A password storage management and control system based on intelligent optimization

Abstract

The application discloses a password storage management and control system based on intelligent optimization, which comprises the following steps: collecting terminal interaction data, user behavior characteristics and verification interaction characteristics in a user interaction period, and completing multi-dimensional verification characteristic processing; realizing user stratification and grouping by relying on a clustering algorithm, constructing a multi-level user table, and matching a multi-level Redis cache table; simultaneously, monitoring the user login verification state and the cache reference frequency in real time, and dynamically updating the user classification and the cache data. The application solves the technical defects of the traditional one-to-one password verification mode, such as low efficiency, weak concurrent bearing capacity, poor data storage security and difficulty in identifying abnormal verification behaviors, realizes efficient access, rapid verification and safe risk control of password data in a large-scale user scenario, and is suitable for user password management and control scenarios of various APPs, small programs and website platforms.

Description

Technical Field

[0001] This invention relates to the field of data encryption and storage, and more specifically, to a cryptographic storage management system based on intelligent optimization. Background Technology

[0002] With the rapid iteration of internet technology, the number of mobile apps, WeChat mini-programs, websites, and online service platforms has exploded. Various online scenarios, including government services, e-commerce transactions, social entertainment, and enterprise office work, all rely on user account passwords for identity verification. In scenarios such as new platform construction, old platform upgrades, cross-platform user migration, and platform promotions, a massive number of new user registrations, old user login verifications, and password resets and verifications are generated in a short period, forming a high-density concurrent user identity verification request. This places extremely high performance and security requirements on the platform's password data storage, retrieval, and verification systems.

[0003] Currently, mainstream password storage and verification models in the industry all adopt the traditional one-to-one data extraction and verification architecture. While this works well in typical scenarios with few users, it suffers from numerous unavoidable technical drawbacks when facing large-scale, centralized user verification scenarios, such as low verification efficiency, high database read / write pressure, and response timeouts. Furthermore, existing technologies lack techniques for hierarchical management and efficient caching based on user verification interaction characteristics, making it difficult to simultaneously improve password data management efficiency and security. Therefore, there is an urgent need for an efficient password storage management and verification method. Summary of the Invention

[0004] This invention overcomes the shortcomings of existing technologies and proposes a password storage and management system based on intelligent optimization.

[0005] The first aspect of this invention provides a password storage management method based on intelligent optimization, comprising: S1: During a user interaction cycle, acquire information interaction data from the user terminal and analyze user behavior characteristics and user verification interaction characteristics through the feature acquisition unit; S2: Based on user behavior features and user verification interaction features, feature vectorization is performed to generate user verification interaction features. The K-means clustering algorithm is introduced to cluster the user verification interaction features and group users, and a multi-level user table is set up. S3: Based on the multi-level user table, extract the password verification data corresponding to users of different priorities from the system database, generate a multi-level Redis cache table from the password verification data, import the password verification data into the cache table, and record the update logs of the system database and the cache table. S4: In the next user interaction cycle, the real-time user is logged in through the cache table. Based on the login verification data and the reference frequency of the cache table, the real-time user classification status is analyzed. The real-time user classification status is then verified and matched with the multi-level user table, and the multi-level user table is updated.

[0006] In this solution, the user interaction period is a preset and configurable time period, and the information interaction data collection dimensions of the feature collection unit include user terminal device information, operation network IP, front-end operation log, back-end verification request log, and account operation records. The user verification interaction features specifically include the user's operating platform type, the number of periodic verifications within a single user interaction cycle, the average password verification time per session, the historical password modification cycle, the password reset frequency, the verification failure frequency, and cross-terminal verification records. The user behavior characteristics specifically include user account registration duration, number of devices logged in within a single interaction cycle, device login frequency, daily operation time period, page operation habits, historical login geographic information, and abnormal login records.

[0007] In this scheme, S1 further includes: During the data acquisition process, the feature acquisition unit simultaneously performs data preprocessing operations, including filtering out null data, duplicate data, garbled data, and invalid data that has timed out, while retaining valid feature data.

[0008] In this solution, S2 specifically refers to: The min-max normalization algorithm is used to perform dimensionless processing on multi-dimensional heterogeneous feature data, and all feature data are uniformly mapped to a preset numerical range to eliminate the dimensional differences between different feature dimensions. Heterogeneous feature data includes user behavior features and user verification interaction features; The standardized user behavior features and user verification interaction features are vectorized to generate a first feature vector and a second feature vector for each user. The first feature vector and the second feature vector are concatenated to form multi-dimensional user verification interaction features.

[0009] In this solution, S2-S3 specifically refers to: The K-means clustering algorithm is used, and multiple cluster centers are randomly set, with a number of 3-5, corresponding to a 3-5 level user classification system. The Euclidean distance between each user feature vector and the cluster center is calculated iteratively, and the clustering criterion is to minimize the distance. The center points are updated cyclically until the preset number of iterations is reached, and then the center point update is stopped. Based on the cluster status of the central point, users are classified and multiple groups of users are set up. Based on the information interaction data of each user group, assess the user verification demand and, in combination with the number of users in each group, set the cache priority information for each user group. Based on the cache priority information, set up a Redis cache table for each group of users. Different cache tables correspond to different priorities, resulting in a multi-level Redis cache table.

[0010] In this solution, S4 specifically refers to: In the next user interaction cycle, the system obtains the login password verification request initiated by the user in real time. The system first matches the user level data corresponding to the multi-level Redis cache table, retrieves the cache data table according to the cache level, and retrieves the pre-stored password verification data in the cache to complete the fast comparison and verification. If the user verification request cannot be matched, the system retrieves the next level cache data table.

[0011] In this solution, S4 further includes: Based on the user's current periodic verification frequency, cached data reference count, verification success rate, number of abnormal verifications, device login stability, and operation behavior matching degree, a multi-dimensional user verification status evaluation is performed. The contribution of the Redis cache table to multiple real-time users is calculated, and multiple real-time users are further classified according to the contribution level to obtain the user's real-time classification status. Extract the current classification status of multiple real-time users from the multi-level user table, match the current classification status with the real-time classification status of the users, and if there is a mismatch in classification status, update the user classification status of the multi-level user table based on the real-time classification status of the users.

[0012] In this solution, user terminal equipment includes computer terminal platforms, mobile terminal platforms, and web terminal platforms.

[0013] A second aspect of the present invention also provides a password storage management system based on intelligent optimization. The system includes: a memory, a processor, and a feature acquisition unit. The memory includes a password storage management program based on intelligent optimization. When the password storage management program based on intelligent optimization is executed by the processor, it implements the steps S1-S4 described above.

[0014] A third aspect of the present invention also provides a computer-readable storage medium comprising a smart-optimized password storage management program, wherein when the smart-optimized password storage management program is executed by a processor, it implements the steps of the smart-optimized password storage management method as described in any of the preceding claims.

[0015] This invention discloses a password storage and management system based on intelligent optimization, comprising: collecting terminal interaction data, user behavior characteristics, and verification interaction characteristics during the user interaction cycle to complete multi-dimensional verification feature processing; relying on clustering algorithms to achieve user hierarchical grouping, constructing a multi-level user table, and matching it with a multi-level Redis cache table; simultaneously, monitoring user login verification status and cache reference frequency in real time, and dynamically updating user hierarchy and cache data. This invention solves the technical defects of traditional one-to-one password verification mode, such as low efficiency, weak concurrency handling capacity, poor data storage security, and difficulty in identifying abnormal verification behavior. It achieves efficient access, rapid verification, and security risk control of password data in large-scale user scenarios, and is suitable for user password management scenarios of various APPs, mini-programs, and website platforms. Attached Figure Description

[0016] Figure 1 A flowchart of a password storage management method based on intelligent optimization according to the present invention is shown; Figure 2 A block diagram of a password storage management system based on intelligent optimization according to the present invention is shown. Detailed Implementation

[0017] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0019] Figure 1 A flowchart of a password storage management method based on intelligent optimization according to the present invention is shown.

[0020] like Figure 1 As shown, the first aspect of the present invention provides a password storage management method based on intelligent optimization, comprising: S1: During a user interaction cycle, acquire information interaction data from the user terminal and analyze user behavior characteristics and user verification interaction characteristics through the feature acquisition unit; S2: Based on user behavior features and user verification interaction features, feature vectorization is performed to generate user verification interaction features. The K-means clustering algorithm is introduced to cluster the user verification interaction features and group users, and a multi-level user table is set up. S3: Based on the multi-level user table, extract the password verification data corresponding to users of different priorities from the system database, generate a multi-level Redis cache table from the password verification data, import the password verification data into the cache table, and record the update logs of the system database and the cache table. S4: In the next user interaction cycle, the real-time user is logged in through the cache table. Based on the login verification data and the reference frequency of the cache table, the real-time user classification status is analyzed. The real-time user classification status is then verified and matched with the multi-level user table, and the multi-level user table is updated.

[0021] Through the embodiments of the present invention, the technical problems of existing password storage and verification technologies, such as low concurrency efficiency of one-to-one verification mode, high database pressure, lack of user hierarchical management, inability to dynamically adapt to changes in user behavior, lack of abnormal verification risk control capabilities, and lack of traceability records for data operations, can be solved, thereby achieving efficient storage and retrieval, rapid verification, hierarchical management, dynamic optimization and security protection of password data in large-scale user scenarios.

[0022] According to an embodiment of the present invention, S1 specifically includes: The user interaction period is a preset and configurable time period, and the information interaction data collection dimensions of the feature collection unit include user terminal device information, operation network IP, front-end operation log, back-end verification request log, and account operation records. The user verification interaction features specifically include the user's operating platform type, the number of periodic verifications within a single user interaction cycle, the average password verification time per session, the historical password modification cycle, the password reset frequency, the verification failure frequency, and cross-terminal verification records. The user behavior characteristics specifically include user account registration duration, number of devices logged in within a single interaction cycle, device login frequency, daily operation time period, page operation habits, historical login geographic information, and abnormal login records.

[0023] It should be noted that the preset configuration time period can be set from 0.5h to 48h. The collection and analysis of information interaction data specifically includes data analysis and collection from two dimensions: user behavior characteristics and user verification interaction characteristics.

[0024] According to an embodiment of the present invention, S1 further includes: During the data acquisition process, the feature acquisition unit simultaneously performs data preprocessing operations, including filtering out null data, duplicate data, garbled data, and invalid data that has timed out, while retaining valid feature data.

[0025] It should be noted that preprocessing is used to improve the effectiveness of data collection.

[0026] According to an embodiment of the present invention, step S2 specifically includes: The min-max normalization algorithm is used to perform dimensionless processing on multi-dimensional heterogeneous feature data, and all feature data are uniformly mapped to a preset numerical range to eliminate the dimensional differences between different feature dimensions. Heterogeneous feature data includes user behavior features and user verification interaction features; The standardized user behavior features and user verification interaction features are vectorized to generate a first feature vector and a second feature vector for each user. The first feature vector and the second feature vector are concatenated to form multi-dimensional user verification interaction features.

[0027] It should be noted that the first feature vector and the second feature vector correspond to user behavior features and verification interaction features, respectively. For example, the first feature vector includes multiple dimensions of data, such as platform type value, number of periodic verifications, average password verification time (seconds), etc., all of which are numerical representations.

[0028] Platform type values ​​can be represented by numerical codes, such as 0, 1, and 2 representing computer terminal platform, mobile terminal platform, and web terminal platform, respectively.

[0029] For data in non-numerical dimensions, they can be represented by related numerical mappings. For example, multiple values ​​from 0 to 24 can be set for daily operation time periods to represent the corresponding intervals of operation time within a day (the interval spans one hour). For page operation habit dimensions, different numerical numbers can be set for different pages. The greater the difference in values, the greater the relevance of the pages and the greater the content span.

[0030] According to an embodiment of the present invention, S2-S3 specifically includes: The K-means clustering algorithm is used, and multiple cluster centers are randomly set, with a number of 3-5, corresponding to a 3-5 level user classification system. The Euclidean distance between each user feature vector and the cluster center is calculated iteratively, and the clustering criterion is to minimize the distance. The center points are updated cyclically until the preset number of iterations is reached, and then the center point update is stopped. Based on the cluster status of the central point, users are classified and multiple groups of users are set up. Based on the information interaction data of each user group, assess the user verification demand and, in combination with the number of users in each group, set the cache priority information for each user group. Based on the cache priority information, set up a Redis cache table for each group of users. Different cache tables correspond to different priorities, resulting in a multi-level Redis cache table.

[0031] It should be noted that password verification data includes user account information, verification credentials, password hash values, salt values, and other data used for user verification. Cache priority information includes the corresponding multi-level user tables.

[0032] The cache priority information is a multi-level user table, which is a structured data table. The stored fields include the user's unique identifier, user level, corresponding cluster center number, verification frequency range, cache storage priority, data update cycle, risk control level, etc. The higher the user level, the higher the user verification frequency and the higher the password verification response priority within the corresponding unit period.

[0033] According to an embodiment of the present invention, step S4 specifically includes: In the next user interaction cycle, the system obtains the login password verification request initiated by the user in real time. The system first matches the user level data corresponding to the multi-level Redis cache table, retrieves the cache data table according to the cache level, and retrieves the pre-stored password verification data in the cache to complete the fast comparison and verification. If the user verification request cannot be matched, the system retrieves the next level cache data table.

[0034] According to an embodiment of the present invention, step S4 further includes: Based on the user's current periodic verification frequency, cached data reference count, verification success rate, number of abnormal verifications, device login stability, and operation behavior matching degree, a multi-dimensional user verification status evaluation is performed. The contribution of the Redis cache table to multiple real-time users is calculated, and multiple real-time users are further classified according to the contribution level to obtain the user's real-time classification status. Extract the current classification status of multiple real-time users from the multi-level user table, match the current classification status with the real-time classification status of the users, and if there is a mismatch in classification status, update the user classification status of the multi-level user table based on the real-time classification status of the users.

[0035] Here, the system performs a hierarchical matching and verification for each interaction cycle. Users whose real-time level is higher than their original registered level are upgraded, and their cache storage priority is increased accordingly. Users whose real-time level is lower than their original registered level are downgraded, and their cache priority is reduced and redundant cache resources are released. Dormant users who have not performed any operations for a long time are marked separately to optimize system resource allocation. The secondary update of the cache table is executed synchronously with the update of the multi-level user table. The update content includes adjusting the user data cache level, refreshing cache data, allocating cache resources, and cleaning up invalid data, so as to ensure that the cache table data accurately matches the real-time status of users.

[0036] It should be noted that the greater the contribution, the higher the efficiency of Redis cache table in verifying and matching multiple levels of users.

[0037] According to embodiments of the present invention, the user terminal device includes a computer terminal platform, a mobile terminal platform, and a web terminal platform.

[0038] According to an embodiment of the present invention, it further includes: Based on user historical login characteristics and user behavior characteristics, set reasonable thresholds for user login credential generation frequency, password verification frequency range, and verification times; perform real-time anomaly matching on database access behavior; identify abnormal verification processes; and intercept database unauthorized password matching behavior and abnormal password extraction table partitioning behavior. The frequency of login credentials generation, the range of password verification frequency, and the reasonable range of verification times are all user-defined values. The specific process of the abnormal matching is as follows: real-time capture of database access behavior data and user verification operation data, and real-time comparison with preset thresholds. When the frequency of user login credential generation exceeds the limit, the frequency of password verification exceeds the limit, or the number of verifications exceeds the reasonable range within a unit period, any one of the three situations will be immediately judged as abnormal verification behavior. The unauthorized password matching behavior includes unauthorized accounts accessing high-priority user password data and high-priority Redis cache tables, unauthorized IPs accessing core password forms, and unauthorized calls to database interfaces to match passwords in batches. The abnormal password extraction and table partitioning behaviors include batch extraction of multi-user password data within a preset short period, batch table partitioning and exporting of password data during non-maintenance periods, and batch crawling of password data by abnormal scripts; After identifying the abnormal verification process, the system automatically performs risk control interception operations, including immediately blocking unauthorized access requests, freezing abnormal access accounts and IPs, locking abnormal database operation permissions, retaining abnormal behavior logs, and pushing abnormal information to the operation and maintenance management terminal in real time. At the same time, it triggers a multi-level early warning mechanism, sets the abnormal risk level based on the Redis cache table level involved in the corresponding abnormal verification process and the user level of the corresponding abnormal extraction, and generates early warning information.

[0039] It should be noted that the frequency of login credential generation, password verification frequency range, and verification count are adaptively adjusted according to different business scenarios and user volumes.

[0040] Figure 2 A block diagram of a password storage management system based on intelligent optimization according to the present invention is shown.

[0041] A second aspect of the present invention also provides a password storage management system based on intelligent optimization. The system includes: a memory, a processor, and a feature acquisition unit. The memory includes a password storage management program based on intelligent optimization. When executed by the processor, the password storage management program based on intelligent optimization performs the following steps: S1: During a user interaction cycle, acquire information interaction data from the user terminal and analyze user behavior characteristics and user verification interaction characteristics through the feature acquisition unit; S2: Based on user behavior features and user verification interaction features, feature vectorization is performed to generate user verification interaction features. The K-means clustering algorithm is introduced to cluster the user verification interaction features and group users, and a multi-level user table is set up. S3: Based on the multi-level user table, extract the password verification data corresponding to users of different priorities from the system database, generate a multi-level Redis cache table from the password verification data, import the password verification data into the cache table, and record the update logs of the system database and the cache table. S4: In the next user interaction cycle, the real-time user is logged in through the cache table. Based on the login verification data and the reference frequency of the cache table, the real-time user classification status is analyzed. The real-time user classification status is then verified and matched with the multi-level user table, and the multi-level user table is updated.

[0042] When the system is running, it can perform one or more steps of the above-described intelligent optimization-based password storage management method.

[0043] A third aspect of the present invention also provides a computer-readable storage medium comprising a smart-optimized password storage management program, wherein when the smart-optimized password storage management program is executed by a processor, it implements the steps of the smart-optimized password storage management method as described in any of the preceding claims.

[0044] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0045] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0046] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0047] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0048] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0049] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A password storage management method based on intelligent optimization, characterized in that, include: S1: During a user interaction cycle, acquire information interaction data from the user terminal and analyze user behavior characteristics and user verification interaction characteristics through the feature acquisition unit; S2: Based on user behavior features and user verification interaction features, feature vectorization is performed to generate user verification interaction features. The K-means clustering algorithm is introduced to cluster the user verification interaction features and group users, and a multi-level user table is set up. S3: Based on the multi-level user table, extract the password verification data corresponding to users of different priorities from the system database, generate a multi-level Redis cache table from the password verification data, import the password verification data into the cache table, and record the update logs of the system database and the cache table. S4: In the next user interaction cycle, the real-time user is logged in through the cache table. Based on the login verification data and the reference frequency of the cache table, the real-time user classification status is analyzed. The real-time user classification status is then verified and matched with the multi-level user table, and the multi-level user table is updated.

2. The password storage management method based on intelligent optimization according to claim 1, characterized in that, The user interaction period is a preset and configurable time period, and the information interaction data collection dimensions of the feature collection unit include user terminal device information, operation network IP, front-end operation log, back-end verification request log, and account operation records. The user verification interaction features specifically include the user's operating platform type, the number of periodic verifications within a single user interaction cycle, the average password verification time per session, the historical password modification cycle, the password reset frequency, the verification failure frequency, and cross-terminal verification records. The user behavior characteristics specifically include user account registration duration, number of devices logged in within a single interaction cycle, device login frequency, daily operation time period, page operation habits, historical login geographic information, and abnormal login records.

3. The password storage management method based on intelligent optimization according to claim 2, characterized in that, S1 further includes: During the data acquisition process, the feature acquisition unit simultaneously performs data preprocessing operations, including filtering out null data, duplicate data, garbled data, and invalid data that has timed out, while retaining valid feature data.

4. The password storage management method based on intelligent optimization according to claim 3, characterized in that, Specifically, S2 is: The min-max normalization algorithm is used to perform dimensionless processing on multi-dimensional heterogeneous feature data, and all feature data are uniformly mapped to a preset numerical range to eliminate the dimensional differences between different feature dimensions. Heterogeneous feature data includes user behavior features and user verification interaction features; The standardized user behavior features and user verification interaction features are vectorized to generate a first feature vector and a second feature vector for each user. The first feature vector and the second feature vector are concatenated to form multi-dimensional user verification interaction features.

5. The password storage management method based on intelligent optimization according to claim 4, characterized in that, Specifically, in S2-S3: The K-means clustering algorithm is used, and multiple cluster centers are randomly set, with a number of 3-5, corresponding to a 3-5 level user classification system. The Euclidean distance between each user feature vector and the cluster center is calculated iteratively, and the clustering criterion is to minimize the distance. The center points are updated cyclically until the preset number of iterations is reached, and then the center point update is stopped. Based on the cluster status of the central point, users are classified and multiple groups of users are set up. Based on the information interaction data of each user group, assess the user verification demand and, in combination with the number of users in each group, set the cache priority information for each user group. Based on the cache priority information, set up a Redis cache table for each group of users. Different cache tables correspond to different priorities, resulting in a multi-level Redis cache table.

6. The password storage management method based on intelligent optimization according to claim 5, characterized in that, Specifically, S4 is: In the next user interaction cycle, the system obtains the login password verification request initiated by the user in real time. The system first matches the user level data corresponding to the multi-level Redis cache table, retrieves the cache data table according to the cache level, and retrieves the pre-stored password verification data in the cache to complete the fast comparison and verification. If the user verification request cannot be matched, the system retrieves the next level cache data table.

7. The password storage management method based on intelligent optimization according to claim 6, characterized in that, The S4 further includes: Based on the user's current periodic verification frequency, cached data reference count, verification success rate, number of abnormal verifications, device login stability, and operation behavior matching degree, a multi-dimensional user verification status evaluation is performed. The contribution of the Redis cache table to multiple real-time users is calculated, and multiple real-time users are further classified according to the contribution level to obtain the user's real-time classification status. Extract the current classification status of multiple real-time users from the multi-level user table, match the current classification status with the real-time classification status of the users, and if there is a mismatch in classification status, update the user classification status of the multi-level user table based on the real-time classification status of the users.

8. The password storage management method based on intelligent optimization according to claim 7, characterized in that, User terminal equipment includes computer terminal platforms, mobile terminal platforms, and web terminal platforms.

9. A password storage and management system based on intelligent optimization, characterized in that, The system includes: a memory, a processor, and a feature acquisition unit. The memory includes a password storage management program based on intelligent optimization. When the password storage management program based on intelligent optimization is executed by the processor, it implements the steps of the password storage management method based on intelligent optimization as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a smart-optimized password storage management program, which, when executed by a processor, implements the steps of the smart-optimized password storage management method as described in any one of claims 1 to 8.

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