Intelligent archive protection ten-prevention all-in-one machine system and cloud of things management method thereof

Abstract

The invention relates to the technical field of archive management, in particular to an intelligent archive protection ten-prevention all-in-one machine system and a cloud of things management method thereof. The method comprises the steps of collecting file information, storing a file to a corresponding storage module based on the file information, then obtaining user information and user calling information, setting a user level based on the user information, obtaining a target file based on the user calling information, judging whether a user can call the target file or not according to the user level, and finally obtaining a file calling record. According to the method, the retrieval graph is created according to the file retrieval, the multiple recommended files are screened out for the real-time user on the basis of the retrieval graph, the retrieval graph of the files is created by combining the user information and the file retrieval record, and the approximate files are recommended for the user, so that the file retrieval efficiency of the user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of file management, and particularly relates to an intelligent file protection ten-prevention integrated machine system and an Internet of Things cloud management method therefor. Background Art

[0002] With the rapid development of technology, intelligence has become an inevitable trend in the development of modern archives. In order to improve the security, reliability, and management efficiency of archives, ten-prevention integrated machines are increasingly used. The ten-prevention integrated machine has the characteristics of integration, visualization, integration, linkage, automation, intelligence, modernization, etc. By integrating and optimizing various systems, devices, and functions, resource sharing and collaborative work are achieved, and the security, reliability, and management efficiency of archives are improved.

[0003] Chinese Patent with publication number CN115033905A discloses an Internet of Things-based intelligent file management system platform. Through an intelligent environment controller, the manual management cost can be reduced and the work efficiency can be improved, realizing real-time sharing, long-term preservation, and reuse of document materials; a data resource library for unified and centralized utilization of various file resources is constructed to achieve document integration construction. However, in the prior art, the retrieval records of files are not combined to recommend to users, resulting in low search efficiency for files by users. Summary of the Invention

[0004] The object of the present invention is to address the problems in the background art and propose an intelligent file protection ten-prevention integrated machine system and a management method therefor.

[0005] The technical solution of the present invention: On the one hand, the present application provides an intelligent file protection ten-prevention integrated machine system, including: A cabinet for storing files through the cabinet; A protection module disposed inside the cabinet for protecting the files in the cabinet through the protection module; A data storage module for storing file data through the data storage module; A server for accessing and retrieving the file data stored in the data storage module through the server.

[0006] Preferably, the protection module includes a collection component and a processing component. The collection component is disposed inside the cabinet for collecting cabinet data through the collection component. The collection component is communicatively connected to the processing component to facilitate the transmission of cabinet data to the processing component.

[0007] On the other hand, the present application also provides an Internet of Things cloud management method for an intelligent file protection ten-prevention integrated machine system, including: Collecting file information and storing the files in the corresponding storage module based on the file information; Obtain user information and user retrieval information, and set the user level based on the user information; Obtain the target file based on the user retrieval information, and determine whether the user can retrieve the target file in combination with the user level; Obtain the file retrieval record, create a retrieval graph based on the file retrieval, and screen out multiple recommended files for the real-time user based on the retrieval graph.

[0008] Preferably, collect file information, and store the file in the corresponding storage module based on the file information, including: Collect file information; the file information includes file confidentiality level and file type; Store the file in the corresponding storage module according to the file confidentiality level.

[0009] Preferably, obtain user information and user retrieval information, and set the user level based on the user information, including: Collect user information; the user information includes user name, user occupation and user position; Calculate the comprehensive score of the user through Formula 1 in combination with the user information; Formula 1; Wherein, is the comprehensive score of the user, is the mth information of the user, is the weight of the mth information of the user; Set the user level and the comprehensive score interval corresponding to each user level; Set the user level according to the interval where the comprehensive score of the user is located.

[0010] Preferably, obtain the target file based on the user retrieval information, and determine whether the user can retrieve the target file in combination with the user level, including: Obtain the user retrieval information; Analyze the file that the user needs to retrieve according to the user retrieval information; record the file that the user needs to retrieve as the target file; Judge whether the user level is higher than or equal to the confidentiality level of the target file; If the user level is higher than or equal to the confidentiality level of the target document, retrieve and display the target document; If the user level is lower than the confidentiality level of the target document, prohibit the user from retrieving the target document.

[0011] Preferably, obtain the file retrieval record, create a retrieval graph based on the file retrieval, and screen out multiple recommended files for the real-time user based on the retrieval graph, including: Obtain the retrieval record of each file; Retrieve the retrieval information of each file based on the file retrieval record; the retrieval information includes the retrieval times, the retrieving users, the levels of the retrieving users, and the number of times each retrieving user at each level retrieves the file. Obtain the retrieving users of each file, and establish a connection relationship for files with the same retrieving users, thereby creating a file retrieval graph. Recommend similar files for real-time users by combining the file retrieval graph and real-time user information.

[0012] Preferably, obtaining the retrieving users of each file and establishing a connection relationship for files with the same retrieving users, thereby creating a file retrieval graph, includes: For all files, respectively obtain the retrieving users of each file; Record files with the same retrieving users as similar files, and obtain multiple groups of similar files; Select a group of similar files, and count the retrieval times of each file under this group of similar files; Connect all the files under this group of similar files in sequence based on the retrieval times from more to less, and obtain the retrieval graph of this group of similar files; Return and select a group of similar files until all groups of similar files are selected, and obtain multiple retrieval graphs.

[0013] Preferably, recommending similar files for real-time users by combining the file retrieval graph and real-time user information includes: Obtain real-time user information and real-time retrieval information; Obtain the corresponding file according to the real-time retrieval information; record the corresponding file as the real-time target file; Obtain the retrieval graph of the real-time target file; Screen out multiple recommended files according to the distance relationship of the file retrieval graph, and recommend the recommended files to the real-time user.

[0014] Preferably, the intelligent file protection ten-prevention integrated machine system IoT cloud management method further includes: Obtain the retrieval record of each file; Sort all files from largest to smallest according to the retrieval times, and screen out the top N files; record the top N files as high-frequency files; For each high-frequency file, calculate the average retrieval level of each high-frequency file respectively through formula 2; Formula 2; Wherein, is the average retrieval level of the high-frequency file, is the user level of the i-th user who retrieves the high-frequency file, and K is the total number of users who retrieve the high-frequency file, is the symbol of rounding down; Set the file level of the high-frequency file to the average retrieval level.

[0015] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects: By collecting file information, storing the file in the corresponding storage module based on the file information, then obtaining user information and user retrieval information, setting the user level based on the user information, obtaining the target file based on the user retrieval information, judging whether the user can retrieve the target file in combination with the user level, finally obtaining the file retrieval record, creating a retrieval map according to the file retrieval, and screening out multiple recommended files for the real-time user based on the retrieval map. This application creates a retrieval map of the file by combining user information and file retrieval records, and recommends similar files for the user, thereby improving the user's retrieval efficiency for the file. Brief Description of the Drawings

[0016] Figure 1 It is a schematic structural diagram of an intelligent file protection ten-prevention integrated machine system proposed by the present invention; Figure 2 It is a schematic flow diagram of an intelligent file protection ten-prevention integrated machine system's Internet of Things cloud management method proposed by the present invention; Reference Signs: 100, cabinet; 200, protection module; 201, acquisition component; 202, processing component; 300, data storage module; 400, server. Detailed Embodiments

[0017] Example 1, as Figure 1 shown, an intelligent file protection ten-prevention integrated machine system proposed by the present invention includes a cabinet 100, a protection module 200, a data storage module 300, and a server 400. The files are stored in the cabinet 100, the protection module 200 is arranged inside the cabinet 100 to protect the files in the cabinet 100, the file data is stored in the data storage module 300, and the server 400 accesses and retrieves the file data stored in the data storage module 300.

[0018] Specifically, the cabinet 100 described in this application is not limited to the physical cabinet 100. Any space that can store files can be regarded as the cabinet 100; In the present invention, the user can connect to the data storage module 300 through the server 400 and access the files with access rights after inputting user information. If the user level of the user is lower than the file level, the user cannot retrieve and access the file.

[0019] In an optional embodiment, the protection module 200 includes a collection component 201 and a processing component 202. The collection component 201 is disposed inside the cabinet 100, and the data of the cabinet 100 is collected through the collection component 201. The collection component 201 is communicatively connected to the processing component 202 to facilitate the transmission of the data of the cabinet 100 to the processing component 202.

[0020] It should be noted that the environmental data inside the cabinet 100 is detected in real time through the protection module 200, and the files inside the cabinet 100 are protected in combination with the environmental data. A variety of different types of sensors constantly detect various data inside the cabinet 100, including the internal temperature and humidity of the cabinet 100, and transmit the detected data inside the cabinet 100 to the processing component 202. When the parameters inside the cabinet 100 are abnormal, a warning is sent to the user through the processing component 202.

[0021] Such as Figure 2 shown, the present application also provides an Internet of Things cloud management method for an intelligent file protection ten-prevention integrated machine system, which is applied to the intelligent file protection ten-prevention integrated machine system described in Embodiment 1, and includes: S100, collecting file information, and storing the file in the corresponding storage module based on the file information; S200, obtaining user information and user retrieval information, and setting the user level based on the user information; S300, obtaining the target file based on the user retrieval information, and judging whether the user can retrieve the target file in combination with the user level; S400, obtaining the file retrieval record, creating a retrieval map according to the file retrieval, and screening out multiple recommended files for the real-time user based on the retrieval map.

[0022] It should be noted that by collecting file information, storing the file in the corresponding storage module based on the file information, then obtaining user information and user retrieval information, setting the user level based on the user information, obtaining the target file based on the user retrieval information, judging whether the user can retrieve the target file in combination with the user level, finally obtaining the file retrieval record, creating a retrieval map according to the file retrieval, and screening out multiple recommended files for the real-time user based on the retrieval map, the present application creates a retrieval map of the file by combining the user information and the file retrieval record, and recommends similar files to the user, thereby improving the retrieval efficiency of the user for the file.

[0023] In an optional embodiment, the S100 includes: S110, collecting file information; the file information includes the file confidentiality level and file type; S120, storing the file in the corresponding storage module according to the file confidentiality level.

[0024] It should be noted that in the present application, multiple cabinets can be set, and each cabinet corresponds to an archive level, so that only the archives corresponding to its level are stored in each cabinet, thereby avoiding the leakage of high-level archives.

[0025] In an optional embodiment, the S200 includes: S210, collecting user information; the user information includes the user name, user occupation, and user position; S220, calculating the comprehensive score of the user through Formula 1 in combination with the user information; Formula 1; Wherein, is the comprehensive score of the user, is the m-th information of the user, is the weight of the user's m-th information; S230, setting the user level and the comprehensive score interval corresponding to each user level; S240, setting the user level according to the interval where the comprehensive score of the user is located.

[0026] It should be noted that by calculating the comprehensive score of each user in combination with the user information and setting the corresponding level for each user based on the comprehensive score of the user, the higher the user level, the higher the access permission it has, that is, the higher the highest level of the archives it can access. High-level users can view all archives whose archive levels are less than or equal to their user levels.

[0027] In an optional embodiment, the S300 includes: S310, obtaining user access information; S320, parsing the archives that the user needs to access according to the user access information; the archives that the user needs to access are recorded as target archives; S330, judging whether the user level is higher than or equal to the confidentiality level of the target archive; S340, if the user level is higher than or equal to the confidentiality level of the target document, then access the target document and display it; S350, if the user level is lower than the confidentiality level of the target document, then prohibit the user from accessing the target document.

[0028] It should be noted that by comparing the seniority of the user level and the archive level, it is judged whether the user has the right to access the target archive. Doing so can, on the one hand, avoid the leakage of the content of archives with higher archive levels, and on the other hand, it can also directly narrow the search range, that is, search for the user's target archive within the range of archive levels less than or equal to the user level, thereby improving the efficiency of the user accessing the archive.

[0029] In an optional embodiment, the S400 includes: S410, obtaining the retrieval records of each file; S420, obtaining the retrieval information of each file based on the retrieval records of the file; the retrieval information includes the number of retrievals, the retrieval user, the level of the retrieval user, and the number of times each retrieval user at each level retrieves the file; S430, obtaining the retrieval users of each file, and establishing a connection relationship for the files with the same retrieval user, thereby creating a file retrieval graph; S440, combining the file retrieval graph with real-time user information to recommend approximate files for the real-time user; Specifically, the approximate file refers to a file that is relatively close to the target file searched by the user. Since the approximate file and the target file are on the same retrieval graph, the approximate file and the target file have certain commonalities. Therefore, by recommending the approximate file to the user as well, the search results of the user can be expanded; It should be noted that after the ten-prevention integrated machine system has been used for a period of time, a large number of user search records and file retrieval records can be obtained. Based on these data records, a file retrieval graph can be established, thereby creating a connection between files. When the user retrieves files subsequently, approximate files with commonalities with the target file can be recommended to the user in combination with the user information. At the same time, as the ten-prevention integrated machine system is continuously used, the user search records and file retrieval records will gradually increase, thereby continuously improving the recommendation accuracy of approximate files.

[0030] In an optional embodiment, the S430 includes: S431, for all files, obtaining the retrieval users of each file respectively; Optionally, in addition to using the "retrieval user" as the standard for classifying file types, other indicators can also be selected as the classification standard. Different classification standards have different focuses. For example, when using the "retrieval season" as the classification standard, the recommendation of approximate files will be more inclined to recommend other files in the same season as the target file; S432, recording the files with the same retrieval user as the same type of files, and obtaining multiple same-type files; S433, selecting a same-type file and counting the number of retrievals of each file under this same-type file; S434, connecting all the files under this same-type file in sequence based on the number of retrievals from more to less, and obtaining the retrieval graph of this same-type file; Specifically, the file with more retrieval times is closer to the starting point, which means that the connection between this file and the starting point is stronger. Therefore, it is worthy of being recommended first when recommending; S435, return to step S433 until all files of the same type are selected, obtaining multiple retrieved graphs.

[0031] It should be noted that in this application, by using the "retrieving user" as the standard for establishing the retrieved graph, a retrieved graph starting from the searching user is created, and the "retrieving times" is used as the distance between the approximate file and the starting point. The closer the approximate file is to the starting point, the more common it is with the target file, and thus the closer the approximate file is to the starting point, the more worthy it is to be recommended.

[0032] In an optional embodiment, the S440 includes: S441, obtaining real-time user information and real-time retrieval information; S442, obtaining the corresponding file according to the real-time retrieval information; recording the corresponding file as the real-time target file; S443, obtaining the retrieved graph of the real-time target file; S444, screening out multiple recommended files according to the distance relationship of the file retrieved graph, and recommending the recommended files to the real-time user; Specifically, after obtaining the recommended files, it is still necessary to judge the file level and user level of the recommended files. If the user level is lower than the file level of the recommended files, the user cannot retrieve the recommended files.

[0033] It should be noted that in the foregoing embodiment, the creation of the file retrieved graph has been completed. Therefore, after obtaining the real-time user information, first establish a communication connection with the data storage module through the server, then retrieve the real-time target file of the user from the data storage module based on the real-time retrieval information, and finally, based on the retrieved graph of the real-time target file, obtain multiple recommended files and recommend them to the real-time user. Through the retrieved graph, the ten-prevention integrated machine system can automatically recommend multiple recommended files for the user outside the target file retrieved by the user itself, thereby improving the retrieval efficiency of the user for files.

[0034] In an optional embodiment, the intelligent file protection ten-prevention integrated machine system IoT cloud management method further includes: S500, obtaining the retrieval record of each file; S510, sorting all files from largest to smallest according to the retrieval times, and screening out the top N files; recording the top N files as high-frequency files; S520, for each high-frequency file, calculating the average retrieval level of each high-frequency file respectively through formula 2; Formula 2; Wherein, is the average retrieval level of the high-frequency file, is the user level of the i-th user who retrieves the high-frequency file, and K is the total number of users who retrieve the high-frequency file. is the symbol for rounding down; S530, set the file level of the high-frequency file to the average retrieval level; Optionally, since some files are confidential, some files can be set as confidential files, and the confidential files are excluded when step S500 is executed, so as to avoid the leakage of the content of the confidential files.

[0035] It should be noted that after the ten-prevention all-in-one machine system has been used for a long time, there are a large number of retrieval records for the internal files. The more times a file is retrieved, the more it represents that this file is needed by users. That is, the retrieval times can reflect the utility of a file to a certain extent. The more times a file is retrieved, the better the utility of the file. Therefore, in order to enable files with better utility to be retrieved by more users, the file level of the file is updated by combining the retrieval times of the file and the user level of the file retrieval user, so that the file level is more in line with the actual retrieval data, and the adaptive update and setting of the file level are realized.

[0036] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made without departing from the spirit of the present invention within the knowledge scope of those skilled in the art.

Claims

1. An intelligent file protection and ten-prevention integrated machine system, characterized in that: include: A cabinet, through which files are stored; A protection module, which is disposed inside the cabinet and protects the files inside the cabinet; A data storage module, through which archive data is stored; The server accesses and retrieves the archive data stored in the data storage module.

2. The intelligent file protection and ten-prevention integrated machine system according to claim 1 is characterized in that: The protection module comprises: A collection component, which is arranged inside the cabinet and collects cabinet data through the collection component; specifically, the collection component includes a temperature sensor, a humidity sensor and a smoke sensor; The processing component, the acquisition component is communicatively connected with the processing component to facilitate the transmission of cabinet data to the processing component.

3. An IoT cloud management method for an intelligent file protection and ten-prevention integrated machine system, applied to the intelligent file protection and ten-prevention integrated machine system as claimed in any one of claims 1 to 2, characterized in that: include: Collecting file information, and storing the file in a corresponding storage module based on the file information; Obtain user information and user retrieval information, and set user levels based on user information; Obtain the target file based on the user's retrieval information, and determine whether the user can retrieve the target file based on the user's level; Obtain archive retrieval records, create a retrieval map based on archive retrieval, and filter out multiple recommended archives for real-time users based on the retrieval map.

4. The method for managing the IoT cloud of the intelligent file protection and ten-prevention integrated machine system according to claim 3 is characterized in that: Collecting file information and storing the file in a corresponding storage module based on the file information includes: Collecting file information; the file information includes file confidentiality level and file type; The files are stored in the corresponding storage module according to the file confidentiality level.

5. The method for managing the IoT cloud of the intelligent file protection and ten-prevention integrated machine system according to claim 4 is characterized in that: Obtain user information and user retrieval information, and set user levels based on user information, including: Collect user information; the user information includes user name, user occupation and user position; Combine the user information and calculate the user's comprehensive score using Formula 1; Formula 1; in, is the user's comprehensive score, is the user's mth information, is the weight of the user’s mth information; Set user levels and the comprehensive score range corresponding to each user level; The user level is set according to the range of the user's comprehensive score.

6. The method for managing the IoT cloud of the intelligent file protection and ten-prevention integrated machine system according to claim 5 is characterized in that: Obtain the target file based on the user's retrieval information, and determine whether the user can retrieve the target file based on the user's level, including: Get user retrieval information; Analyze the files that the user needs to retrieve according to the user retrieval information; record the files that the user needs to retrieve as target files; Determine whether the user level is higher than or equal to the confidentiality level of the target file; If the user level is higher than or equal to the confidentiality level of the target document, the target document is retrieved and displayed; If the user level is lower than the confidentiality level of the target document, the user is prohibited from accessing the target document.

7. The method for managing the IoT cloud of the intelligent file protection and ten-prevention integrated machine system according to claim 6 is characterized in that: Obtain archive retrieval records, create a retrieval graph based on archive retrieval, and filter out multiple recommended archives for real-time users based on the retrieval graph, including: Obtain the retrieval record of each file; Acquire the retrieval information of each file based on the retrieval record of the file; the retrieval information includes the number of retrievals, the retrieval user, the level of the retrieval user, and the number of times the retrieval user of each level has retrieved the file; Get the user who retrieved each file, and establish a connection relationship between files with the same retrieval user, so as to create a file retrieval map; Combine the profile retrieval graph with real-time user information to recommend similar profiles to real-time users.

8. The method for managing the IoT cloud of the intelligent file protection and ten-prevention integrated machine system according to claim 7 is characterized in that: Get the user who retrieved each file, and establish a connection relationship between files with the same retrieval user, so as to create a file retrieval graph, including: For all files, obtain the user who retrieved each file; Record the files of the same user as the same type of files, and obtain multiple files of the same type; Select a file of the same type and count the number of times each file under the same type is retrieved; All the files under the same type of files are sequentially connected based on the order of the number of retrievals from most to least, to obtain a retrieval map of the same type of files; Return to select a similar file until all similar files are selected, and multiple retrieved maps are obtained.

9. The method for managing the IoT cloud of the intelligent file protection and ten-prevention integrated machine system according to claim 8 is characterized in that: Combine the profile retrieval graph with real-time user information to recommend similar profiles to real-time users, including: Obtain real-time user information and retrieve information in real time; Acquire the corresponding file according to the real-time retrieved information; record the corresponding file as the real-time target file; Obtain a real-time target file retrieval map; According to the distance relationship of the archive retrieval graph, multiple recommended archives are screened out and recommended to real-time users.

10. The method for managing the IoT cloud of the intelligent file protection and ten-prevention integrated machine system according to claim 9 is characterized in that: The IoT cloud management method of the intelligent archive protection ten-prevention integrated machine system also includes: Obtain the retrieval record of each file; Sort all the files according to the number of retrievals from large to small, and select the first N files; record the first N files as high-frequency files; For each high-frequency file, the average retrieval level of each high-frequency file is calculated by Formula 2; Formula 2: in, is the average retrieval level of high-frequency files, is the user level of the i-th user who retrieves the high-frequency file, K is the total number of users who retrieve the high-frequency file, is the floor symbol; Set the file level of high-frequency files to the average retrieval level.

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