A NAS-based method and system for data verification, repair, and aggregation.

Abstract

This invention provides a data verification, repair, and aggregation method and system based on NAS, comprising: generating a real-time data stream in the NAS based on each user's real-time access data and real-time shared data in the network, performing preliminary verification to obtain several vulnerability strings in the real-time data stream, then deriving the access / sharing behavior of the vulnerability strings, generating relevant behavioral characteristics corresponding to each access / sharing behavior based on the real-time data stream, performing character repair on the corresponding vulnerability strings and dividing and classifying the data segments for storage, constructing migration guidelines for the corresponding data source based on the migration trajectory corresponding to each data segment, and adjusting the storage nodes of the corresponding suspicious data segments based on the migration differences between the migration guidelines corresponding to each data source at different times, thereby improving the data security, repair reliability, and access efficiency of the NAS system.

Description

Technical Field

[0001] This invention relates to the field of network attached storage technology, and in particular to a data verification, repair and aggregation method and system based on NAS. Background Technology

[0002] With the rapid development of information technology, the demand for data storage from enterprises and individuals is increasing. Network Attached Storage (NAS) systems are widely used in various data storage scenarios due to their advantages such as easy expansion, convenient access, and relatively low cost. However, in the actual operation of NAS systems, data storage and management still face many problems, specifically: First, the risk of data corruption is high. Data in NAS systems is stored on storage nodes for a long time, and is easily corrupted due to factors such as hardware aging, electromagnetic interference, and network transmission errors. Currently, most NAS systems only use a single verification method to verify data. The fault tolerance of a single verification method is limited, and it is difficult to accurately identify all types of data corruption, which may lead to corrupted data being read and used normally, causing data loss or misuse for users. Second, data recovery efficiency is low and reliability is insufficient. When data corruption is detected, existing NAS systems typically rely solely on a single backup for repair. If the backup data is also corrupted due to storage node failures, the data repair operation cannot be completed. While some systems introduce redundant data for repair, the generation and storage methods of this redundant data are often unreasonable, resulting in significant computational resources and time consumption during the reconstruction of corrupted data, leading to low repair efficiency and impacting the overall performance of the NAS system. Third, data storage is fragmented, resulting in low access efficiency. As the amount of data stored in NAS systems continues to increase, different types of data with varying access frequencies are distributed across various storage nodes. When a user needs to access specific data, the NAS system must search across multiple storage nodes, increasing the time cost of data retrieval. Simultaneously, fragmented storage leads to uneven utilization of storage resources. Some storage nodes are idle due to storing large amounts of low-frequency access data, while others are overloaded due to storing high-frequency access data, further reducing data access efficiency.

[0003] Therefore, this invention provides a NAS-based method and system for data verification, repair, and aggregation. Summary of the Invention

[0004] This invention provides a data verification, repair, and aggregation method and system based on NAS, aiming to overcome the shortcomings of existing NAS systems, such as weak data verification fault tolerance, insufficient data repair reliability, and low data access efficiency, thereby improving the data security, repair reliability, and access efficiency of NAS systems.

[0005] This invention provides a NAS-based data verification, repair, and aggregation method, including:

[0006] Step 1: In the NAS, a real-time data stream is generated based on each user's real-time access data and real-time shared data in the network, and the real-time data stream is initially verified using initial verification rules to obtain several data vulnerability information.

[0007] Step 2: Identify several vulnerability strings in the real-time data stream based on the data vulnerability information, and deduce the access / sharing behavior of the vulnerability string based on the data characteristics corresponding to each vulnerability string;

[0008] Step 3: Generate relevant behavioral features corresponding to each access behavior / sharing behavior based on the real-time data stream, and use the relevant behavioral features to repair the corresponding vulnerability string to obtain a repair data stream;

[0009] Step 4: Divide the repair data stream into data segments and classify them according to the storage type corresponding to each storage node in the NAS. Construct a migration guide for the corresponding data source based on the migration trajectory corresponding to each data segment.

[0010] Step 5: Based on the migration differences between the migration guidelines corresponding to each data source at different times, find the corresponding suspicious data segments and perform aggregation verification, and adjust the storage nodes corresponding to the suspicious data segments according to the aggregation verification results.

[0011] In one feasible embodiment, step 1 includes:

[0012] Step 11: Transmit the user's real-time access data and real-time shared data in the network to the NAS for timestamp alignment to generate a real-time data stream. Based on the first data source corresponding to each real-time access data and the second data source corresponding to each real-time shared data, mark the first data position of each real-time access data and the second data position of each real-time shared data in the real-time data stream.

[0013] Step 12: Generate the data format features of the real-time data stream based on the data tags, obtain the data verification method corresponding to each data source, and perform fusion training on the data verification methods based on the data format features to obtain the initial verification rules of the real-time data stream;

[0014] Step 13: Decompose the real-time data stream into continuous strings using the initial verification rules, perform real-time data verification on the continuous strings, screen out several abnormal strings, identify the abnormal type corresponding to each abnormal string, and generate data vulnerability information for the real-time data stream.

[0015] One feasible approach also includes:

[0016] The frequency of each anomaly type in the real-time data stream is counted to determine the high-frequency anomaly characteristics of the real-time data stream.

[0017] When the abnormal value corresponding to the high-frequency abnormal feature is higher than the specified abnormal value, the NAS is repaired according to the abnormal factors corresponding to the high-frequency abnormal feature.

[0018] In one feasible embodiment, step 2 includes:

[0019] Step 21: Identify the vulnerability-related data segments in the real-time data stream for each data vulnerability information, and convert each data element contained in the vulnerability-related data segment into an independent string. Merge the independent strings to generate several vulnerability strings in the real-time data stream.

[0020] Step 22: Identify the meaning of each vulnerability-related data segment in the real-time data stream, and the data relationship between each vulnerability-related data segment and the real-time data stream; construct the fault tolerance range corresponding to the vulnerability string based on the data meaning and the data relationship.

[0021] Step 23: Construct data features for each vulnerability string in the real-time data stream, perform behavior transformation on the data features according to the corresponding fault tolerance range, obtain the restoration behavior corresponding to each vulnerability string, determine the behavior attribute corresponding to each restoration behavior according to the data meaning, and obtain the access behavior / sharing behavior of each vulnerability string.

[0022] One feasible approach also includes:

[0023] Based on the behavioral attributes, the vulnerability string is divided into access strings and sharing strings;

[0024] Simulate the user's access process on the network based on the data meaning corresponding to the access string, and generate the access behavior corresponding to the vulnerability string;

[0025] Based on the data meaning corresponding to the shared string, simulate the user's sharing content and sharing process in the network, and generate the sharing behavior corresponding to the shared string.

[0026] In one feasible embodiment, step 3 includes:

[0027] Step 31: Construct several network operation behaviors of the user in the network based on the real-time data stream, determine several related behaviors corresponding to each access behavior / sharing behavior based on the behavior logic corresponding to each network operation behavior, and obtain the behavior purpose and behavior basis corresponding to each related behavior respectively.

[0028] Step 32: Construct relevant behavioral features for each vulnerability string based on the behavioral purpose and behavioral basis of several related behaviors corresponding to each access behavior / sharing behavior, and identify logical contradiction features between the vulnerability string and the corresponding related behavioral features;

[0029] Step 33: Based on the network operation behavior, deduce several estimated behaviors corresponding to each vulnerability string, and use the behavioral purpose and behavioral basis corresponding to each related behavior to perform contradiction repair on the corresponding vulnerability string until the logical contradiction features are eliminated to generate a repair string;

[0030] Step 34: Convert the repair string into a repair behavior, take the estimated behavior with the highest similarity to the repair behavior as the repair result, and use the repair result to repair the real-time data stream accordingly to obtain a repaired data stream.

[0031] In one feasible embodiment, step 4 includes:

[0032] Step 41: Perform a functional inventory of each storage node in the NAS to determine the storage type and remaining storage space of each storage node. At the same time, divide the repair data stream into data segments to obtain several data segments.

[0033] Step 42: Match each data segment with different storage types to obtain the matching result for each data segment, and match the corresponding migration storage node for each data segment based on the remaining storage space corresponding to each storage node.

[0034] Step 43: Store each data segment into the corresponding migration storage node, and at the same time obtain the data source corresponding to each data segment, and record the migration process of each data segment from the data source to the migration storage node to generate the migration trajectory corresponding to each data segment;

[0035] Step 44: Statistically analyze the migration trajectory corresponding to each of the data sources, determine the migration characteristics between each data source and different storage nodes, and generate corresponding migration guidelines.

[0036] In one feasible embodiment, step 5 includes:

[0037] Step 51: Statistically count several historical migration guidelines corresponding to each data source, and arrange the historical migration guidelines in chronological order to obtain a guide sequence corresponding to each data source. Construct a migration rule corresponding to the data source based on the guide sequence, and use the migration rule to identify several migration differences of the data source.

[0038] Step 52: Locate the suspicious data segment and the corresponding suspicious storage node corresponding to each migration difference in the real-time data stream, and identify the first aggregation result between the suspicious data segment and all stored data segments and the second aggregation result between all stored data segments in the suspicious storage node;

[0039] Step 53: When the first aggregation result is different from the second aggregation result, it is determined that the suspicious data segment does not match the suspicious storage node, and the corresponding valid storage node is reassigned to the suspicious data segment according to the storage type corresponding to each storage node.

[0040] This invention provides a NAS-based data verification, repair, and aggregation system, comprising:

[0041] The preliminary verification module is used to generate a real-time data stream in the NAS based on each user's real-time access data and real-time shared data in the network, and to perform preliminary verification on the real-time data stream using the initial verification rules to obtain several data vulnerability information.

[0042] The behavior analysis module is used to identify several vulnerability strings in the real-time data stream based on the data vulnerability information, and to deduce the access behavior / sharing behavior of the vulnerability string based on the data characteristics corresponding to each vulnerability string.

[0043] The data repair module is used to generate relevant behavioral features corresponding to each access behavior / sharing behavior based on the real-time data stream, and use the relevant behavioral features to repair the corresponding vulnerability string to obtain a repaired data stream;

[0044] The migration execution module is used to divide the repair data stream into data segments and classify and store them according to the storage type corresponding to each storage node in the NAS, and to construct a migration guide for the corresponding data source based on the migration trajectory corresponding to each data segment.

[0045] The aggregation analysis module is used to find the corresponding suspicious data segments and perform aggregation verification based on the migration differences between the migration guidelines corresponding to each data source at different times, and adjust the storage nodes corresponding to the suspicious data segments based on the aggregation verification results.

[0046] In one implementable manner, the preliminary verification module includes:

[0047] The data tagging unit is used to transmit the user's real-time access data and real-time shared data in the network to the NAS for timestamp alignment to generate a real-time data stream. According to the first data source corresponding to each real-time access data and the second data source corresponding to each real-time shared data, the first data position of each real-time access data in the real-time data stream and the second data position of each real-time shared data in the real-time data stream are respectively data-tagged.

[0048] The procedure training unit is used to generate the data format features of the real-time data stream based on the data tags, obtain the data verification method corresponding to each data source, and perform fusion training on the data verification methods based on the data format features to obtain the initial verification rules of the real-time data stream.

[0049] The vulnerability identification unit is used to decompose the real-time data stream into continuous strings using the initial verification rules, perform real-time data verification on the continuous strings, screen out several abnormal strings, identify the abnormal type corresponding to each abnormal string, and generate data vulnerability information of the real-time data stream.

[0050] The beneficial effects of the above technical solution are as follows: To overcome the shortcomings of existing technologies and improve the system's data security, repair reliability, and access efficiency, the system first generates a real-time data stream by allowing users to access and share data in real time. Initial verification rules are used to preliminarily verify data vulnerability information, enabling immediate problem detection and saving time for subsequent repairs. This reduces losses caused by the spread of vulnerable data and ensures data integrity during real-time interaction. Then, vulnerability strings are identified based on the data vulnerability information, and access / sharing behaviors are deduced by combining data characteristics. This provides a behavioral dimension reference for subsequent repairs, preventing repeated vulnerability occurrences due to merely repairing data without optimizing behavior, thus improving the thoroughness of vulnerability handling. Furthermore, by utilizing the relevant characteristics of access / sharing behaviors to repair vulnerability strings and obtain a repair data stream, the system can accurately repair vulnerabilities. To address data security issues and preserve legitimate user edits, the system ensures that the repaired data can support subsequent user access and sharing operations, improving data availability. Then, the repaired data streams are divided and stored according to NAS storage node type. Simultaneously, migration guidelines are constructed based on data segment migration trajectories, optimizing the NAS system's storage resource allocation. This facilitates subsequent data flow path tracking, reduces the risk of data loss during migration, and improves the standardization of storage management. Finally, by analyzing differences in migration guidelines at different times, suspicious data segments are identified and aggregated for verification. Based on the results, storage nodes are adjusted, strengthening the NAS system's data security and storage flexibility. This not only prevents security risks such as data tampering and unauthorized access but also dynamically optimizes storage locations according to data security needs, further enhancing the NAS system's security and storage adaptability.

[0051] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0052] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0053] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0054] Figure 1 This is a schematic diagram illustrating the workflow of a NAS-based data verification, repair, and aggregation method in an embodiment of the present invention.

[0055] Figure 2 This is a schematic diagram of the composition of a NAS-based data verification, repair, and aggregation system in an embodiment of the present invention. Detailed Implementation

[0056] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0057] Example 1: This example provides a NAS-based data verification, repair, and aggregation method, such as... Figure 1 As shown, it includes:

[0058] Step 1: In the NAS, a real-time data stream is generated based on each user's real-time access data and real-time shared data in the network, and the real-time data stream is initially verified using initial verification rules to obtain several data vulnerability information.

[0059] Step 2: Identify several vulnerability strings in the real-time data stream based on the data vulnerability information, and deduce the access / sharing behavior of the vulnerability string based on the data characteristics corresponding to each vulnerability string;

[0060] Step 3: Generate relevant behavioral features corresponding to each access behavior / sharing behavior based on the real-time data stream, and use the relevant behavioral features to repair the corresponding vulnerability string to obtain a repair data stream;

[0061] Step 4: Divide the repair data stream into data segments and classify them according to the storage type corresponding to each storage node in the NAS. Construct a migration guide for the corresponding data source based on the migration trajectory corresponding to each data segment.

[0062] Step 5: Based on the migration differences between the migration guidelines corresponding to each data source at different times, find the corresponding suspicious data segments and perform aggregation verification, and adjust the storage nodes corresponding to the suspicious data segments according to the aggregation verification results.

[0063] In this example, real-time access data refers to the data presented when a user makes an access to the network, and real-time shared data refers to the data presented when a user makes a sharing to the network.

[0064] In this example, the data vulnerability information represents the information presented when an anomaly exists in the real-time data stream;

[0065] In this example, the vulnerability string represents a string that presents the vulnerability information;

[0066] In this example, access behavior refers to the vulnerable behavior presented in real-time access data, and sharing behavior refers to the vulnerable behavior presented in real-time shared data.

[0067] In this example, the relevant behavioral features represent the characteristics exhibited by behaviors that have a logical relationship with access / sharing behaviors;

[0068] In this example, character repair includes: character replacement, character deletion, character addition, and adding whitespace characters;

[0069] In this example, NAS stands for Network Attached Storage System;

[0070] In this example, the migration guide indicates a concise path to migrate a data segment from the real-time data stream to the corresponding storage node;

[0071] In this example, migration differences represent the differences between migration guidelines at different times;

[0072] In this example, the suspicious data segment refers to a data segment that is suspected of being stored in an incorrect location;

[0073] In this example, aggregate verification refers to the process of determining whether the storage node of a suspicious data segment is correct;

[0074] In this example, step 1 breaks the limitation of traditional NAS data verification being lagging, step 2 solves the problem that traditional vulnerability repair only targets the data itself and ignores the root cause of the behavior, step 3 overcomes the defects of traditional repair being one-size-fits-all and the data after repair not matching the actual use scenario, step 4 optimizes the storage resource allocation of the NAS system, and step 5 solves the problem that traditional NAS is unable to detect abnormal changes in data segments during migration.

[0075] In this example, after the classification storage and aggregation verification are completed, the data segment is transmitted to the corresponding storage node for storage according to the classification verification result;

[0076] Simultaneously, a verification node is configured in the NAS;

[0077] The verification node is used to set a corresponding data verification code for each data segment based on the relevant behavioral features and the classification verification result corresponding to each data segment.

[0078] When a user issues a data segment extraction command, the data verification code is used to identify the storage location of the corresponding data segment to be extracted and then the extraction is performed.

[0079] When the user selects a target storage node, after migrating several built-in data segments contained in the target storage node to the guiding storage node, the corresponding built-in data segments are verified using the corresponding data verification code, and the storage format of each built-in data segment in the guiding storage node is adjusted according to the verification result.

[0080] When there is an abnormal storage node in the NAS, a data recovery plan is generated using the corresponding data check code, and a storage node of the same type as the abnormal storage node is selected in the NAS. The data recovery plan is then used to perform data recovery on the storage node of the same type.

[0081] When the user issues a backup command, the corresponding data segment is backed up using the data checksum according to the backup range selected by the user.

[0082] The working principle and beneficial effects of the above technical solution are as follows: To overcome the shortcomings of existing technologies and improve the system's data security, repair reliability, and access efficiency, the system first generates a real-time data stream by allowing users to access and share data in real time. Initial verification rules are used to preliminarily verify data vulnerability information, enabling immediate problem detection and saving time for subsequent repairs. This reduces losses caused by the spread of vulnerable data and ensures data integrity during real-time interaction. Then, vulnerability strings are identified based on the data vulnerability information, and access / sharing behaviors are deduced based on data characteristics. This provides a behavioral dimension reference for subsequent repairs, preventing repeated vulnerability recurrence due to merely repairing data without optimizing behavior, thus improving the thoroughness of vulnerability handling. Furthermore, the relevant characteristics of access / sharing behaviors are used to repair vulnerability strings, resulting in a repair data stream that can accurately repair vulnerabilities. To address data security issues and preserve legitimate user edits, the system ensures that the repaired data can support subsequent user access and sharing operations, improving data availability. Then, the repaired data streams are divided and stored according to NAS storage node type. Simultaneously, migration guidelines are constructed based on data segment migration trajectories, optimizing the NAS system's storage resource allocation. This facilitates subsequent data flow path tracking, reduces the risk of data loss during migration, and improves the standardization of storage management. Finally, by analyzing differences in migration guidelines at different times, suspicious data segments are identified and aggregated for verification. Based on the results, storage nodes are adjusted, strengthening the NAS system's data security and storage flexibility. This not only prevents security risks such as data tampering and unauthorized access but also dynamically optimizes storage locations according to data security needs, further enhancing the NAS system's security and storage adaptability.

[0083] Example 2: Based on Example 1, the NAS-based data verification, repair, and aggregation method, step 1 includes:

[0084] Step 11: Transmit the user's real-time access data and real-time shared data in the network to the NAS for timestamp alignment to generate a real-time data stream. Based on the first data source corresponding to each real-time access data and the second data source corresponding to each real-time shared data, mark the first data position of each real-time access data and the second data position of each real-time shared data in the real-time data stream.

[0085] Step 12: Generate the data format features of the real-time data stream based on the data tags, obtain the data verification method corresponding to each data source, and perform fusion training on the data verification methods based on the data format features to obtain the initial verification rules of the real-time data stream;

[0086] Step 13: Decompose the real-time data stream into continuous strings using the initial verification rules, perform real-time data verification on the continuous strings, screen out several abnormal strings, identify the abnormal type corresponding to each abnormal string, and generate data vulnerability information for the real-time data stream.

[0087] In this example, the first data source represents the source corresponding to the real-time access data, the second data source represents the source corresponding to the real-time shared data, different data can correspond to different data sources, and one data source can also generate multiple data;

[0088] In this example, timestamp alignment ensures that data of different users and different types (access data and shared data) are arranged in order according to the actual interaction time, avoiding deviations in subsequent verification and analysis due to misalignment of data time sequence;

[0089] In this example, the first data location represents the location of the real-time access data in the real-time data stream, and the second data location represents the location of the real-time shared data in the real-time data stream;

[0090] In this example, fusion training refers to the process of first arranging the data verification methods according to the data format characteristics, and then adding a connection method between adjacent data verification methods to perform the verification steps.

[0091] The working principle and beneficial effects of the above technical solution are as follows: To ensure the accuracy and traceability of real-time data streams, and to construct highly adaptable initial verification rules for data verification, a real-time data stream is first generated through timestamp alignment. The locations of accessed and shared data are then marked according to their sources, reducing the time spent troubleshooting issues caused by ambiguous sources and improving the traceability of data management. Next, combining the data format characteristics generated by the data markings, verification methods from different data sources are integrated and trained to obtain initial verification rules. This identifies differences in data formats and verification requirements corresponding to different data sources, enabling the initial verification rules to cover the verification needs of multiple data types, thereby ensuring the accuracy and traceability of data streams. By dynamically generating rules based on the format characteristics of real-time data streams, the problem of fixed rules being unable to adapt to new data formats is avoided. This improves the flexibility and coverage of verification rules, ensuring thorough verification without blind spots. Finally, the initial verification rules decompose the real-time data stream into continuous strings and verify them in real time, screening for abnormal strings and identifying abnormal types. By decomposing the data stream into continuous strings for refined verification, the vulnerability discovery time is significantly shortened, and the retention and spread of vulnerable data within the system are reduced. In this way, a solid foundation is laid for the entire NAS data processing process from the data source, significantly improving the efficiency and accuracy of subsequent repair and aggregation stages, and laying a key foundation for ensuring the integrity of NAS system data.

[0092] Example 3: Based on Example 2, the NAS-based data verification, repair, and aggregation method further includes:

[0093] The frequency of each anomaly type in the real-time data stream is counted to determine the high-frequency anomaly characteristics of the real-time data stream.

[0094] When the abnormal value corresponding to the high-frequency abnormal feature is higher than the specified abnormal value, the NAS is repaired according to the abnormal factors corresponding to the high-frequency abnormal feature.

[0095] In this example, the outlier value is specified as 6.

[0096] The working principle and beneficial effects of the above technical solution are as follows: When the real-time data stream repeatedly exhibits the same abnormal characteristics, it is determined that the NAS function is damaged. Therefore, the corresponding functions are repaired in a timely manner to reduce anomalies in the data.

[0097] Example 4: Based on Example 1, the NAS-based data verification, repair, and aggregation method, step 2 includes:

[0098] Step 21: Identify the vulnerability-related data segments in the real-time data stream for each data vulnerability information, and convert each data element contained in the vulnerability-related data segment into an independent string. Merge the independent strings to generate several vulnerability strings in the real-time data stream.

[0099] Step 22: Identify the meaning of each vulnerability-related data segment in the real-time data stream, and the data relationship between each vulnerability-related data segment and the real-time data stream; construct the fault tolerance range corresponding to the vulnerability string based on the data meaning and the data relationship.

[0100] Step 23: Construct data features for each vulnerability string in the real-time data stream, perform behavior transformation on the data features according to the corresponding fault tolerance range, obtain the restoration behavior corresponding to each vulnerability string, determine the behavior attribute corresponding to each restoration behavior according to the data meaning, and obtain the access behavior / sharing behavior of each vulnerability string.

[0101] In this example, a standalone string represents the string corresponding to a data element;

[0102] In this example, the data meaning refers to the significance of vulnerability-related data presented in the real-time data stream;

[0103] In this example, the data relationship represents the positional and logical relationship between vulnerability-related data and the real-time data stream;

[0104] In this example, the tolerance range refers to the range within which the vulnerability string appears abnormal but does not affect the real-time data stream;

[0105] In this example, the restoration behavior refers to the user behavior that restores the vulnerability based on the vulnerability string.

[0106] The working principle and beneficial effects of the above technical solution are as follows: To further improve the efficiency and effectiveness of vulnerability handling, firstly, by identifying vulnerability-related data segments, converting data elements into independent strings, and merging them to generate vulnerability strings, the specific character manifestations of each type of vulnerability can be clearly located. This lays a precise data foundation for subsequent analysis of vulnerability causes and deduction of corresponding behaviors, reducing subsequent processing deviations caused by ambiguous vulnerability information. Furthermore, by combining the data meaning and data relationships of vulnerability-related data segments, the fault tolerance range of the vulnerability string is constructed. This ensures the strictness of critical data while reserving reasonable fault tolerance space for non-critical data, improving the flexibility and practicality of vulnerability handling and reducing unnecessary repair resource consumption. Finally, by constructing the data characteristics of the vulnerability string and combining the fault tolerance range conversion to obtain the restored behavior and determine the behavioral attributes, the access / sharing behavior corresponding to the vulnerability can be accurately deduced, providing a scientific basis for subsequent data repair.

[0107] Example 5: Based on Example 4, the NAS-based data verification, repair, and aggregation method further includes:

[0108] Based on the behavioral attributes, the vulnerability string is divided into access strings and sharing strings;

[0109] Simulate the user's access process on the network based on the data meaning corresponding to the access string, and generate the access behavior corresponding to the vulnerability string;

[0110] Based on the data meaning corresponding to the shared string, simulate the user's sharing content and sharing process in the network, and generate the sharing behavior corresponding to the shared string.

[0111] The working principle and beneficial effects of the above technical solution are as follows: it generates corresponding behaviors based on the strings corresponding to different behaviors, laying the foundation for subsequent data repair.

[0112] Example 6: Based on Example 1, the NAS-based data verification, repair, and aggregation method, step 3 includes:

[0113] Step 31: Construct several network operation behaviors of the user in the network based on the real-time data stream, determine several related behaviors corresponding to each access behavior / sharing behavior based on the behavior logic corresponding to each network operation behavior, and obtain the behavior purpose and behavior basis corresponding to each related behavior respectively.

[0114] Step 32: Construct relevant behavioral features for each vulnerability string based on the behavioral purpose and behavioral basis of several related behaviors corresponding to each access behavior / sharing behavior, and identify logical contradiction features between the vulnerability string and the corresponding related behavioral features;

[0115] Step 33: Based on the network operation behavior, deduce several estimated behaviors corresponding to each vulnerability string, and use the behavioral purpose and behavioral basis corresponding to each related behavior to perform contradiction repair on the corresponding vulnerability string until the logical contradiction features are eliminated to generate a repair string;

[0116] Step 34: Convert the repair string into a repair behavior, take the estimated behavior with the highest similarity to the repair behavior as the repair result, and use the repair result to repair the real-time data stream accordingly to obtain a repaired data stream.

[0117] In this example, network operation behavior refers to the actions a user takes on the network;

[0118] In this example, behavioral logic represents the logic presented by a network operation behavior;

[0119] In this example, the related behavior refers to the behavior that is related to the access behavior / sharing behavior;

[0120] In this example, the purpose of the behavior refers to the purpose of the relevant behavior, and the basis of the behavior refers to the underlying conditions for performing a relevant behavior.

[0121] In this example, the logical contradiction feature represents a contradiction between the vulnerability string and the related behavioral features;

[0122] In this example, the estimated behavior refers to the behavior that the vulnerability string might correspond to after speculation.

[0123] The working principle and beneficial effects of the above technical solution are as follows: To effectively repair data, user network operation behavior is first constructed based on real-time data streams. This is combined with behavioral logic to determine relevant access / sharing behaviors and extract their purpose and basis. This provides a basis for subsequent repairs that aligns with actual user operation scenarios, preventing repaired data from becoming unusable due to inconsistencies with user logic. Then, the relevant behavioral characteristics of vulnerability strings are constructed based on the purpose and basis of related behaviors, and logical contradictions are identified, providing a clear direction for repair, significantly reducing ineffective repair attempts, and improving repair efficiency. Furthermore, estimated behaviors are derived from network operation behavior, and contradictions are repaired based on the purpose and basis of related behaviors until logical contradictions are eliminated, ensuring the targeted and thorough nature of the repair. This ensures that the repaired data meets user operation purposes and actual usage needs. Finally, the repair string is converted into repair behaviors, and similarity matching is used to compare it with the estimated behaviors to determine the repair result. The real-time data stream is then repaired, thus both data repair and ensuring that the repaired behaviors are consistent with user operation logic. This reduces compatibility issues between the repaired data and subsequent network operations, guaranteeing the long-term availability of the repaired data stream.

[0124] Example 7: Based on Example 1, the NAS-based data verification, repair, and aggregation method, step 4 includes:

[0125] Step 41: Perform a functional inventory of each storage node in the NAS to determine the storage type and remaining storage space of each storage node. At the same time, divide the repair data stream into data segments to obtain several data segments.

[0126] Step 42: Match each data segment with different storage types to obtain the matching result for each data segment, and match the corresponding migration storage node for each data segment based on the remaining storage space corresponding to each storage node.

[0127] Step 43: Store each data segment into the corresponding migration storage node, and at the same time obtain the data source corresponding to each data segment, and record the migration process of each data segment from the data source to the migration storage node to generate the migration trajectory corresponding to each data segment;

[0128] Step 44: Statistically analyze the migration trajectory corresponding to each of the data sources, determine the migration characteristics between each data source and different storage nodes, and generate corresponding migration guidelines.

[0129] In this example, the migrated storage node represents the storage node that will be migrated for a data segment;

[0130] In this example, when it is necessary to find the storage location of a certain data segment or to investigate data loss issues, the migration trajectory can quickly locate the current storage node and historical flow path of the data.

[0131] The working principle and beneficial effects of the above technical solution are as follows: First, a functional inventory of the storage nodes is performed to determine the storage type and remaining storage space. At the same time, data segments of the repair data stream are divided, which can smoothly store high-frequency access data in low-speed nodes, providing a prerequisite for accurate matching of storage nodes in the future. Then, the data segments are matched with storage types, and the remaining storage space is combined to match migration storage nodes for the data segments. This not only matches the corresponding storage node for each data segment, but also balances the space occupation of each storage node. The data segments are further stored in the migration storage nodes and the migration trajectory is recorded. Finally, the migration trajectory corresponding to the statistical data source is used to generate migration guidance, which provides a scientific basis for subsequent data management and migration optimization, improves the standardization and predictability of NAS system data management, and lays an efficient and orderly storage foundation for subsequent data aggregation verification and long-term management.

[0132] Example 8: Based on Example 1, the NAS-based data verification, repair, and aggregation method, step 5 includes:

[0133] Step 51: Statistically count several historical migration guidelines corresponding to each data source, and arrange the historical migration guidelines in chronological order to obtain a guide sequence corresponding to each data source. Construct a migration rule corresponding to the data source based on the guide sequence, and use the migration rule to identify several migration differences of the data source.

[0134] Step 52: Locate the suspicious data segment and the corresponding suspicious storage node corresponding to each migration difference in the real-time data stream, and identify the first aggregation result between the suspicious data segment and all stored data segments and the second aggregation result between all stored data segments in the suspicious storage node;

[0135] Step 53: When the first aggregation result is different from the second aggregation result, it is determined that the suspicious data segment does not match the suspicious storage node, and the corresponding valid storage node is reassigned to the suspicious data segment according to the storage type corresponding to each storage node.

[0136] In this example, migration discrepancies represent migration guidelines that do not conform to migration patterns;

[0137] In this example, when the first aggregation result differs from the second aggregation result, it indicates that the suspicious data segment has seriously interfered with the storage type of the suspicious storage node. The verification method of comparing the two aggregation results is more convincing than the traditional single data verification, ensuring that the location and judgment of the suspicious data segment are accurate.

[0138] The working principle and beneficial effects of the above technical solution are as follows: First, historical migration guidance from statistical data sources is collected and sorted by time to construct a guidance sequence. Then, migration patterns are mined and migration differences are identified. By identifying differences based on historical patterns, anomalies in data migration can be captured in a timely manner, avoiding security risks caused by abnormal data remaining in erroneous nodes for a long time. Furthermore, suspicious data segments and suspicious storage nodes corresponding to migration differences are located in the real-time data stream. The first and second aggregation results are obtained through aggregation verification, which can initially determine that the storage location of the data segment is abnormal. Then, if the aggregation results are different, it is determined that the data segment and storage node are inconsistent, and effective storage nodes are reallocated according to storage type. This not only significantly shortens the abnormal data processing time, but also ensures that the reallocated nodes are fully adapted to the data characteristics. In this way, data storage deviations can be corrected in a timely manner, maintaining the high efficiency and orderliness of the storage system. Moreover, through continuous migration pattern learning and storage optimization, the adaptability of the NAS system to data storage needs can be continuously improved.

[0139] Example 9: This example provides a NAS-based data verification, repair, and aggregation system, such as... Figure 2 As shown, it includes:

[0140] The preliminary verification module is used to generate a real-time data stream in the NAS based on each user's real-time access data and real-time shared data in the network, and to perform preliminary verification on the real-time data stream using the initial verification rules to obtain several data vulnerability information.

[0141] The behavior analysis module is used to identify several vulnerability strings in the real-time data stream based on the data vulnerability information, and to deduce the access behavior / sharing behavior of the vulnerability string based on the data characteristics corresponding to each vulnerability string.

[0142] The data repair module is used to generate relevant behavioral features corresponding to each access behavior / sharing behavior based on the real-time data stream, and use the relevant behavioral features to repair the corresponding vulnerability string to obtain a repaired data stream;

[0143] The migration execution module is used to divide the repair data stream into data segments and classify and store them according to the storage type corresponding to each storage node in the NAS, and to construct a migration guide for the corresponding data source based on the migration trajectory corresponding to each data segment.

[0144] The aggregation analysis module is used to find the corresponding suspicious data segments and perform aggregation verification based on the migration differences between the migration guidelines corresponding to each data source at different times, and adjust the storage nodes corresponding to the suspicious data segments based on the aggregation verification results.

[0145] In this example, real-time access data refers to the data presented when a user makes an access to the network, and real-time shared data refers to the data presented when a user makes a sharing to the network.

[0146] In this example, the data vulnerability information represents the information presented when an anomaly exists in the real-time data stream;

[0147] In this example, the vulnerability string represents a string that presents the vulnerability information;

[0148] In this example, access behavior refers to the vulnerable behavior presented in real-time access data, and sharing behavior refers to the vulnerable behavior presented in real-time shared data.

[0149] In this example, the relevant behavioral features represent the characteristics exhibited by behaviors that have a logical relationship with access / sharing behaviors;

[0150] In this example, character repair includes: character replacement, character deletion, character addition, and adding whitespace characters;

[0151] In this example, NAS stands for Network Attached Storage System;

[0152] In this example, the migration guide indicates a concise path to migrate a data segment from the real-time data stream to the corresponding storage node;

[0153] In this example, migration differences represent the differences between migration guidelines at different times;

[0154] In this example, the suspicious data segment refers to a data segment that is suspected of being stored in an incorrect location;

[0155] In this example, aggregate verification refers to the process of determining whether the storage node of a suspicious data segment is correct;

[0156] In this example, the preliminary verification module breaks through the limitation of the strong lag in traditional NAS data verification, the behavior analysis module solves the problem that traditional vulnerability repair only targets the data itself and ignores the root cause of behavior, the data repair module overcomes the defects of traditional repair being one-size-fits-all and the data after repair not matching the actual use scenario, the migration execution module optimizes the storage resource allocation of the NAS system, and the aggregation analysis module solves the problem that traditional NAS is unable to detect abnormal changes in data segments during migration.

[0157] In this example, after the classification storage and aggregation verification are completed, the data segment is transmitted to the corresponding storage node for storage according to the classification verification result;

[0158] Simultaneously, a verification node is configured in the NAS;

[0159] The verification node is used to set a corresponding data verification code for each data segment based on the relevant behavioral features and the classification verification result corresponding to each data segment.

[0160] When a user issues a data segment extraction command, the data verification code is used to identify the storage location of the corresponding data segment to be extracted and then the extraction is performed.

[0161] When the user selects a target storage node, after migrating several built-in data segments contained in the target storage node to the guiding storage node, the corresponding built-in data segments are verified using the corresponding data verification code, and the storage format of each built-in data segment in the guiding storage node is adjusted according to the verification result.

[0162] When there is an abnormal storage node in the NAS, a data recovery plan is generated using the corresponding data check code, and a storage node of the same type as the abnormal storage node is selected in the NAS. The data recovery plan is then used to perform data recovery on the storage node of the same type.

[0163] When the user issues a backup command, the corresponding data segment is backed up using the data checksum according to the backup range selected by the user.

[0164] The working principle and beneficial effects of the above technical solution are as follows: To overcome the shortcomings of existing technologies and improve the system's data security, repair reliability, and access efficiency, the system first generates a real-time data stream by allowing users to access and share data in real time. Initial verification rules are used to preliminarily verify data vulnerability information, enabling immediate problem detection and saving time for subsequent repairs. This reduces losses caused by the spread of vulnerable data and ensures data integrity during real-time interaction. Then, vulnerability strings are identified based on the data vulnerability information, and access / sharing behaviors are deduced based on data characteristics. This provides a behavioral dimension reference for subsequent repairs, preventing repeated vulnerability recurrence due to merely repairing data without optimizing behavior, thus improving the thoroughness of vulnerability handling. Furthermore, the relevant characteristics of access / sharing behaviors are used to repair vulnerability strings, resulting in a repair data stream that can accurately repair vulnerabilities. To address data security issues and preserve legitimate user edits, the system ensures that the repaired data can support subsequent user access and sharing operations, improving data availability. Then, the repaired data streams are divided and stored according to NAS storage node type. Simultaneously, migration guidelines are constructed based on data segment migration trajectories, optimizing the NAS system's storage resource allocation. This facilitates subsequent data flow path tracking, reduces the risk of data loss during migration, and improves the standardization of storage management. Finally, by analyzing differences in migration guidelines at different times, suspicious data segments are identified and aggregated for verification. Based on the results, storage nodes are adjusted, strengthening the NAS system's data security and storage flexibility. This not only prevents security risks such as data tampering and unauthorized access but also dynamically optimizes storage locations according to data security needs, further enhancing the NAS system's security and storage adaptability.

[0165] Example 10: Based on Example 9, the preliminary verification module of the NAS-based data verification, repair, and aggregation system includes:

[0166] The data tagging unit is used to transmit the user's real-time access data and real-time shared data in the network to the NAS for timestamp alignment to generate a real-time data stream. According to the first data source corresponding to each real-time access data and the second data source corresponding to each real-time shared data, the first data position of each real-time access data in the real-time data stream and the second data position of each real-time shared data in the real-time data stream are respectively data-tagged.

[0167] The procedure training unit is used to generate the data format features of the real-time data stream based on the data tags, obtain the data verification method corresponding to each data source, and perform fusion training on the data verification methods based on the data format features to obtain the initial verification rules of the real-time data stream.

[0168] The vulnerability identification unit is used to decompose the real-time data stream into continuous strings using the initial verification rules, perform real-time data verification on the continuous strings, screen out several abnormal strings, identify the abnormal type corresponding to each abnormal string, and generate data vulnerability information of the real-time data stream.

[0169] In this example, the first data source represents the source corresponding to the real-time access data, the second data source represents the source corresponding to the real-time shared data, different data can correspond to different data sources, and one data source can also generate multiple data;

[0170] In this example, timestamp alignment ensures that data of different users and different types (access data and shared data) are arranged in order according to the actual interaction time, avoiding deviations in subsequent verification and analysis due to misalignment of data time sequence;

[0171] In this example, the first data location represents the location of the real-time access data in the real-time data stream, and the second data location represents the location of the real-time shared data in the real-time data stream;

[0172] In this example, fusion training refers to the process of first arranging the data verification methods according to the data format characteristics, and then adding a connection method between adjacent data verification methods to perform the verification steps.

[0173] The working principle and beneficial effects of the above technical solution are as follows: To ensure the accuracy and traceability of real-time data streams, and to construct highly adaptable initial verification rules for data verification, a real-time data stream is first generated through timestamp alignment. The locations of accessed and shared data are then marked according to their sources, reducing the time spent troubleshooting issues caused by ambiguous sources and improving the traceability of data management. Next, combining the data format characteristics generated by the data markings, verification methods from different data sources are integrated and trained to obtain initial verification rules. This identifies differences in data formats and verification requirements corresponding to different data sources, enabling the initial verification rules to cover the verification needs of multiple data types, thereby ensuring the accuracy and traceability of data streams. By dynamically generating rules based on the format characteristics of real-time data streams, the problem of fixed rules being unable to adapt to new data formats is avoided. This improves the flexibility and coverage of verification rules, ensuring thorough verification without blind spots. Finally, the initial verification rules decompose the real-time data stream into continuous strings and verify them in real time, screening for abnormal strings and identifying abnormal types. By decomposing the data stream into continuous strings for refined verification, the vulnerability discovery time is significantly shortened, and the retention and spread of vulnerable data within the system are reduced. In this way, a solid foundation is laid for the entire NAS data processing process from the data source, significantly improving the efficiency and accuracy of subsequent repair and aggregation stages, and laying a key foundation for ensuring the integrity of NAS system data.

[0174] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A NAS-based data verification, repair, and aggregation method, characterized in that, include: Step 1: In the NAS, a real-time data stream is generated based on each user's real-time access data and real-time shared data in the network, and the real-time data stream is initially verified using initial verification rules to obtain several data vulnerability information. Step 2: Identify several vulnerability strings in the real-time data stream based on the data vulnerability information, and deduce the access / sharing behavior of the vulnerability string based on the data characteristics corresponding to each vulnerability string; Step 3: Generate relevant behavioral features corresponding to each access behavior / sharing behavior based on the real-time data stream, and use the relevant behavioral features to repair the corresponding vulnerability string to obtain a repair data stream; Step 4: Divide the repair data stream into data segments and classify them according to the storage type corresponding to each storage node in the NAS. Construct a migration guide for the corresponding data source based on the migration trajectory corresponding to each data segment. Step 5: Based on the migration differences between the migration guidelines corresponding to each data source at different times, find the corresponding suspicious data segments and perform aggregation verification, and adjust the storage nodes corresponding to the suspicious data segments according to the aggregation verification results; Step 1 includes: Step 11: Transmit the user's real-time access data and real-time shared data in the network to the NAS for timestamp alignment to generate a real-time data stream. Based on the first data source corresponding to each real-time access data and the second data source corresponding to each real-time shared data, mark the first data position of each real-time access data and the second data position of each real-time shared data in the real-time data stream. Step 12: Generate the data format features of the real-time data stream based on the data tags, obtain the data verification method corresponding to each data source, and perform fusion training on the data verification methods based on the data format features to obtain the initial verification rules of the real-time data stream; Step 13: Decompose the real-time data stream into continuous strings using the initial verification rules, perform real-time data verification on the continuous strings, screen out several abnormal strings, identify the abnormal type corresponding to each abnormal string, and generate data vulnerability information for the real-time data stream. Step 3 includes: Step 31: Construct several network operation behaviors of the user in the network based on the real-time data stream, determine several related behaviors corresponding to each access behavior / sharing behavior based on the behavior logic corresponding to each network operation behavior, and obtain the behavior purpose and behavior basis corresponding to each related behavior respectively. Step 32: Construct relevant behavioral features for each vulnerability string based on the behavioral purpose and behavioral basis of several related behaviors corresponding to each access behavior / sharing behavior, and identify logical contradiction features between the vulnerability string and the corresponding related behavioral features; Step 33: Based on the network operation behavior, deduce several estimated behaviors corresponding to each vulnerability string, and use the behavioral purpose and behavioral basis corresponding to each related behavior to perform contradiction repair on the corresponding vulnerability string until the logical contradiction features are eliminated to generate a repair string; Step 34: Convert the repair string into a repair behavior, take the estimated behavior with the highest similarity to the repair behavior as the repair result, and use the repair result to repair the real-time data stream accordingly to obtain a repaired data stream.

2. The NAS-based data verification, repair, and aggregation method as described in claim 1, characterized in that, Also includes: The frequency of each anomaly type in the real-time data stream is counted to determine the high-frequency anomaly characteristics of the real-time data stream. When the abnormal value corresponding to the high-frequency abnormal feature is higher than the specified abnormal value, the NAS is repaired according to the abnormal factors corresponding to the high-frequency abnormal feature.

3. The NAS-based data verification, repair, and aggregation method as described in claim 1, characterized in that, Step 2 includes: Step 21: Identify the vulnerability-related data segments in the real-time data stream for each data vulnerability information, and convert each data element contained in the vulnerability-related data segment into an independent string. Merge the independent strings to generate several vulnerability strings in the real-time data stream. Step 22: Identify the meaning of each vulnerability-related data segment in the real-time data stream, and the data relationship between each vulnerability-related data segment and the real-time data stream; construct the fault tolerance range corresponding to the vulnerability string based on the data meaning and the data relationship. Step 23: Construct data features for each vulnerability string in the real-time data stream, perform behavior transformation on the data features according to the corresponding fault tolerance range, obtain the restoration behavior corresponding to each vulnerability string, determine the behavior attribute corresponding to each restoration behavior according to the data meaning, and obtain the access behavior / sharing behavior of each vulnerability string.

4. The NAS-based data verification, repair, and aggregation method as described in claim 3, characterized in that, Also includes: Based on the behavioral attributes, the vulnerability string is divided into access strings and sharing strings; Simulate the user's access process on the network based on the data meaning corresponding to the access string, and generate the access behavior corresponding to the vulnerability string; Based on the data meaning corresponding to the shared string, simulate the user's sharing content and sharing process in the network, and generate the sharing behavior corresponding to the shared string.

5. The NAS-based data verification, repair, and aggregation method as described in claim 1, characterized in that, Step 4 includes: Step 41: Perform a functional inventory of each storage node in the NAS to determine the storage type and remaining storage space of each storage node. At the same time, divide the repair data stream into data segments to obtain several data segments. Step 42: Match each data segment with different storage types to obtain the matching result for each data segment, and match the corresponding migration storage node for each data segment based on the remaining storage space corresponding to each storage node. Step 43: Store each data segment into the corresponding migration storage node, and at the same time obtain the data source corresponding to each data segment, and record the migration process of each data segment from the data source to the migration storage node to generate the migration trajectory corresponding to each data segment; Step 44: Statistically analyze the migration trajectory corresponding to each of the data sources, determine the migration characteristics between each data source and different storage nodes, and generate corresponding migration guidelines.

6. The NAS-based data verification, repair, and aggregation method as described in claim 1, characterized in that, Step 5 includes: Step 51: Statistically count several historical migration guidelines corresponding to each data source, and arrange the historical migration guidelines in chronological order to obtain a guide sequence corresponding to each data source. Construct a migration rule corresponding to the data source based on the guide sequence, and use the migration rule to identify several migration differences of the data source. Step 52: Locate the suspicious data segment and the corresponding suspicious storage node corresponding to each migration difference in the real-time data stream, and identify the first aggregation result between the suspicious data segment and all stored data segments and the second aggregation result between all stored data segments in the suspicious storage node; Step 53: When the first aggregation result is different from the second aggregation result, it is determined that the suspicious data segment does not match the suspicious storage node, and the corresponding valid storage node is reassigned to the suspicious data segment according to the storage type corresponding to each storage node.

7. A NAS-based data verification, repair, and aggregation system, characterized in that, include: The preliminary verification module is used to generate a real-time data stream in the NAS based on each user's real-time access data and real-time shared data in the network, and to perform preliminary verification on the real-time data stream using the initial verification rules to obtain several data vulnerability information. The behavior analysis module is used to identify several vulnerability strings in the real-time data stream based on the data vulnerability information, and to deduce the access behavior / sharing behavior of the vulnerability string based on the data characteristics corresponding to each vulnerability string. The data repair module is used to generate relevant behavioral features corresponding to each access behavior / sharing behavior based on the real-time data stream, and use the relevant behavioral features to repair the corresponding vulnerability string to obtain a repaired data stream; The migration execution module is used to divide the repair data stream into data segments and classify and store them according to the storage type corresponding to each storage node in the NAS, and to construct a migration guide for the corresponding data source based on the migration trajectory corresponding to each data segment. The aggregation analysis module is used to find the corresponding suspicious data segments and perform aggregation verification based on the migration differences between the migration guidelines corresponding to each data source at different times, and adjust the storage nodes corresponding to the suspicious data segments based on the aggregation verification results. The preliminary verification module includes: The data tagging unit is used to transmit the user's real-time access data and real-time shared data in the network to the NAS for timestamp alignment to generate a real-time data stream. According to the first data source corresponding to each real-time access data and the second data source corresponding to each real-time shared data, the first data position of each real-time access data in the real-time data stream and the second data position of each real-time shared data in the real-time data stream are respectively data-tagged. The procedure training unit is used to generate the data format features of the real-time data stream based on the data tags, obtain the data verification method corresponding to each data source, and perform fusion training on the data verification methods based on the data format features to obtain the initial verification rules of the real-time data stream. The vulnerability identification unit is used to decompose the real-time data stream into continuous strings using the initial verification rules, perform real-time data verification on the continuous strings, screen out several abnormal strings, identify the abnormal type corresponding to each abnormal string, and generate data vulnerability information of the real-time data stream. The system is also used for: Based on the real-time data stream, construct several network operation behaviors of the user on the network, determine several related behaviors corresponding to each access behavior / sharing behavior based on the behavior logic corresponding to each network operation behavior, and obtain the behavior purpose and behavior basis corresponding to each related behavior respectively. Based on the behavioral purpose and behavioral basis of several related behaviors corresponding to each access behavior / sharing behavior, construct the relevant behavioral features of each vulnerability string, and identify the logical contradiction features between the vulnerability string and the corresponding relevant behavioral features; Based on the network operation behavior, several estimated behaviors are derived for each vulnerability string. The corresponding vulnerability string is then repaired by using the behavioral purpose and behavioral basis corresponding to each related behavior until the logical contradiction features are eliminated, and a repair string is generated. The repair string is converted into a repair behavior, and the estimated behavior with the highest similarity to the repair behavior is regarded as the repair result. The real-time data stream is repaired accordingly using the repair result to obtain a repaired data stream.

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