Archive management method and system based on cloud storage and artificial intelligence authentication

Abstract

The invention relates to the technical field of archive management, and discloses an archive management method and system based on cloud storage and artificial intelligence authentication, and the method comprises the steps: calculating the scene utilization rate of archive information, calculating the user utilization rate of the archive information, and determining the total utilization rate of the archive information through the scene utilization rate and the user utilization rate; extracting a low-utilization-rate file in the file information by using the total utilization rate, obtaining a regular calling channel of the low-utilization-rate file, identifying an actual calling channel of the low-utilization-rate file, and judging whether the low-utilization-rate file is called by the regular calling channel or not by using the actual calling channel and the regular calling channel; generating a channel optimization scheme of low-utilization-rate archives in the archive information; constructing a content adjustment scheme of low-utilization-rate archives in the archive information; and distributing an information serial number for the archive information, analyzing a serial number collision value of the archive information, and generating a position improvement scheme of the archive information by using the serial number collision value. According to the method, file data redundancy can be reduced, and content query convenience and index query efficiency are improved.

Description

Technical Field

[0001] The present invention relates to a cloud storage and artificial intelligence authentication archive management method and system, belonging to the technical field of archive management. Background Art

[0002] With the popularization of cloud storage, most of the existing archives have been separated from the paper-based archive management method and have been stored in cloud systems. The method of archive management through cloud storage is efficient and reliable, but there are still some problems.

[0003] First, when users upload paper archive records in the warehouse to the cloud, they will not only upload the most recently added archive data, but also upload old archive data from many years ago. With the development of the times, some old archive data may be unavailable because they cannot adapt to the development of the times, so they are "accumulated dust" in the cloud system. Therefore, archive management needs to consider the redundancy of archive data caused by these old archive data. Secondly, the archive layout, content and order of querying archives (using the mouse to click on the A archive data on the front-end interface to enter, find the B archive data, and then enter the C archive data from the B archive data) may not be convenient enough for users to read. At this time, some professionals responsible for data collection and statistics may use their personal In the name of , the layout, content and sequence of these archives are reconstructed to attract users to watch and earn traffic. This process shows that the layout, content and reading sequence of archives in the cloud system are not suitable for users. Therefore, it is necessary to optimize the layout, content and sequence of archives when querying archives, so as to increase the number of users entering the cloud system to query archives. Finally, when managing archives, an index structure of archives will be constructed, and archive data will be queried through multi-level indexes. When users use the index structure to query data, the names of the two indexes E and F are similar, which causes the user to want to query the archives in the F index, but mistakenly query the archives in the E index. At this time, the index of the archives also needs to be managed.

[0004] Therefore, there is an urgent need for a solution that can reduce archive data redundancy, improve content query convenience and index query efficiency. Summary of the invention

[0005] The present invention provides a cloud storage and artificial intelligence authentication file management method and system, the main purpose of which is to improve the intelligence of ventilator data analysis and treatment suggestions.

[0006] To achieve the above purpose, the present invention provides a cloud storage and artificial intelligence authentication file management method, comprising:

[0007] Obtaining archive information from a cloud storage system, identifying the actual call time of the archive information, collecting activity scene data at the actual call time, analyzing the application scenario of the archive information from the activity scene data, and querying the user group of the archive information in the reference scenario based on an artificial intelligence authentication method;

[0008] Based on the application scenario, the scenario utilization rate of the archive information is calculated, according to the user group, the user utilization rate of the archive information is calculated, and the total utilization rate of the archive information is determined by using the scenario utilization rate and the user utilization rate;

[0009] Extracting low-utilization files from the file information using the total utilization, obtaining a regular calling channel for the low-utilization files, identifying an actual calling channel for the low-utilization files, and determining whether the low-utilization files are called by a regular channel using the actual calling channel and the regular calling channel;

[0010] When the low-utilization file is not called by a regular channel, a channel optimization plan for the low-utilization file in the file information is generated;

[0011] When the low-utilization file is called by a regular channel, construct a content adjustment plan for the low-utilization file in the file information;

[0012] Assigning an information serial number to the archive information, analyzing a serial number collision value of the archive information based on the information serial number, and generating a position improvement plan for the archive information using the serial number collision value;

[0013] The channel optimization scheme, the content adjustment scheme and the location improvement scheme are used to perform archive management on the archive information in the cloud storage system to obtain an archive management result of the archive information.

[0014] Optionally, the calculating the scenario utilization rate of the archive information based on the application scenario includes:

[0015] Setting a unit call time period for the archive information;

[0016] Extract the number of unit calls of the archive information within the unit call period;

[0017] Query the application scenario time period of the application scenario;

[0018] Obtaining a scene calling period belonging to the application scene period in the unit calling period;

[0019] Extract the scene call times belonging to the scene call time period from the unit call times:

[0020] Calculating the scene density of the archive information in the application scene period based on the unit call number and the scene call number;

[0021] The scene density is used as the scene utilization rate of the archive information.

[0022] Optionally, calculating the user utilization rate of the archive information according to the user group includes:

[0023] Setting the characteristic category of the archival information;

[0024] Extract the number of category calls of the archive information within the feature category;

[0025] Recording the identity characteristics of the user group;

[0026] Acquire user features belonging to the identity features in the feature category;

[0027] Extracting the feature call times belonging to the user feature from the category call times;

[0028] Calculating the feature density of the profile information with respect to the identity feature based on the category call count and the feature call count;

[0029] The feature density is used as the user utilization rate of the profile information.

[0030] Optionally, the using the actual calling channel and the regular calling channel to determine whether the low-utilization file is called by a regular channel includes:

[0031] Selecting a first calling channel among the actual calling channels that is consistent with the regular calling channel;

[0032] Selecting a second calling channel in the actual calling channel that is inconsistent with the regular calling channel;

[0033] Query the first calling number and the second calling number of the first calling channel and the second calling channel respectively;

[0034] Calculating a regular call rate of the low-utilization file according to the first call number and the second call number;

[0035] When the regular call rate is greater than a preset call rate threshold, it is determined that the low-utilization file is called by a regular channel; when the regular call rate is not greater than the preset call rate threshold, it is determined that the low-utilization file is not called by a regular channel.

[0036] Optionally, the channel optimization scheme for generating the low-utilization archive in the archive information includes:

[0037] Query the informal files when the low-utilization files are not called by the formal channels;

[0038] Identify a first file format, a first index sequence, and a first file keyword of the informal file;

[0039] Obtaining a second file layout, a second index sequence, and a second file keyword of the low-utilization file;

[0040] Calculating the layout similarity between the first file layout and the second file layout;

[0041] Calculating a sequence similarity between the first index sequence and the second index sequence;

[0042] Calculating keyword similarity between the first archive keyword and the second archive keyword;

[0043] Using the layout similarity, the sequence similarity and the keyword similarity, respectively screening out abnormal file layouts, abnormal index sequences and abnormal file keywords in the second file layout, second index sequence and second file keywords;

[0044] The channel optimization scheme corresponding to the abnormal file layout, the abnormal index sequence and the abnormal file keyword is set by using the first file layout, the first index sequence and the first file keyword.

[0045] Optionally, the step of constructing a content adjustment scheme for low-utilization archives in the archive information includes:

[0046] Performing information segmentation on the archive information to obtain segmentation information;

[0047] Acquire non-low-utilization archives in the archive information;

[0048] querying whether the segmentation information exists in the non-low utilization file;

[0049] When the segmentation information exists in the non-low-utilization file, distinguishing existing information from non-existing information in the segmentation information;

[0050] Setting a first content adjustment scheme to delete the existing information from the low-utilization archive;

[0051] Constructing a transition probability matrix of the non-existence information;

[0052] Based on the transition probability matrix, construct an inference probability model of the non-existence information;

[0053] When the value of the inference probability model is greater than a preset probability threshold, determining whether the amount of inferred information in the inference probability model is lower than a preset amount threshold;

[0054] When the amount of inferred information in the inference probability model is lower than a preset amount threshold, the inferred information is deleted from the non-existent information as a second content adjustment scheme;

[0055] The first content adjustment scheme and the second content adjustment scheme are used to determine a content adjustment scheme for low-utilization files in the file information.

[0056] Optionally, the allocating an information serial number to the archive information includes:

[0057] An information node for collecting the archival information;

[0058] According to the superior-subordinate relationship and the peer relationship in the information nodes, the information nodes are sorted to obtain a node sequence;

[0059] Node values ​​are assigned to the node sequence in ascending order to obtain information sequence numbers.

[0060] Optionally, analyzing the sequence number collision value of the archive information based on the information sequence number includes:

[0061] Collect the number of information calls corresponding to the first sequence number in the information sequence number;

[0062] querying the target information called after calling the information corresponding to the first sequence number within a continuous period;

[0063] Obtaining a second serial number of the target information from the information serial number;

[0064] When the second serial number is not greater than the first serial number, determining a serial number collision value of the archive information;

[0065] When the second serial number is greater than the first serial number, a serial number collision value of the archive information is determined.

[0066] Optionally, the location improvement scheme for generating the archive information by using the sequence number collision value includes:

[0067] Get the sequence number collision value when the second sequence number is greater than the first sequence number;

[0068] Determine whether the second sequence number is a leaf sequence number of the first sequence number;

[0069] When the second serial number is a leaf serial number of the first serial number, generating a location improvement plan for the archive information;

[0070] When the second sequence number is not the leaf sequence number of the first sequence number, exchanging the node structure of the first sequence number with the node structure of the second sequence number is used as a position improvement solution.

[0071] In order to solve the above problems, the present invention also provides a cloud storage and artificial intelligence authentication archive management system, the system comprising:

[0072] A user query module is used to obtain archive information from the cloud storage system, identify the real call time of the archive information, collect activity scene data at the real call time, analyze the application scenario of the archive information from the activity scene data, and query the user group of the archive information in the reference scenario based on the artificial intelligence authentication method;

[0073] A utilization rate determination module, configured to calculate the scenario utilization rate of the archive information based on the application scenario, calculate the user utilization rate of the archive information according to the user group, and determine the total utilization rate of the archive information by using the scenario utilization rate and the user utilization rate;

[0074] a regularity determination module, configured to extract low-utilization files from the file information by using the total utilization, obtain regular calling channels of the low-utilization files, identify actual calling channels of the low-utilization files, and determine whether the low-utilization files are called by regular channels by using the actual calling channels and the regular calling channels;

[0075] A channel optimization module, used for generating a channel optimization plan for the low-utilization file in the file information when the low-utilization file is not called by the regular channel;

[0076] A content construction module, used for constructing a content adjustment plan for the low-utilization archive in the archive information when the low-utilization archive is called by a regular channel;

[0077] A position generation module, used to assign an information serial number to the archive information, analyze the serial number collision value of the archive information based on the information serial number, and generate a position improvement plan for the archive information using the serial number collision value;

[0078] The archive management module is used to perform archive management on the archive information in the cloud storage system by using the channel optimization scheme, the content adjustment scheme and the location improvement scheme to obtain the archive management result of the archive information.

[0079] Compared with the problem described in the background technology, the embodiment of the present invention calculates the scenario utilization rate of the archive information based on the application scenario, so as to use the scenario utilization rate to characterize whether a certain archive information is frequently mobilized in the application scenario. In non-application scenarios, the user will not need this archive information and will not call it. Therefore, the archive call rate when the user has a demand for this archive information is counted to characterize whether this archive information is useful, that is, whether the user needs this archive data. Furthermore, the embodiment of the present invention determines whether the low-utilization archive is called by the regular channel by using the actual call channel and the regular call channel, so as to not immediately determine that the archive information is invalid data that the user does not need when the archive information has a low utilization rate, but instead determines whether the user queries the archive information because the channel is blocked by identifying the channel through which the user queries the archive information. Failed to find the archive information. If the user cannot find the archive information because the query channel is blocked, it means that the low utilization rate of the archive information is not because the user does not need it. The embodiment of the present invention constructs a content adjustment scheme for low-utilization archives in the archive information to remove redundant data that the user does not need. Further, the embodiment of the present invention analyzes the sequence number collision value of the archive information based on the information sequence number to identify whether the user will exit this window and access other windows with similar names after accessing the archive data under a certain front-end interface window. If accessed, it means that the archive in the window accessed for the first time is the archive that the user mistakenly clicked in to view. That is to say, the user mistakenly clicked in to view, which will generate a conflict value and collision value. Further, the embodiment of the present invention generates a location improvement scheme for the archive information by using the sequence number collision value to improve the index query efficiency. Therefore, the cloud storage and artificial intelligence authentication archive management method proposed by the present invention can reduce the redundancy of archive data, improve the convenience of content query and the efficiency of index query. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A schematic diagram of a process of a cloud storage and artificial intelligence authentication file management method provided in one embodiment of the present invention;

[0081] Figure 2 A schematic diagram of a module for implementing the cloud storage and artificial intelligence authentication archive management method provided in accordance with an embodiment of the present invention.

[0082] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0083] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0084] The embodiment of the present application provides a method for managing archives based on cloud storage and artificial intelligence authentication. The execution subject of the method for managing archives based on cloud storage and artificial intelligence authentication includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for managing archives based on cloud storage and artificial intelligence authentication can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0085] Embodiment 1:

[0086] Reference Figure 1 FIG. 1 is a flow chart of a method for managing a file based on cloud storage and artificial intelligence authentication according to an embodiment of the present invention. In this embodiment, the method for managing a file based on cloud storage and artificial intelligence authentication includes:

[0087] S1. Obtain archival information from a cloud storage system, identify the actual call time of the archival information, collect activity scene data at the actual call time, analyze the application scenario of the archival information from the activity scene data, and query the user group of the archival information in the reference scenario based on an artificial intelligence authentication method.

[0088] In an embodiment of the present invention, the archival information refers to archival data applied to a certain scenario, such as the treatment file of a patient in a medical scenario. The actual call time refers to the moment when the archival information is called and queried by the front-end user. The activity scenario data refers to the activity data that occurs at the actual call time. For some archival data that are not often called, such as personal tax files, only when personal income tax data needs to be counted every year, the user uses his own tax payment file to call. That is to say, the activity of needing to count personal income tax data every year is the activity scenario data. The application scenario refers to the activity related to the archival information in the activity scenario data. For example, the aforementioned personal tax statistical activity is not related to the archival data in the medical scenario, so the personal tax statistical activity cannot be used as an application scenario. Furthermore, the artificial intelligence authentication method refers to authenticating whether the user's identity is legal when the front-end user logs in to the front-end system of the archival data, and recording the time, age, gender and other data when the user calls the file.

[0089] Optionally, the process of querying the user group of the archive information in the reference scenario based on the artificial intelligence authentication method refers to querying the users who make archive calls and the time, age, gender and other data of these users who call the archives.

[0090] The user group refers to users who have called up the archives and include data such as time, age, and gender.

[0091] S2. Based on the application scenario, calculate the scenario utilization rate of the archive information, calculate the user utilization rate of the archive information according to the user group, and determine the total utilization rate of the archive information by using the scenario utilization rate and the user utilization rate.

[0092] The embodiment of the present invention calculates the scenario utilization rate of the archival information based on the application scenario, so as to use the scenario utilization rate to characterize whether a certain archival information is frequently mobilized in the application scenario. In non-application scenarios, this archival information will not be called because there is no demand for it. Therefore, the archival call rate when the user has a demand for this archival information is counted to characterize whether this archival information is useful, that is, whether the user needs this archival data.

[0093] In one embodiment of the present invention, the scene utilization rate of the archive information is calculated based on the application scenario, including: setting a unit call time period of the archive information; extracting the number of unit calls of the archive information within the unit call time period; querying the application scenario time period of the application scenario; obtaining the scene call time period belonging to the application scenario time period in the unit call time period; extracting the number of scene calls belonging to the scene call time period from the unit call times: based on the unit call times and the scene call times, calculating the scene density of the archive information in the application scenario time period using the following formula:

[0094]

[0095] Among them, a represents the scene density, x i represents the number of unit calls in the i-th unit call period, n represents the total number of unit call periods, represents the number of scene calls in the jth scene call period of the kth application scene, and m represents the total number of scene call periods of the kth application scene;

[0096] The scene density is used as the scene utilization rate of the archive information.

[0097] Among them, the unit call time period refers to a series of continuous time periods in a certain year or month. For example, if each week of a certain year is a unit call time period, then the first week, the second week, etc. of a certain year are regarded as a series of continuous unit call time periods. The unit call number refers to the total number of times this archive information is called within a unit call time period.

[0098] In one embodiment of the present invention, the user utilization rate of the archive information is calculated according to the user group, including: setting a feature category of the archive information; extracting the category call times of the archive information within the feature category; recording the identity features of the user group; obtaining the user features belonging to the identity features in the feature category; extracting the feature call times belonging to the user features from the category call times; and calculating the feature density of the archive information with respect to the identity features using the following formula based on the category call times and the feature call times:

[0099]

[0100] Among them, b represents the feature density, x' i' represents the number of category calls for the i'th feature category, n' represents the total number of feature categories, represents the number of scene calls of the jth user feature of the k'th user group, and m represents the total number of user features of the kth user group;

[0101] The feature density is used as the user utilization rate of the profile information.

[0102] The feature category refers to the identity feature of the user when the profile information is called by the user, such as gender, age, etc., and the category call count refers to the call count belonging to the same feature category.

[0103] The total utilization is the weighted sum of the scene utilization and the user utilization.

[0104] S3. Use the total utilization to extract low-utilization files in the file information, obtain the regular calling channel of the low-utilization files, identify the actual calling channel of the low-utilization files, and use the actual calling channel and the regular calling channel to determine whether the low-utilization files are called by the regular channel.

[0105] Optionally, the process of extracting low-utilization archives in the archive information by using the total utilization rate can filter the total utilization rate by setting a utilization rate threshold to obtain low utilization rate, and then obtain archive information corresponding to the low utilization rate.

[0106] Furthermore, the embodiment of the present invention uses the actual calling channel and the regular calling channel to determine whether the low-utilization file is called by the regular channel, so that when the file information is low-utilization, it does not immediately determine that the file information is invalid data that the user does not need, but instead determines whether the user cannot query the file information because the channel is blocked by identifying the channel through which the user queries the file information. If the user cannot query the file information because the query channel is blocked, it means that the low utilization of the file information is not because the user does not need it.

[0107] Among them, the formal calling channel is set up to query the archival information through the software and system where the archival information is located. The actual calling channel includes channels that are consistent with the formal calling channel and channels that are inconsistent with the formal calling channel. For example, some professionals who mainly monetize traffic use channels to obtain archival information. Simply put, the channel refers to the channel through which users obtain archival information.

[0108] In one embodiment of the present invention, the use of the actual calling channel and the regular calling channel to determine whether the low utilization file is called by the regular channel includes: selecting a first calling channel in the actual calling channel that is consistent with the regular calling channel; selecting a second calling channel in the actual calling channel that is inconsistent with the regular calling channel; respectively querying the first calling number and the second calling number of the first calling channel and the second calling channel; and calculating the regular calling rate of the low utilization file according to the first calling number and the second calling number using the following formula:

[0109]

[0110] Where c represents the regular call rate, u represents the number of first calls, and v represents the number of second calls;

[0111] When the regular call rate is greater than a preset call rate threshold, it is determined that the low-utilization file is called by a regular channel; when the regular call rate is not greater than the preset call rate threshold, it is determined that the low-utilization file is not called by a regular channel.

[0112] The call rate threshold is set according to the actual scenario and will not be further described here.

[0113] S4. When the low-utilization file is not called by a regular channel, a channel optimization solution for the low-utilization file in the file information is generated.

[0114] The embodiment of the present invention generates a channel optimization solution for low-utilization archives in the archive information to improve the convenience of content query.

[0115] In one embodiment of the present invention, the channel optimization scheme for generating the low-utilization archives in the archive information includes: querying the informal archives when the low-utilization archives are not called by the formal channel; identifying the first archive typesetting, the first index sequence and the first archive keyword of the informal archive; obtaining the second archive typesetting, the second index sequence and the second archive keyword of the low-utilization archive; calculating the typesetting similarity between the first archive typesetting and the second archive typesetting; calculating the sequence similarity between the first index sequence and the second index sequence; calculating the keyword similarity between the first archive keyword and the second archive keyword; using the typesetting similarity, the sequence similarity and the keyword similarity to respectively screen out abnormal archive typesetting, abnormal index sequence and abnormal archive keyword in the second archive typesetting, the second index sequence and the second archive keyword; using the first archive typesetting, the first index sequence and the first archive keyword to set the channel optimization scheme corresponding to the abnormal archive typesetting, the abnormal index sequence and the abnormal archive keyword.

[0116] Among them, the layout similarity is the similarity between the Sth position in the first file layout and the Sth position in the second file layout, and the calculation method is similar to the calculation of image pixel similarity. The sequential similarity refers to the similarity of the front and back order of the query file data. For example, first query the A file data, and then query the B file data of the next level of the A file data. The query process is a tree node traversal process, because the multi-level structure of the index is a tree structure. The abnormal file layout, abnormal index sequence and abnormal file keywords refer to the second file layout, second index sequence and second file keywords that are lower than the similarity threshold. The channel optimization scheme refers to correcting the abnormal file layout, abnormal index sequence and abnormal file keywords to the first file layout, first index sequence and first file keywords, that is, imitating the file management method of informal files.

[0117] S5. When the low-utilization file is called by a regular channel, a content adjustment plan for the low-utilization file in the file information is constructed.

[0118] The embodiment of the present invention constructs a content adjustment scheme for low-utilization archives in the archive information to remove redundant data that is not required by users.

[0119] In one embodiment of the present invention, the content adjustment scheme for constructing the low-utilization archive in the archive information includes: performing information segmentation on the archive information to obtain segmentation information; obtaining non-low-utilization archives in the archive information; querying whether the segmentation information exists in the non-low-utilization archive; when the segmentation information exists in the non-low-utilization archive, distinguishing existing information from non-existing information in the segmentation information; setting a first content adjustment scheme for deleting the existing information from the low-utilization archive; and constructing a transition probability matrix of the non-existing information using the following matrix:

[0120]

[0121]

[0122] Among them, P represents the transition probability matrix, p IJ , p(J|I) represents the state transition probability of inferring the non-existence information J from the non-existence information I, p(IJ) represents the probability of the coexistence of the non-existence information I and the non-existence information J, p(I) represents the probability that the non-existence information I accounts for the total non-existence information, d represents the number of categories of non-existence information, represents the probability in the transition probability matrix;

[0123] Based on the transition probability matrix, the inference probability model of the non-existence information is constructed using the following formula:

[0124]

[0125] Among them, P(X t+1 ∣…X t-2 ,X t-1 ,X t ) represents the inference probability model, …X t-2 ,X t-1 ,X t Indicates that the non-existence information contains X t+1 Any number t of the remaining non-existence information, X t+1 Indicates the non-existence information of the currently calculated inference probability value, p(…X t-2 ,X t-1 ,X t ,X t+1 )、p(…X t-2 ,X t-1 ,X t ) represents the probability calculated by the transition probability matrix;

[0126] When the value of the inference probability model is greater than a preset probability threshold, determine whether the amount of inferred information in the inference probability model is lower than the preset quantity threshold; when the amount of inferred information in the inference probability model is lower than the preset quantity threshold, delete the inferred information from the non-existent information as a second content adjustment scheme; use the first content adjustment scheme and the second content adjustment scheme to determine the content adjustment scheme for low-utilization archives in the archive information.

[0127] The segmentation information may be a keyword or a sentence, etc. It should be noted that, by constructing the inference probability model, it is possible to determine whether, when a part of the segmentation information exists, other segmentation information can be inferred based on the known segmentation information. If so, only the known segmentation information can be retained, and the other inferred segmentation information can be deleted. The probability threshold is used to filter the larger value in the value of the inference probability model. This value indicates that the segmentation information can be obtained by...X t-2 ,X t-1 ,X t To infer X t+1 , and the quantity threshold is set to 2, that is, when the amount of inferred information in the inference probability model is lower than the preset quantity threshold, it means that in the presence of...X t-2 ,X t-1 ,X t Under such information conditions, only one P(X t+1 ∣…X t-2 ,X t-1 ,X t ), which can make it possible to have...X t-2 ,X t-1 ,X t Under such information conditions, there is only one inference result, namely X t+1 , and the inferred information is X t+1 .

[0128] S6. Allocate an information serial number to the archive information, analyze the serial number collision value of the archive information based on the information serial number, and generate a position improvement plan for the archive information using the serial number collision value.

[0129] In one embodiment of the present invention, the information serial number is assigned to the archival information, comprising: collecting information nodes of the archival information; sorting the information nodes according to the superior-subordinate relationship and the peer relationship in the information nodes to obtain a node sequence; and assigning node values ​​to the node sequence in order from small to large to obtain an information serial number.

[0130] The information node includes a file collection, such as a file collection of a certain type. In a medical scenario, a file collection belonging to the category of patient identity information is an information node.

[0131] Optionally, the process of assigning information serial numbers to the archive information is a process of assigning 1, 2, 3, 4, 5, ... to the information nodes of the tree structure from top to bottom and from left to right.

[0132] Furthermore, the embodiment of the present invention analyzes the serial number collision value of the archive information based on the information serial number to identify whether the user will exit this window and access other windows with similar names after accessing the archive data in a front-end interface window. If so, it means that the archive in the window visited for the first time is the archive that the user mistakenly clicked in to view. In other words, if the user mistakenly clicks in to view, a conflict value or collision value will be generated.

[0133] In one embodiment of the present invention, analyzing the sequence number collision value of the archive information based on the information sequence number includes: collecting the number of information calls corresponding to the first sequence number in the information sequence number; querying the target information called after calling the information corresponding to the first sequence number within a continuous time period; obtaining the second sequence number of the target information in the information sequence number; determining the sequence number collision value of the archive information when the second sequence number is not greater than the first sequence number; determining the sequence number collision value of the archive information when the second sequence number is greater than the first sequence number.

[0134] Among them, the first serial number refers to the serial number clicked when the archive information is accessed for the first time, and the target information refers to the next information called after calling the information corresponding to the first serial number. When the second serial number is not greater than the first serial number, the serial number collision value of the archive information is determined to be 0. When the second serial number is greater than the first serial number, the serial number collision value of the archive information is determined to be the number of collisions, that is, the number of times the user accidentally clicks in to view it.

[0135] Furthermore, the embodiment of the present invention generates a location improvement scheme for the archive information by utilizing the sequence number collision value, so as to improve the index query efficiency.

[0136] In one embodiment of the present invention, the method of generating a location improvement plan for the archive information using the sequence number collision value includes: obtaining a sequence number collision value when the second sequence number is greater than the first sequence number; determining whether the second sequence number is a leaf sequence number of the first sequence number; generating a location improvement plan for the archive information when the second sequence number is a leaf sequence number of the first sequence number; and exchanging a node structure of the first sequence number with a node structure of the second sequence number as a location improvement plan when the second sequence number is not a leaf sequence number of the first sequence number.

[0137] Among them, the leaf serial number refers to the serial number corresponding to the leaf node of the tree structure. When the second serial number is the leaf serial number of the first serial number, the location improvement plan for generating the archive information is no improvement. The node structure of the first serial number refers to the tree structure with the first serial number as the root node, and the node structure of the second serial number is the same.

[0138] S7. Perform archive management on the archive information in the cloud storage system by using the channel optimization scheme, the content adjustment scheme and the location improvement scheme to obtain an archive management result of the archive information.

[0139] Compared with the problem described in the background technology, the embodiment of the present invention calculates the scenario utilization rate of the archive information based on the application scenario, so as to use the scenario utilization rate to characterize whether a certain archive information is frequently mobilized in the application scenario. In non-application scenarios, the user will not need this archive information and will not call it. Therefore, the archive call rate when the user has a demand for this archive information is counted to characterize whether this archive information is useful, that is, whether the user needs this archive data. Furthermore, the embodiment of the present invention determines whether the low-utilization archive is called by the regular channel by using the actual call channel and the regular call channel, so as to not immediately determine that the archive information is invalid data that the user does not need when the archive information has a low utilization rate, but instead determines whether the user queries the archive information because the channel is blocked by identifying the channel through which the user queries the archive information. Failed to find the archive information. If the user cannot find the archive information because the query channel is blocked, it means that the low utilization rate of the archive information is not because the user does not need it. The embodiment of the present invention constructs a content adjustment scheme for low-utilization archives in the archive information to remove redundant data that the user does not need. Further, the embodiment of the present invention analyzes the sequence number collision value of the archive information based on the information sequence number to identify whether the user will exit this window and access other windows with similar names after accessing the archive data under a certain front-end interface window. If accessed, it means that the archive in the window accessed for the first time is the archive that the user mistakenly clicked in to view. That is to say, the user mistakenly clicked in to view, which will generate a conflict value and collision value. Further, the embodiment of the present invention generates a location improvement scheme for the archive information by using the sequence number collision value to improve the index query efficiency. Therefore, the cloud storage and artificial intelligence authentication archive management method proposed by the present invention can reduce the redundancy of archive data, improve the convenience of content query and the efficiency of index query.

[0140] Embodiment 2:

[0141] like Figure 2 The figure shows a functional module diagram of a cloud storage and artificial intelligence authentication archive management system according to the present invention.

[0142] The cloud storage and artificial intelligence authentication archive management system 200 described in the present invention can be installed in an electronic device. According to the functions implemented, the cloud storage and artificial intelligence authentication archive management system can include a user query module 201, a utilization determination module 202, a regular judgment module 203, a channel optimization module 204, a content construction module 205, a location generation module 206 and an archive management module 207. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0143] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0144] The user query module 201 is used to obtain archive information from the cloud storage system, identify the real call time of the archive information, collect activity scene data at the real call time, analyze the application scenario of the archive information from the activity scene data, and query the user group of the archive information in the reference scenario based on the artificial intelligence authentication method;

[0145] The utilization rate determination module 202 is used to calculate the scenario utilization rate of the archive information based on the application scenario, calculate the user utilization rate of the archive information according to the user group, and determine the total utilization rate of the archive information by using the scenario utilization rate and the user utilization rate;

[0146] The regularity determination module 203 is used to extract low-utilization files in the file information by using the total utilization, obtain the regular calling channel of the low-utilization files, identify the actual calling channel of the low-utilization files, and determine whether the low-utilization files are called by the regular channel by using the actual calling channel and the regular calling channel;

[0147] The channel optimization module 204 is used to generate a channel optimization plan for the low-utilization file in the file information when the low-utilization file is not called by the regular channel;

[0148] The content construction module 205 is used to construct a content adjustment plan for the low-utilization file in the file information when the low-utilization file is called by a regular channel;

[0149] The position generating module 206 is used to assign an information serial number to the archive information, analyze the serial number collision value of the archive information based on the information serial number, and generate a position improvement plan for the archive information using the serial number collision value;

[0150] The archive management module 207 is used to perform archive management on the archive information in the cloud storage system by using the channel optimization scheme, the content adjustment scheme and the location improvement scheme to obtain the archive management result of the archive information.

[0151] In detail, each module in the cloud storage and artificial intelligence authentication archive management system 200 described in the embodiment of the present invention is used in the same manner as described above. Figure 1 The technical means are the same as the cloud storage and artificial intelligence authentication archive management method described in, and can produce the same technical effects, so I will not go into details here.

[0152] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for managing archives based on cloud storage and artificial intelligence authentication, characterized in that: The method comprises: Obtaining archive information from a cloud storage system, identifying the actual call time of the archive information, collecting activity scene data at the actual call time, analyzing the application scenario of the archive information from the activity scene data, and querying the user group of the archive information in the reference scenario based on an artificial intelligence authentication method; Based on the application scenario, the scenario utilization rate of the archive information is calculated, according to the user group, the user utilization rate of the archive information is calculated, and the total utilization rate of the archive information is determined by using the scenario utilization rate and the user utilization rate; Extracting low-utilization files from the file information using the total utilization, obtaining a regular calling channel for the low-utilization files, identifying an actual calling channel for the low-utilization files, and determining whether the low-utilization files are called by a regular channel using the actual calling channel and the regular calling channel; When the low-utilization file is not called by a regular channel, a channel optimization plan for the low-utilization file in the file information is generated; When the low-utilization file is called by a regular channel, construct a content adjustment plan for the low-utilization file in the file information; Assigning an information serial number to the archive information, analyzing a serial number collision value of the archive information based on the information serial number, and generating a position improvement plan for the archive information using the serial number collision value; The channel optimization scheme, the content adjustment scheme and the location improvement scheme are used to perform archive management on the archive information in the cloud storage system to obtain an archive management result of the archive information.

2. The cloud storage and artificial intelligence authentication file management method according to claim 1, characterized in that: The calculating the scene utilization rate of the archive information based on the application scene includes: Setting a unit call time period for the archive information; Extract the number of unit calls of the archive information within the unit call period; Query the application scenario time period of the application scenario; Obtaining a scene calling period belonging to the application scene period in the unit calling period; Extract the scene call times belonging to the scene call time period from the unit call times: Calculating the scene density of the archive information in the application scene period based on the unit call number and the scene call number; The scene density is used as the scene utilization rate of the archive information.

3. The cloud storage and artificial intelligence authentication file management method according to claim 1, characterized in that: The calculating the user utilization rate of the archive information according to the user group includes: Setting the characteristic category of the archival information; Extract the number of category calls of the archive information within the feature category; Recording the identity characteristics of the user group; Acquire user features belonging to the identity features in the feature category; Extracting the feature call times belonging to the user feature from the category call times; Calculating the feature density of the profile information with respect to the identity feature based on the category call count and the feature call count; The feature density is used as the user utilization rate of the profile information.

4. The cloud storage and artificial intelligence authentication file management method according to claim 1, characterized in that: The using the actual calling channel and the regular calling channel to determine whether the low utilization rate file is called by the regular channel includes: Selecting a first calling channel among the actual calling channels that is consistent with the regular calling channel; Selecting a second calling channel in the actual calling channel that is inconsistent with the regular calling channel; Query the first calling number and the second calling number of the first calling channel and the second calling channel respectively; Calculating a regular call rate of the low-utilization file according to the first call number and the second call number; When the regular call rate is greater than a preset call rate threshold, it is determined that the low-utilization file is called by a regular channel; when the regular call rate is not greater than the preset call rate threshold, it is determined that the low-utilization file is not called by a regular channel.

5. The cloud storage and artificial intelligence authentication file management method according to claim 1, characterized in that: The channel optimization scheme for generating low-utilization archives in the archive information includes: Query the informal files when the low-utilization files are not called by the formal channels; Identify a first file format, a first index sequence, and a first file keyword of the informal file; Obtaining a second file layout, a second index sequence, and a second file keyword of the low-utilization file; Calculating the layout similarity between the first file layout and the second file layout; Calculating a sequence similarity between the first index sequence and the second index sequence; Calculating keyword similarity between the first archive keyword and the second archive keyword; Using the layout similarity, the sequence similarity and the keyword similarity, respectively screening out abnormal file layouts, abnormal index sequences and abnormal file keywords in the second file layout, second index sequence and second file keywords; The channel optimization scheme corresponding to the abnormal file layout, the abnormal index sequence and the abnormal file keyword is set by using the first file layout, the first index sequence and the first file keyword.

6. The cloud storage and artificial intelligence authentication file management method according to claim 1, characterized in that: The content adjustment scheme for constructing the low-utilization archive in the archive information includes: Performing information segmentation on the archive information to obtain segmentation information; Acquire non-low-utilization archives in the archive information; querying whether the segmentation information exists in the non-low utilization file; When the segmentation information exists in the non-low-utilization file, distinguishing existing information from non-existing information in the segmentation information; Setting a first content adjustment scheme to delete the existing information from the low-utilization archive; Constructing a transition probability matrix of the non-existence information; Based on the transition probability matrix, construct an inference probability model of the non-existence information; When the value of the inference probability model is greater than a preset probability threshold, determining whether the amount of inferred information in the inference probability model is lower than a preset amount threshold; When the amount of inferred information in the inference probability model is lower than a preset amount threshold, the inferred information is deleted from the non-existent information as a second content adjustment scheme; The first content adjustment scheme and the second content adjustment scheme are used to determine a content adjustment scheme for low-utilization files in the file information.

7. The cloud storage and artificial intelligence authentication file management method according to claim 1, characterized in that: The step of allocating information serial numbers to the archive information includes: An information node for collecting the archival information; According to the superior-subordinate relationship and the peer relationship in the information nodes, the information nodes are sorted to obtain a node sequence; Node values ​​are assigned to the node sequence in ascending order to obtain information sequence numbers.

8. The cloud storage and artificial intelligence authentication file management method according to claim 1, characterized in that: The analyzing the sequence number collision value of the archive information based on the information sequence number includes: Collect the number of information calls corresponding to the first sequence number in the information sequence number; querying the target information called after calling the information corresponding to the first sequence number within a continuous period; Obtaining a second serial number of the target information from the information serial number; When the second serial number is not greater than the first serial number, determining a serial number collision value of the archive information; When the second serial number is greater than the first serial number, a serial number collision value of the archive information is determined.

9. The cloud storage and artificial intelligence authentication file management method according to claim 1, characterized in that: The location improvement scheme for generating the archive information by using the sequence number collision value includes: Get the sequence number collision value when the second sequence number is greater than the first sequence number; Determine whether the second sequence number is a leaf sequence number of the first sequence number; When the second serial number is a leaf serial number of the first serial number, generating a location improvement plan for the archive information; When the second sequence number is not the leaf sequence number of the first sequence number, exchanging the node structure of the first sequence number with the node structure of the second sequence number is used as a position improvement solution.

10. A cloud storage and artificial intelligence authentication archive management system, characterized in that: The system comprises: A user query module is used to obtain archive information from the cloud storage system, identify the real call time of the archive information, collect activity scene data at the real call time, analyze the application scenario of the archive information from the activity scene data, and query the user group of the archive information in the reference scenario based on the artificial intelligence authentication method; A utilization rate determination module, configured to calculate the scenario utilization rate of the archive information based on the application scenario, calculate the user utilization rate of the archive information according to the user group, and determine the total utilization rate of the archive information by using the scenario utilization rate and the user utilization rate; a regularity determination module, configured to extract low-utilization files from the file information by using the total utilization, obtain regular calling channels of the low-utilization files, identify actual calling channels of the low-utilization files, and determine whether the low-utilization files are called by regular channels by using the actual calling channels and the regular calling channels; A channel optimization module, used for generating a channel optimization plan for the low-utilization file in the file information when the low-utilization file is not called by the regular channel; A content construction module, used for constructing a content adjustment plan for the low-utilization archive in the archive information when the low-utilization archive is called by a regular channel; A position generation module, used to assign an information serial number to the archive information, analyze the serial number collision value of the archive information based on the information serial number, and generate a position improvement plan for the archive information using the serial number collision value; The archive management module is used to perform archive management on the archive information in the cloud storage system by using the channel optimization scheme, the content adjustment scheme and the location improvement scheme to obtain the archive management result of the archive information.

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