Intelligent security file management method and system based on big data analysis

Abstract

The invention provides an intelligent security archive management method and system based on big data analysis in the technical field of security archive management, and the method comprises the steps: S1, creating a plurality of data templates, and setting collection parameters; s2, acquiring security data based on the acquisition parameters and preprocessing the security data; s3, analyzing each piece of security data to obtain a classification label, and classifying each piece of security data based on the classification label; s4, matching corresponding data templates for the security data of each category based on the classification labels, and mapping and filling the corresponding data templates with the security data to obtain corresponding security archives; s5, encrypting each security file into a corresponding encrypted file, and storing each encrypted file in a cloud server; and S6, the cloud server decrypts the encrypted archive based on the archive management instruction to obtain the security archive, and adds, deletes, repairs or checks the security archive. The method has the advantages that the reliability, the efficiency and the quality of security file management are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of security archive management, and in particular to an intelligent security archive management method and system based on big data analysis. Background Art

[0002] As the importance of production safety continues to increase, all parties involved in the project have strengthened their awareness of safety file management. Project managers have begun to realize that safety files are not only a compliance requirement, but also an important basis for project management, accident tracing and accountability.

[0003] The management of safety archives during the construction of engineering projects has traditionally relied on paper documents, which has the following disadvantages:

[0004] 1. Reliance on paper documents leads to difficulties in information retrieval, untimely updates, storage taking up a large amount of physical space, and being easily damaged or lost due to moisture, insect infestation, fire, etc.; 2. It is difficult to effectively integrate and share safety files between different departments or projects, which affects the effective use of resources and cross-departmental collaboration, and there is a data island phenomenon; 3. Manual operations and paper processes increase management costs and reduce work efficiency, especially in large-scale engineering projects, the collection, organization and analysis of safety information is time-consuming and labor-intensive; 4. The degree of standardization of classification and coding varies, affecting the systematicness and consistency of safety file management; 5. The supervision mechanism for safety file management is not sound enough, and there is a lack of effective feedback and improvement mechanism for existing problems.

[0005] Therefore, how to provide an intelligent security file management method and system based on big data analysis to improve the reliability, efficiency and quality of security file management has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide an intelligent security archive management method and system based on big data analysis, so as to improve the reliability, efficiency and quality of security archive management.

[0007] In a first aspect, the present invention provides an intelligent security archive management method based on big data analysis, comprising the following steps:

[0008] Step S1, creating several data templates for generating security files and setting the collection parameters of security data;

[0009] Step S2, collecting safety data based on the collection parameters, and preprocessing each of the safety data;

[0010] Step S3, analyzing the pre-processed security data by big data analysis technology to obtain classification labels, and classifying the security data based on the classification labels;

[0011] Step S4, matching the corresponding data template for each category of security data based on the classification label, mapping and filling each security data into the corresponding data template to obtain a corresponding security file;

[0012] Step S5, encrypting each of the security files into a corresponding encrypted file, and storing each of the encrypted files in a cloud server;

[0013] Step S6: After the cloud server verifies the input file management instruction, it decrypts the corresponding encrypted file based on the file management instruction to obtain a security file, and manages the security file by adding, deleting, modifying or querying.

[0014] Furthermore, the step S1 is specifically as follows:

[0015] Creating a number of data templates for generating security files based on the file type, setting a unique template ID for each of the data templates, and storing each of the data templates in a pre-created template library;

[0016] The document template is an Excel template or a Word template; the Excel template is created by using jx ls, contains static content and dynamic data, and the dynamic data is identified by a preset data tag; the Word template is created by using PowerPoint, and is provided with a number of custom tags;

[0017] The collection parameters set for collecting security data include at least data source address, account password, data collection frequency, data collection method, data collection logic and data format.

[0018] Furthermore, the step S2 is specifically as follows:

[0019] The ETL tool collects security data based on the collection parameters, performs preprocessing on each of the security data including at least missing value completion, outlier removal, duplicate value deletion, format conversion and data aggregation, and performs format verification on each of the preprocessed security data based on the data format.

[0020] Furthermore, the step S3 is specifically as follows:

[0021] Extracting text features from each of the preprocessed security data using natural language processing technology, classifying the text features of each of the security data using a random forest algorithm in big data analysis technology to obtain classification labels, and classifying each of the security data based on the classification labels;

[0022] The step S4 is specifically as follows:

[0023] Based on the classification label, the corresponding data template is matched for each category of security data, and each security data is mapped and filled into the corresponding data template through Python to obtain the corresponding security file, and a unique file ID is set for each security file.

[0024] Furthermore, the step S5 is specifically as follows:

[0025] A pair of public and private keys are created based on the SM9 algorithm, HMAC calculation is performed on each of the security files to obtain a MAC value, each of the security files and the corresponding MAC value are encrypted into primary encrypted data in turn through the private key, each of the primary encrypted data is mapped through a preset mapping rule to obtain secondary encrypted data, the number 2 and the letter z in each of the secondary encrypted data are swapped to obtain the third-level encrypted data, the public key is encrypted into a primary key through the Serpent algorithm, each character of the primary key is shifted right by 6 bits to obtain the secondary key, each of the third-level encrypted data and the secondary key is encapsulated through the AES-KW algorithm to obtain an encrypted file, each of the encrypted files is stored in a cloud server through the TLS protocol, and each of the encrypted files is backed up to a NAS device through Mini IO;

[0026] The cloud server updates the pre-created archive management table based on the storage path of each encrypted archive, wherein the archive management table is further provided with at least an archive ID, an archive keyword, an archive creation time, and a business code field for associating different security archives;

[0027] The step S6 is specifically as follows:

[0028] The cloud server obtains the input archive management instruction, parses the archive management instruction to obtain the instruction content, timestamp, identity identification code and hash value carried by the instruction, where the hash value is obtained by hashing the instruction content, timestamp and identity identification code;

[0029] After the integrity check of the instruction content, timestamp and identity identification code is performed through the hash value, the validity check is performed through the timestamp, and then the legality check of the identity identification code is performed through the preset user management table;

[0030] Searching for a corresponding encrypted file based on the file ID carried in the instruction content and the file management table, decrypting the encrypted file to obtain a secure file, and managing the addition, deletion, modification or query of the secure file;

[0031] When the security file is added, deleted or modified, the security file is encrypted, stored and backed up again, and the file management table is updated synchronously; when the security file is queried, the export of the security file is supported.

[0032] In a second aspect, the present invention provides an intelligent security archive management system based on big data analysis, comprising the following modules:

[0033] Initialization module, used to create several data templates for generating security files and set the collection parameters of security data;

[0034] A safety data acquisition module, used for acquiring safety data based on the acquisition parameters and preprocessing each safety data;

[0035] A security data classification module, used to analyze the pre-processed security data by big data analysis technology to obtain classification labels, and classify the security data based on the classification labels;

[0036] A security file generation module, used for matching the corresponding data template with the security data of each category based on the classification label, and mapping and filling each of the security data into the corresponding data template to obtain the corresponding security file;

[0037] A security file encryption storage module, used to encrypt each of the security files into a corresponding encrypted file, and store each of the encrypted files in a cloud server;

[0038] The security file management module is used for the cloud server to verify the input file management instruction, decrypt the corresponding encrypted file based on the file management instruction to obtain the security file, and manage the addition, deletion, modification or query of the security file.

[0039] Furthermore, the initialization module is specifically used for:

[0040] Creating a number of data templates for generating security files based on the file type, setting a unique template ID for each of the data templates, and storing each of the data templates in a pre-created template library;

[0041] The document template is an Excel template or a Word template; the Excel template is created by using jx ls, contains static content and dynamic data, and the dynamic data is identified by a preset data tag; the Word template is created by using PowerPoint, and is provided with a number of custom tags;

[0042] The collection parameters set for collecting security data include at least data source address, account password, data collection frequency, data collection method, data collection logic and data format.

[0043] Furthermore, the security data acquisition module is specifically used for:

[0044] The ETL tool collects security data based on the collection parameters, performs preprocessing on each of the security data including at least missing value completion, outlier removal, duplicate value deletion, format conversion and data aggregation, and performs format verification on each of the preprocessed security data based on the data format.

[0045] Furthermore, the security data classification module is specifically used for:

[0046] Extracting text features from each of the preprocessed security data using natural language processing technology, classifying the text features of each of the security data using a random forest algorithm in big data analysis technology to obtain classification labels, and classifying each of the security data based on the classification labels;

[0047] The security profile generation module is specifically used for:

[0048] Based on the classification label, the corresponding data template is matched for each category of security data, and each security data is mapped and filled into the corresponding data template through Python to obtain the corresponding security file, and a unique file ID is set for each security file.

[0049] Furthermore, the security archive encryption storage module is specifically used for:

[0050] A pair of public and private keys are created based on the SM9 algorithm, HMAC calculation is performed on each of the security files to obtain a MAC value, each of the security files and the corresponding MAC value are encrypted into primary encrypted data in turn through the private key, each of the primary encrypted data is mapped through a preset mapping rule to obtain secondary encrypted data, the number 2 and the letter z in each of the secondary encrypted data are swapped to obtain the third-level encrypted data, the public key is encrypted into a primary key through the Serpent algorithm, each character of the primary key is shifted right by 6 bits to obtain the secondary key, each of the third-level encrypted data and the secondary key is encapsulated through the AES-KW algorithm to obtain an encrypted file, each of the encrypted files is stored in a cloud server through the TLS protocol, and each of the encrypted files is backed up to a NAS device through Mini IO;

[0051] The cloud server updates the pre-created archive management table based on the storage path of each encrypted archive, wherein the archive management table is further provided with at least an archive ID, an archive keyword, an archive creation time, and a business code field for associating different security archives;

[0052] The security archive management module is specifically used for:

[0053] The cloud server obtains the input archive management instruction, parses the archive management instruction to obtain the instruction content, timestamp, identity identification code and hash value carried by the instruction, where the hash value is obtained by hashing the instruction content, timestamp and identity identification code;

[0054] After the integrity check of the instruction content, timestamp and identity identification code is performed through the hash value, the validity check is performed through the timestamp, and then the legality check of the identity identification code is performed through the preset user management table;

[0055] Searching for a corresponding encrypted file based on the file ID carried in the instruction content and the file management table, decrypting the encrypted file to obtain a secure file, and managing the addition, deletion, modification or query of the secure file;

[0056] When the security file is added, deleted or modified, the security file is encrypted, stored and backed up again, and the file management table is updated synchronously; when the security file is queried, the export of the security file is supported.

[0057] The advantages of the present invention are:

[0058] 1. Create several data templates for generating security files, set the collection parameters of security data, then collect and pre-process security data based on the collection parameters, analyze the pre-processed security data through big data analysis technology to obtain classification labels, classify each security data based on the classification labels, match the corresponding data templates for each category of security data based on the classification labels, map each security data into the corresponding data template to obtain the corresponding security file; then encrypt each security file into a corresponding encrypted file and store it in the cloud server; then the cloud server verifies the input file management instructions, decrypts the corresponding encrypted file based on the file management instructions to obtain the security file, and adds security files to the security files. , deletion, modification or query management; that is, the security archives are converted from traditional paper documents to electronic documents for management, and stored on cloud servers for remote management, which is not only convenient for retrieval and updating, but also does not need to take up a lot of physical space, and avoids the influence of moisture, insect infestation, fire, etc. Security archives can be shared and integrated between different departments or projects to eliminate the phenomenon of data islands, and automatically collect security data based on pre-set collection parameters and automatically generate security archives using data templates without manual operation, and ensure the standardization of security archives. Accessing security archives through cloud servers requires verification of archive management instructions to avoid illegal access, thereby greatly improving the reliability, efficiency and quality of security archive management.

[0059] 2. By setting collection parameters including at least data source address, account password, data collection frequency, data collection method, data collection logic and data format, security data can be automatically collected based on the data source address, account password, data collection frequency, data collection method and data collection logic, and the collected security data can be verified based on the data format, which greatly improves the efficiency and quality of security data collection.

[0060] 3. By preprocessing the collected security data, including at least missing value filling, outlier removal, duplicate value deletion, format conversion and data aggregation, and performing format verification on the preprocessed security data based on the data format, the quality of security data collection is greatly improved.

[0061] 4. Through natural language processing technology, text features are extracted from the pre-processed security data. Through the random forest algorithm in big data analysis technology, the text features of each security data are classified to obtain classification labels. Based on the classification labels, each security data is classified, which greatly improves the accuracy of security data classification and thus greatly improves the matching accuracy of data templates.

[0062] 5. Create a pair of public and private keys through the SM9 algorithm, perform HMAC calculation on the security file to obtain the MAC value, encrypt the security file and the MAC value into primary encrypted data through the private key, map the primary encrypted data through the mapping rule to obtain secondary encrypted data, swap the number 2 and the letter z in the secondary encrypted data to obtain the third-level encrypted data, encrypt the public key into the primary key through the Serpent algorithm, shift each character of the primary key to the right by 6 bits to obtain the secondary key, encapsulate the third-level encrypted data and the secondary key through the AES-KW algorithm to obtain an encrypted file, store the encrypted file in the cloud server through the TLS protocol, and send it to the cloud server through Min IO backs up the encrypted files to the NAS device; since the data encrypted by the private key can only be decrypted by the public key, and the security files and public keys are encrypted at multiple levels, the MAC value can be used for integrity verification later. The TLS protocol is a secure transmission protocol. Combined with data backup, at least 10 security measures are taken before and after (SM9 algorithm, public and private keys, HMAC calculation, mapping rules, 2 and z swap, Serpent algorithm, character displacement, AES-KW algorithm, TLS protocol, backup), which greatly improves the security of security file storage.

[0063] 6. By setting up an archive management table that includes at least storage path, archive ID, archive keyword, archive creation time and business code fields, it is convenient to quickly retrieve the corresponding encrypted archives later.

[0064] 7. By setting the file management instructions to carry the instruction content, timestamp, identity identification code and hash value, three-fold security verification can be performed based on the timestamp, identity identification code and hash value, which greatly improves the security of secure file management. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The present invention will be further described below in conjunction with embodiments with reference to the accompanying drawings.

[0066] Figure 1 It is a flow chart of an intelligent security archive management method based on big data analysis of the present invention.

[0067] Figure 2 It is a structural schematic diagram of an intelligent security archive management system based on big data analysis of the present invention. DETAILED DESCRIPTION

[0068] The technical solution in the embodiments of the present application has the following overall idea: the security archives are converted from traditional paper documents into electronic documents for management, and stored on a cloud server for remote management, which is not only convenient for retrieval and updating, but also does not require a large amount of physical space and avoids the effects of moisture, insect infestation, fire, etc. The security archives can be shared and integrated between different departments or projects to eliminate the phenomenon of data islands. Security data is automatically collected based on pre-set collection parameters and security archives are automatically generated using data templates without manual operation, and the standardization of security archives is guaranteed. Access to security archives through a cloud server requires verification of archive management instructions to avoid illegal access, so as to improve the reliability, efficiency and quality of security archive management.

[0069] Please refer to Figure 1 to Figure 2 As shown, a preferred embodiment of the intelligent security archive management method based on big data analysis of the present invention includes the following steps:

[0070] Step S1, creating several data templates for generating security files and setting the collection parameters of security data;

[0071] Step S2, collecting safety data based on the collection parameters, and preprocessing each of the safety data;

[0072] Step S3, analyzing the pre-processed security data by big data analysis technology to obtain classification labels, and classifying the security data based on the classification labels;

[0073] Step S4, matching the corresponding data template for each category of security data based on the classification label, mapping and filling each security data into the corresponding data template to obtain a corresponding security file;

[0074] Step S5, encrypting each of the security files into a corresponding encrypted file, and storing each of the encrypted files in a cloud server;

[0075] Step S6: After the cloud server verifies the input file management instruction, it decrypts the corresponding encrypted file based on the file management instruction to obtain the security file, and manages the addition, deletion, modification or query of the security file.

[0076] The step S1 is specifically as follows:

[0077] Based on the file type, several data templates for generating safety files are created, for example, data templates for generating personnel safety files, equipment safety management files, hidden danger investigation and control management files, and safety log management files are created, a unique template ID (GU ID) is set for each of the data templates, and each of the data templates is stored in a pre-created template library;

[0078] The document template is an Excel template or a Word template; the Excel template is created by using jx ls, contains static content and dynamic data, and the dynamic data is identified by a preset data tag; the Word template is created by using PowerPoint, and is provided with a number of custom tags;

[0079] jx ls is a Java-based library that simplifies the process of generating Excel documents, especially when such materials need to be dynamically filled according to data in the application; po it l (po i-template-language) is a Word template engine that generates new documents based on Microsoft Word templates and data. Unlike FreeMarker or Velocity, which generate new HTML pages or configuration files based on text templates and data, po it l focuses on the generation of Word documents. It allows users to use ordinary Word files as the basic template, replace the custom tags in them to generate output files, and achieve a certain degree of visual design.

[0080] The collection parameters for collecting security data include at least the data source address, account password, data collection frequency, data collection method, data collection logic and data format. The security file can be continuously updated based on the data collection frequency.

[0081] By setting collection parameters including at least data source address, account password, data collection frequency, data collection method, data collection logic and data format, security data can be automatically collected based on the data source address, account password, data collection frequency, data collection method and data collection logic, and the collected security data can be verified based on the data format, which greatly improves the efficiency and quality of security data collection.

[0082] The step S2 is specifically as follows:

[0083] The ETL tool collects security data based on the collection parameters, performs preprocessing on each of the security data, including at least missing value completion, outlier removal, duplicate value deletion, format conversion, and data aggregation, and performs format verification on each of the preprocessed security data based on the data format. Format conversion, for example, converts a date from a string to a date type, or converts data from one encoding to another, such as UTF-8 to ISO-8859-1. In specific implementation, a scheduled task can be set in conjunction with a Cron expression.

[0084] By preprocessing the collected security data, including at least missing value filling, outlier removal, duplicate value deletion, format conversion and data aggregation, and performing format verification on the preprocessed security data based on the data format, the quality of security data collection is greatly improved.

[0085] Examples of the safety data include safety rules and regulations, safety training records, hidden danger investigation and rectification records, accident reporting and handling records, safety inspection records, emergency drill records, personnel management, and machinery and equipment management records.

[0086] The step S3 is specifically as follows:

[0087] Extracting text features from the preprocessed security data using natural language processing technology, classifying the text features of the security data using a random forest algorithm in big data analysis technology to obtain classification labels, and classifying the security data based on the classification labels; for example, classifying by classification labels such as time, project, and category;

[0088] Through natural language processing technology, text features are extracted from the pre-processed security data. Through the random forest algorithm in big data analysis technology, the text features of each security data are classified to obtain classification labels. Based on the classification labels, each security data is classified, which greatly improves the accuracy of security data classification and thus greatly improves the matching accuracy of data templates.

[0089] The step S4 is specifically as follows:

[0090] Based on the classification label, the corresponding data template is matched for each category of security data, and each security data is mapped and filled into the corresponding data template through Python to obtain the corresponding security file, and a unique file ID is set for each security file.

[0091] The step S5 is specifically as follows:

[0092] A pair of public and private keys are created based on the SM9 algorithm, HMAC calculation is performed on each of the security files to obtain a MAC value, each of the security files and the corresponding MAC value are encrypted into primary encrypted data in turn through the private key, each of the primary encrypted data is mapped through a preset mapping rule to obtain secondary encrypted data, the number 2 and the letter z in each of the secondary encrypted data are swapped to obtain the third-level encrypted data, the public key is encrypted into a primary key through the Serpent algorithm, each character of the primary key is shifted right by 6 bits to obtain the secondary key, each of the third-level encrypted data and the secondary key is encapsulated through the AES-KW algorithm to obtain an encrypted file, each of the encrypted files is stored in a cloud server through the TLS protocol, and each of the encrypted files is backed up to a NAS device through Mini IO;

[0093] Create a pair of public and private keys through the SM9 algorithm, perform HMAC calculation on the security file to obtain the MAC value, encrypt the security file and the MAC value into first-level encrypted data through the private key, map the first-level encrypted data through the mapping rule to obtain second-level encrypted data, swap the number 2 and the letter z in the second-level encrypted data to obtain third-level encrypted data, encrypt the public key into a first-level key through the Serpent algorithm, shift each character of the first-level key to the right by 6 bits to obtain the second-level key, encapsulate the third-level encrypted data and the second-level key through the AES-KW algorithm to obtain an encrypted file, store the encrypted file in the cloud server through the TLS protocol, and send it to the cloud server through Min IO backs up the encrypted files to the NAS device; since the data encrypted by the private key can only be decrypted by the public key, and the security files and public keys are encrypted at multiple levels, the MAC value can be used for integrity verification later. The TLS protocol is a secure transmission protocol. Combined with data backup, at least 10 security measures are taken before and after (SM9 algorithm, public and private keys, HMAC calculation, mapping rules, 2 and z swap, Serpent algorithm, character displacement, AES-KW algorithm, TLS protocol, backup), which greatly improves the security of security file storage.

[0094] The cloud server updates the pre-created archive management table based on the storage path of each encrypted archive, wherein the archive management table is further provided with at least an archive ID, an archive keyword, an archive creation time, and a business code field for associating different security archives;

[0095] By setting up an archive management table that includes at least storage path, archive ID, archive keyword, archive creation time and business code fields, it is convenient to quickly retrieve the corresponding encrypted archives later.

[0096] The step S6 is specifically as follows:

[0097] The cloud server obtains the input archive management instruction, parses the archive management instruction to obtain the instruction content, timestamp, identity identification code and hash value carried by the instruction, where the hash value is obtained by hashing the instruction content, timestamp and identity identification code;

[0098] By setting the archive management instructions to carry the instruction content, timestamp, identity identification code and hash value, a triple security check can be performed based on the timestamp, identity identification code and hash value, which greatly improves the security of secure archive management.

[0099] After the integrity check of the instruction content, timestamp and identity identification code is performed through the hash value, the validity check is performed through the timestamp, and then the legality check of the identity identification code is performed through the preset user management table;

[0100] Searching for a corresponding encrypted file based on the file ID carried in the instruction content and the file management table, decrypting the encrypted file to obtain a secure file, managing the addition, deletion, modification or query of the secure file, and recording access logs in real time;

[0101] When the security file is added, deleted or modified, the security file is encrypted, stored and backed up again, and the file management table is updated synchronously; when the security file is queried, the export of the security file is supported.

[0102] A preferred embodiment of the intelligent security archive management system based on big data analysis of the present invention includes the following modules:

[0103] Initialization module, used to create several data templates for generating security files and set the collection parameters of security data;

[0104] A safety data acquisition module, used for acquiring safety data based on the acquisition parameters and preprocessing each safety data;

[0105] A security data classification module, used to analyze the pre-processed security data by big data analysis technology to obtain classification labels, and classify the security data based on the classification labels;

[0106] A security file generation module, used for matching the corresponding data template with the security data of each category based on the classification label, and mapping and filling each of the security data into the corresponding data template to obtain the corresponding security file;

[0107] A security file encryption storage module, used to encrypt each of the security files into a corresponding encrypted file, and store each of the encrypted files in a cloud server;

[0108] The security file management module is used for the cloud server to verify the input file management instruction, decrypt the corresponding encrypted file based on the file management instruction to obtain the security file, and manage the addition, deletion, modification or query of the security file.

[0109] The initialization module is specifically used for:

[0110] Based on the file type, several data templates for generating safety files are created, for example, data templates for generating personnel safety files, equipment safety management files, hidden danger investigation and control management files, and safety log management files are created, a unique template ID (GU ID) is set for each of the data templates, and each of the data templates is stored in a pre-created template library;

[0111] The document template is an Excel template or a Word template; the Excel template is created by using jx ls, contains static content and dynamic data, and the dynamic data is identified by a preset data tag; the Word template is created by using PowerPoint, and is provided with a number of custom tags;

[0112] jx ls is a Java-based library that simplifies the process of generating Excel documents, especially when such materials need to be dynamically filled according to data in the application; po it l (po i-template-language) is a Word template engine that generates new documents based on Microsoft Word templates and data. Unlike FreeMarker or Velocity, which generate new HTML pages or configuration files based on text templates and data, po it l focuses on the generation of Word documents. It allows users to use ordinary Word files as the basic template, replace the custom tags in them to generate output files, and achieve a certain degree of visual design.

[0113] The collection parameters for collecting security data include at least the data source address, account password, data collection frequency, data collection method, data collection logic and data format. The security file can be continuously updated based on the data collection frequency.

[0114] By setting collection parameters including at least data source address, account password, data collection frequency, data collection method, data collection logic and data format, security data can be automatically collected based on the data source address, account password, data collection frequency, data collection method and data collection logic, and the collected security data can be verified based on the data format, which greatly improves the efficiency and quality of security data collection.

[0115] The safety data acquisition module is specifically used for:

[0116] The ETL tool collects security data based on the collection parameters, performs preprocessing on each of the security data, including at least missing value completion, outlier removal, duplicate value deletion, format conversion, and data aggregation, and performs format verification on each of the preprocessed security data based on the data format. Format conversion, for example, converts a date from a string to a date type, or converts data from one encoding to another, such as UTF-8 to ISO-8859-1. In specific implementation, a scheduled task can be set in conjunction with a Cron expression.

[0117] By preprocessing the collected security data, including at least missing value filling, outlier removal, duplicate value deletion, format conversion and data aggregation, and performing format verification on the preprocessed security data based on the data format, the quality of security data collection is greatly improved.

[0118] Examples of the safety data include safety rules and regulations, safety training records, hidden danger investigation and rectification records, accident reporting and handling records, safety inspection records, emergency drill records, personnel management, and machinery and equipment management records.

[0119] The security data classification module is specifically used for:

[0120] Extracting text features from the preprocessed security data using natural language processing technology, classifying the text features of the security data using a random forest algorithm in big data analysis technology to obtain classification labels, and classifying the security data based on the classification labels; for example, classifying by classification labels such as time, project, and category;

[0121] Through natural language processing technology, text features are extracted from the pre-processed security data. Through the random forest algorithm in big data analysis technology, the text features of each security data are classified to obtain classification labels. Based on the classification labels, each security data is classified, which greatly improves the accuracy of security data classification and thus greatly improves the matching accuracy of data templates.

[0122] The security profile generation module is specifically used for:

[0123] Based on the classification label, the corresponding data template is matched for each category of security data, and each security data is mapped and filled into the corresponding data template through Python to obtain the corresponding security file, and a unique file ID is set for each security file.

[0124] The security archive encryption storage module is specifically used for:

[0125] A pair of public and private keys are created based on the SM9 algorithm, HMAC calculation is performed on each of the security files to obtain a MAC value, each of the security files and the corresponding MAC value are encrypted into primary encrypted data in turn through the private key, each of the primary encrypted data is mapped through a preset mapping rule to obtain secondary encrypted data, the number 2 and the letter z in each of the secondary encrypted data are swapped to obtain the third-level encrypted data, the public key is encrypted into a primary key through the Serpent algorithm, each character of the primary key is shifted right by 6 bits to obtain the secondary key, each of the third-level encrypted data and the secondary key is encapsulated through the AES-KW algorithm to obtain an encrypted file, each of the encrypted files is stored in a cloud server through the TLS protocol, and each of the encrypted files is backed up to a NAS device through Mini IO;

[0126] Create a pair of public and private keys through the SM9 algorithm, perform HMAC calculation on the security file to obtain the MAC value, encrypt the security file and the MAC value into first-level encrypted data through the private key, map the first-level encrypted data through the mapping rule to obtain second-level encrypted data, swap the number 2 and the letter z in the second-level encrypted data to obtain third-level encrypted data, encrypt the public key into a first-level key through the Serpent algorithm, shift each character of the first-level key to the right by 6 bits to obtain the second-level key, encapsulate the third-level encrypted data and the second-level key through the AES-KW algorithm to obtain an encrypted file, store the encrypted file in the cloud server through the TLS protocol, and send it to the cloud server through Min IO backs up the encrypted files to the NAS device; since the data encrypted by the private key can only be decrypted by the public key, and the security files and public keys are encrypted at multiple levels, the MAC value can be used for integrity verification later. The TLS protocol is a secure transmission protocol. Combined with data backup, at least 10 security measures are taken before and after (SM9 algorithm, public and private keys, HMAC calculation, mapping rules, 2 and z swap, Serpent algorithm, character displacement, AES-KW algorithm, TLS protocol, backup), which greatly improves the security of security file storage.

[0127] The cloud server updates the pre-created archive management table based on the storage path of each encrypted archive, wherein the archive management table is further provided with at least an archive ID, an archive keyword, an archive creation time, and a business code field for associating different security archives;

[0128] By setting up an archive management table that includes at least storage path, archive ID, archive keyword, archive creation time and business code fields, it is convenient to quickly retrieve the corresponding encrypted archives later.

[0129] The security archive management module is specifically used for:

[0130] The cloud server obtains the input archive management instruction, parses the archive management instruction to obtain the instruction content, timestamp, identity identification code and hash value carried by the instruction, where the hash value is obtained by hashing the instruction content, timestamp and identity identification code;

[0131] By setting the archive management instructions to carry the instruction content, timestamp, identity identification code and hash value, a triple security check can be performed based on the timestamp, identity identification code and hash value, which greatly improves the security of secure archive management.

[0132] After the integrity check of the instruction content, timestamp and identity identification code is performed through the hash value, the validity check is performed through the timestamp, and then the legality check of the identity identification code is performed through the preset user management table;

[0133] Searching for a corresponding encrypted file based on the file ID carried in the instruction content and the file management table, decrypting the encrypted file to obtain a secure file, managing the addition, deletion, modification or query of the secure file, and recording access logs in real time;

[0134] When the security file is added, deleted or modified, the security file is encrypted, stored and backed up again, and the file management table is updated synchronously; when the security file is queried, the export of the security file is supported.

[0135] In summary, the advantages of the present invention are:

[0136] 1. Create several data templates for generating security files, set the collection parameters of security data, then collect and pre-process security data based on the collection parameters, analyze the pre-processed security data through big data analysis technology to obtain classification labels, classify each security data based on the classification labels, match the corresponding data templates for each category of security data based on the classification labels, map each security data into the corresponding data template to obtain the corresponding security file; then encrypt each security file into a corresponding encrypted file and store it in the cloud server; then the cloud server verifies the input file management instructions, decrypts the corresponding encrypted file based on the file management instructions to obtain the security file, and adds security files to the security files. , deletion, modification or query management; that is, the security archives are converted from traditional paper documents to electronic documents for management, and stored on cloud servers for remote management, which is not only convenient for retrieval and updating, but also does not need to take up a lot of physical space, and avoids the influence of moisture, insect infestation, fire, etc. Security archives can be shared and integrated between different departments or projects to eliminate the phenomenon of data islands, and automatically collect security data based on pre-set collection parameters and automatically generate security archives using data templates without manual operation, and ensure the standardization of security archives. Accessing security archives through cloud servers requires verification of archive management instructions to avoid illegal access, thereby greatly improving the reliability, efficiency and quality of security archive management.

[0137] 2. By setting collection parameters including at least data source address, account password, data collection frequency, data collection method, data collection logic and data format, security data can be automatically collected based on the data source address, account password, data collection frequency, data collection method and data collection logic, and the collected security data can be verified based on the data format, which greatly improves the efficiency and quality of security data collection.

[0138] 3. By preprocessing the collected security data, including at least missing value filling, outlier removal, duplicate value deletion, format conversion and data aggregation, and performing format verification on the preprocessed security data based on the data format, the quality of security data collection is greatly improved.

[0139] 4. Through natural language processing technology, text features are extracted from the pre-processed security data. Through the random forest algorithm in big data analysis technology, the text features of each security data are classified to obtain classification labels. Based on the classification labels, each security data is classified, which greatly improves the accuracy of security data classification and thus greatly improves the matching accuracy of data templates.

[0140] 5. Create a pair of public and private keys through the SM9 algorithm, perform HMAC calculation on the security file to obtain the MAC value, encrypt the security file and the MAC value into primary encrypted data through the private key, map the primary encrypted data through the mapping rule to obtain secondary encrypted data, swap the number 2 and the letter z in the secondary encrypted data to obtain the third-level encrypted data, encrypt the public key into the primary key through the Serpent algorithm, shift each character of the primary key to the right by 6 bits to obtain the secondary key, encapsulate the third-level encrypted data and the secondary key through the AES-KW algorithm to obtain an encrypted file, store the encrypted file in the cloud server through the TLS protocol, and send it to the cloud server through Min IO backs up the encrypted files to the NAS device; since the data encrypted by the private key can only be decrypted by the public key, and the security files and public keys are encrypted at multiple levels, the MAC value can be used for integrity verification later. The TLS protocol is a secure transmission protocol. Combined with data backup, at least 10 security measures are taken before and after (SM9 algorithm, public and private keys, HMAC calculation, mapping rules, 2 and z swap, Serpent algorithm, character displacement, AES-KW algorithm, TLS protocol, backup), which greatly improves the security of security file storage.

[0141] 6. By setting up an archive management table that includes at least storage path, archive ID, archive keyword, archive creation time and business code fields, it is convenient to quickly retrieve the corresponding encrypted archives later.

[0142] 7. By setting the file management instructions to carry the instruction content, timestamp, identity identification code and hash value, three-fold security verification can be performed based on the timestamp, identity identification code and hash value, which greatly improves the security of secure file management.

[0143] Although the specific implementation modes of the present invention are described above, those skilled in the art should understand that the specific implementation modes described are only illustrative and are not intended to limit the scope of the present invention. Equivalent modifications and changes made by those skilled in the art in accordance with the spirit of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. An intelligent security archive management method based on big data analysis, characterized by: The steps include: Step S1, creating several data templates for generating security files and setting the collection parameters of security data; Step S2, collecting safety data based on the collection parameters, and preprocessing each of the safety data; Step S3, analyzing the pre-processed security data by big data analysis technology to obtain classification labels, and classifying the security data based on the classification labels; Step S4, matching the corresponding data template for each category of security data based on the classification label, mapping and filling each security data into the corresponding data template to obtain a corresponding security file; Step S5, encrypting each of the security files into a corresponding encrypted file, and storing each of the encrypted files in a cloud server; Step S6: After the cloud server verifies the input file management instruction, it decrypts the corresponding encrypted file based on the file management instruction to obtain the security file, and manages the addition, deletion, modification or query of the security file.

2. The intelligent security archive management method based on big data analysis according to claim 1, characterized in that: The step S1 is specifically as follows: Creating a number of data templates for generating security files based on the file type, setting a unique template ID for each of the data templates, and storing each of the data templates in a pre-created template library; The data template is an Excel template or a Word template; the Excel template is created by jxls and contains static content and dynamic data, and the dynamic data is identified by a preset data tag; the Word template is created by poi-tl and has a number of custom tags; The collection parameters set for collecting security data include at least data source address, account password, data collection frequency, data collection method, data collection logic and data format.

3. The intelligent security archive management method based on big data analysis according to claim 1, characterized in that: The step S2 is specifically as follows: The ETL tool collects security data based on the collection parameters, performs preprocessing on each security data including at least missing value completion, outlier removal, duplicate value deletion, format conversion and data aggregation, and performs format verification on each preprocessed security data based on the data format.

4. The intelligent security archive management method based on big data analysis according to claim 1, characterized in that: The step S3 is specifically as follows: Extracting text features from each of the preprocessed security data using natural language processing technology, classifying the text features of each of the security data using a random forest algorithm in big data analysis technology to obtain classification labels, and classifying each of the security data based on the classification labels; The step S4 is specifically as follows: Based on the classification label, the corresponding data template is matched for each category of security data, and each security data is mapped and filled into the corresponding data template through Python to obtain the corresponding security file, and a unique file ID is set for each security file.

5. The intelligent security archive management method based on big data analysis according to claim 1, characterized in that: The step S5 is specifically as follows: A pair of public and private keys are created based on the SM9 algorithm, and HMAC calculations are performed on each of the security files to obtain a MAC value. Each security file and the corresponding MAC value are encrypted into primary encrypted data in turn through the private key. Each primary encrypted data is mapped through a preset mapping rule to obtain secondary encrypted data. The number 2 and the letter z in each secondary encrypted data are swapped to obtain a third-level encrypted data. The public key is encrypted into a primary key through the Serpent algorithm, and each character of the primary key is shifted right by 6 bits to obtain a secondary key. The third-level encrypted data and the secondary key are encapsulated through the AES-KW algorithm to obtain an encrypted file. Each encrypted file is stored in a cloud server through the TLS protocol, and each encrypted file is backed up to a NAS device through MinIO. The cloud server updates the pre-created archive management table based on the storage path of each encrypted archive, wherein the archive management table is further provided with at least an archive ID, an archive keyword, an archive creation time, and a business code field for associating different security archives; The step S6 is specifically as follows: The cloud server obtains the input archive management instruction, parses the archive management instruction to obtain the instruction content, timestamp, identity identification code and hash value carried by the instruction, where the hash value is obtained by hashing the instruction content, timestamp and identity identification code; After the integrity check of the instruction content, timestamp and identity identification code is performed through the hash value, the validity check is performed through the timestamp, and then the legality check of the identity identification code is performed through the preset user management table; Searching for a corresponding encrypted file based on the file ID carried in the instruction content and the file management table, decrypting the encrypted file to obtain a secure file, and managing the addition, deletion, modification or query of the secure file; When the security file is added, deleted or modified, the security file is encrypted, stored and backed up again, and the file management table is updated synchronously; When the security file is queried, the export of the security file is supported.

6. An intelligent security archive management system based on big data analysis, characterized by: Includes the following modules: Initialization module, used to create several data templates for generating security files and set the collection parameters of security data; A safety data acquisition module, used for acquiring safety data based on the acquisition parameters and preprocessing each safety data; A security data classification module, used to analyze the pre-processed security data by big data analysis technology to obtain classification labels, and classify the security data based on the classification labels; A security file generation module, used for matching the corresponding data template with the security data of each category based on the classification label, and mapping and filling each of the security data into the corresponding data template to obtain the corresponding security file; A security file encryption storage module, used to encrypt each of the security files into a corresponding encrypted file, and store each of the encrypted files in a cloud server; The security file management module is used for the cloud server to verify the input file management instruction, decrypt the corresponding encrypted file based on the file management instruction to obtain the security file, and manage the addition, deletion, modification or query of the security file.

7. The intelligent security archive management system based on big data analysis as claimed in claim 6, characterized in that: The initialization module is specifically used for: Creating a number of data templates for generating security files based on the file type, setting a unique template ID for each of the data templates, and storing each of the data templates in a pre-created template library; The data template is an Excel template or a Word template; the Excel template is created by jxls and contains static content and dynamic data, and the dynamic data is identified by a preset data tag; the Word template is created by poi-tl and has a number of custom tags; The collection parameters set for collecting security data include at least data source address, account password, data collection frequency, data collection method, data collection logic and data format.

8. The intelligent security archive management system based on big data analysis as claimed in claim 6, characterized in that: The safety data acquisition module is specifically used for: The ETL tool collects security data based on the collection parameters, performs preprocessing on each security data including at least missing value completion, outlier removal, duplicate value deletion, format conversion and data aggregation, and performs format verification on each preprocessed security data based on the data format.

9. The intelligent security archive management system based on big data analysis according to claim 6, characterized in that: The security data classification module is specifically used for: Extracting text features from each of the preprocessed security data using natural language processing technology, classifying the text features of each of the security data using a random forest algorithm in big data analysis technology to obtain classification labels, and classifying each of the security data based on the classification labels; The security profile generation module is specifically used for: Based on the classification label, the corresponding data template is matched for each category of security data, and each security data is mapped and filled into the corresponding data template through Python to obtain the corresponding security file, and a unique file ID is set for each security file.

10. The intelligent security archive management system based on big data analysis according to claim 6, characterized in that: The security archive encryption storage module is specifically used for: A pair of public and private keys are created based on the SM9 algorithm, and HMAC calculations are performed on each of the security files to obtain a MAC value. Each security file and the corresponding MAC value are encrypted into primary encrypted data in turn through the private key. Each primary encrypted data is mapped through a preset mapping rule to obtain secondary encrypted data. The number 2 and the letter z in each secondary encrypted data are swapped to obtain a third-level encrypted data. The public key is encrypted into a primary key through the Serpent algorithm, and each character of the primary key is shifted right by 6 bits to obtain a secondary key. The third-level encrypted data and the secondary key are encapsulated through the AES-KW algorithm to obtain an encrypted file. Each encrypted file is stored in a cloud server through the TLS protocol, and each encrypted file is backed up to a NAS device through MinIO. The cloud server updates the pre-created archive management table based on the storage path of each encrypted archive, wherein the archive management table is further provided with at least an archive ID, an archive keyword, an archive creation time, and a business code field for associating different security archives; The security archive management module is specifically used for: The cloud server obtains the input archive management instruction, parses the archive management instruction to obtain the instruction content, timestamp, identity identification code and hash value carried by the instruction, where the hash value is obtained by hashing the instruction content, timestamp and identity identification code; After the integrity check of the instruction content, timestamp and identity identification code is performed through the hash value, the validity check is performed through the timestamp, and then the legality check of the identity identification code is performed through the preset user management table; Searching for a corresponding encrypted file based on the file ID carried in the instruction content and the file management table, decrypting the encrypted file to obtain a secure file, and managing the addition, deletion, modification or query of the secure file; When the security file is added, deleted or modified, the security file is encrypted, stored and backed up again, and the file management table is updated synchronously; When the security file is queried, the export of the security file is supported.

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