managed in the sensitivity calculation unit (320) comprises the following steps: S10. Establish unit overlap rate threshold , obtain the comparison results of the sensitive words of the data to be managed and each confidential protocol, and obtain the overlap rate of each sensitive word ; S20, compare the overlap rate of each sensitive word Unit coincidence rate threshold ; When the sensitive word overlap rate ≥Unit overlap rate threshold , then the overlap rate of the sensitive words The corresponding confidential protocols are marked as relevant confidential protocols; When the sensitive word overlap rate <Unit overlap rate threshold , then the overlap rate of the sensitive words The corresponding confidential protocols are marked as irrelevant confidential protocols and are not used for reference; S30, counting all relevant confidentiality protocols, and comparing the levels of all relevant confidentiality protocols, marking the relevant confidentiality protocol with the highest confidentiality level as a matching confidentiality protocol, and its corresponding confidentiality level is the confidentiality level of the current data to be managed.

5. The file management system based on big data technology according to claim 1 is characterized by: The method for formulating a matching archive storage path in the archive storage path formulating module (40) comprises the following steps: S100, dividing the archive storage repository according to the confidentiality level, generating a partitioned archive storage repository, and marking the area code of the partitioned archive storage repository according to the confidentiality level; S200, obtaining the confidentiality levels of different contents of the data to be managed, and matching the corresponding partition archive storage libraries according to the confidentiality levels; S300, extracting the area code corresponding to each matching partition archive repository, and marking and generating the storage path of the data to be managed.

6. The file management system based on big data technology according to claim 1 is characterized by: The decryption rule definition module (50) comprises a partition encryption code generation unit (510) and a partition extraction decryption code matching unit (520). The partition encryption code generation unit (510) generates a corresponding partition encryption code in combination with the partition archive repository and storage time of the data to be managed. The partition extraction decryption code matching unit (520) formulates a corresponding partition decryption code according to the encryption rule of the partition encryption code.

7. The file management system based on big data technology according to claim 6 is characterized by: The decryption rule definition module (50) further comprises an extraction code partition formulation unit (530), wherein the extraction code partition formulation unit (530) combines the partition encryption code and the storage path of the data to be managed to generate an extraction code for storing the data to be managed in different partition archive storage repositories.

8. The file management system based on big data technology according to claim 7 is characterized by: The method for generating the extraction code for storing the to-be-managed data in different partition archive storage repositories in the extraction code partition formulation unit (530) comprises the following steps: S1000, obtaining each partition archive repository storing the data to be managed currently, and obtaining the corresponding partition encryption code; S2000, extracting serial numbers according to the confidentiality level of each partition archive repository from low to high; S3000. Use the confidentiality level of the partition archive repository to be extracted as the suffix of the serial number, combined with the corresponding partition encryption code, to form the final extraction code.

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