A data channel management system and method based on big data

Abstract

The present invention relates to the field of computer technology, and specifically to a data channel management system and method based on big data. The system comprises a historical fault assessment module, a fault monitoring and analysis module, a fault warning condition setting module and a data transmission channel selection module. The historical fault assessment module obtains a computer security diary report through historical data, extracts fault data information in the security diary report, and constructs a historical fault assessment model in combination with the impact degree of the corresponding fault. The present invention obtains computer security report information in historical data, analyzes the fault data in the security report information, and then obtains the impact degree of different faults on the data transmission channel, and monitors the fault type corresponding to the data transmission channel in real time. By matching the monitored fault type with the historical fault assessment model, a warning signal is issued in combination with the impact degree of the corresponding fault, and the data transmission channel is adjusted, thereby improving the data transmission efficiency.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a data channel management system and method based on big data. Background Art

[0002] After the continuous development of communication technology in recent years, data transmission has received more and more attention in the industry. The speed of data transmission directly affects the data processing of the client to a certain extent, and then affects the user experience of the client. Network transmission refers to the process of communicating through a series of lines through circuit adjustment and changes based on network transmission protocols. Network transmission requires a medium, which is the physical path between the sender and the receiver in the network. It has a certain impact on the data communication of the network. Commonly used transmission media include: twisted pair, coaxial cable, optical fiber, wireless transmission media. Network protocols are some specifications for transmitting and managing information in the network. The most important thing in network transmission is to conduct real-time monitoring during data transmission to ensure stable and continuous network transmission.

[0003] However, due to the objective existence of network instability factors, when the acceleration node server encounters unstable data transmission in the network, the server's acceleration behavior for data transmission is almost ineffective, which in turn causes abnormalities in the data transmission process in the data channel. Therefore, how to reasonably manage the data channel and issue early warnings for abnormal data transmission is one of the problems that need to be solved urgently. Summary of the invention

[0004] The purpose of the present invention is to provide a computer-based operation behavior early warning system and method to solve the problems raised in the above background technology. The present invention provides the following technical solutions:

[0005] A data channel management method based on big data, the method comprising the following steps:

[0006] S1. Query the security diary report of the Ath computer through historical data to obtain a set of fault data analysis reports in the security diary report, and build a historical fault assessment model in combination with the fault data analysis report;

[0007] S2. Acquire a set of fault data analysis reports in any period in any data transmission channel in real time, and analyze the evaluation value corresponding to the set of fault data analysis reports in combination with a historical fault evaluation model;

[0008] S3, obtaining the analysis results in S2, and further analyzing the average fault occurrence rate to obtain the fault warning condition value;

[0009] S4. Monitor the computer data transmission process in real time, and adjust the data channel interface corresponding to the data transmission in real time based on the early warning signal.

[0010] Furthermore, the method in S1 comprises the following steps:

[0011] Step 1001: record the fault data analysis report set described in S1 as set B = (B1, B2, B3, ..., B n ), where B n Indicates the fault type corresponding to the fault in the nth security diary report, where n represents the total number of fault data analysis report sets in the security diary report of the Ath computer;

[0012] Step 1002: construct a set of fault influencing factors based on the factors that constitute the fault type in the fault analysis report set, and record the set of fault influencing factors corresponding to the rth security diary report as G (B，r) ,

[0013]

[0014] in, Indicates that the fault influencing factor set corresponding to the rth safety diary report constitutes the mth sub-fault influencing factor set of the fault;

[0015] Step 1003: Obtain the mth sub-fault influencing factor set that constitutes the fault influencing factor set corresponding to the rth security diary report in step 1002,

[0016] in

[0017] in represents the pth fault influencing factor in the mth sub-fault influencing factor set that constitutes the fault in the fault influencing factor set corresponding to the rth security diary report, and p represents the total number of elements in the mth sub-fault influencing factor set that constitutes the fault in the fault influencing factor set corresponding to the rth security diary report;

[0018] Step 1004: construct an impact assessment set according to the impact levels corresponding to different sub-fault impact factors, denoted as

[0019] V=(V1, V2, V3, V4, V5), where V1 indicates that the influence degree of the sub-fault influence factor on the data channel is level V, V2 indicates that the influence degree of the sub-fault influence factor on the data channel is level IV, V3 indicates that the influence degree of the sub-fault influence factor on the data channel is level III, V4 indicates that the influence degree of the sub-fault influence factor on the data channel is level II, and V5 indicates that the influence degree of the sub-fault influence factor on the data channel is level I, wherein the influence degrees corresponding to the different sub-fault influence factors can be queried through the preset form of the database;

[0020] Step 1005: The fault influencing factor set corresponding to the rth security diary report constitutes the mth sub-fault influencing factor set that generates the fault, and the membership degree of each element in the impact degree assessment set is calculated in turn, which is recorded as

[0021] where Q∈[1,5],

[0022] It represents the total number of fault influencing factors with an influence degree of Q in the mth sub-fault influencing factor set that constitutes the fault caused by the fault influencing factor set corresponding to the rth security diary report, Indicates the total number of elements in the mth sub-fault influencing factor set that constitutes the fault generated by the fault influencing factor set corresponding to the rth safety diary report;

[0023] The fault influencing factor set corresponding to the rth safety diary report constitutes the mth sub-fault influencing factor set of the fault. Each element in the set of influencing factors calculates the membership degree of each element in the impact degree assessment set in turn.

[0024] Step 1006: According to the calculation result of step 1005, obtain the evaluation value of the influence degree of each element in the mth sub-fault influencing factor set that constitutes the fault caused by the fault influencing factor set corresponding to the rth security diary report, which is recorded as

[0025]

[0026] Step 1007: Repeat steps 1002 to 1006 to obtain the evaluation membership matrix corresponding to the fault influencing factor set, which is recorded as

[0027]

[0028] in Indicates that the fault influencing factor set corresponding to the rth security diary report constitutes the mth sub-fault influencing factor of the fault The impact level in the evaluation is the membership degree corresponding to V1,

[0029] in Indicates that the fault influencing factor set corresponding to the rth security diary report constitutes the mth sub-fault influencing factor of the fault The impact level in the evaluation is the membership degree corresponding to V2,

[0030] in Indicates that the fault influencing factor set corresponding to the rth security diary report constitutes the mth sub-fault influencing factor of the fault The medium impact level is assessed as the membership corresponding to V5;

[0031] Step 1008: construct a historical fault assessment model.

[0032]

[0033] Where P B express The corresponding matrix determinant value, ω r Indicates the weight value corresponding to the fault type corresponding to the rth security diary report. The weight value is queried through the database preset form, where each fault type in the database preset form has a unique weight value.

[0034] The present invention obtains the corresponding computer security diary report in the monitored area through historical data, constructs a fault data analysis report set by extracting fault data information in the security diary report, and constructs a fault influencing factor set according to the factors that constitute the fault in the extracted fault data analysis report set, and then constructs an evaluation set according to the impact degrees corresponding to different fault influencing factors. By constructing a fuzzy mapping of the fault influencing factor set to the impact degree evaluation set, a historical fault evaluation model is constructed based on the fuzzy mapping, which provides data reference for the subsequent construction of predicted fault condition values ​​and real-time adjustment of data transmission channels.

[0035] Furthermore, the method in S2 comprises the following steps:

[0036] Step 2001, setting the computer security diary report analysis period to α;

[0037] Step 2002: randomly obtain a set of data analysis reports in any period of any data transmission channel, and record the set of data analysis reports as set D = (D1, D2, D3, ..., D j ), where D j represents the data type corresponding to the j-th data in the data analysis report set, and j represents the total number of data types in the data analysis report set;

[0038] Step 2003: Calculate the similarity between each set and the fault analysis report set in the historical security diary report, denoted as X.

[0039]

[0040] Where |B∩D| represents the number of elements in the intersection of set B and set D, |B| represents the number of elements in set B, and |D| represents the number of elements in set D.

[0041] If X=0, then there is no fault in the judgment set D.

[0042] If X≠0, it is determined that there is a fault in the set D, and the analysis report set with the fault in the data analysis report set is extracted, which is recorded as the set in Indicates the fault type corresponding to the hth fault in the fault analysis report set, h indicates the total number of fault types in the fault analysis report set, and determines whether the fault types in the fault analysis report set are the same.

[0043] If the fault types in the fault analysis report set are all the same, the fault types of the same type in the fault analysis report set are recorded as γ, and γ is matched with the fault types in the historical data fault influencing factor set to obtain the union of the historical data fault influencing factor set corresponding to the fault type γ.

[0044] If the fault types in the fault analysis report set are different, then count the number of elements corresponding to each fault type, record the fault type with the largest number of corresponding elements as δ, and delete the set D * For all elements whose fault type is not δ, match δ with the fault type in the historical data fault influencing factor set to obtain the union of the historical data fault influencing factor set corresponding to the fault type γ.

[0045] Step 2004: According to the analysis result of step 2003, the union is brought into step 1003-step 1008 to obtain the corresponding determinant value in the historical evaluation model, which is recorded as Y D* .

[0046] The present invention randomly obtains a data analysis set within any cycle in a data transmission channel, obtains an analysis report set with faults by calculating the similarity between the obtained data analysis set and a fault data analysis report set in a historical database, and further analyzes the obtained fault analysis report set to determine whether the faults in the fault analysis report set are of the same type. Then, by matching the union of the historical data fault influencing factor sets corresponding to the corresponding fault types in the historical data fault influencing factor sets, and matching the evaluation value of the corresponding fault type in combination with the historical fault evaluation model, a data reference is provided for the subsequent construction of the fault warning condition value, and a secondary limitation of the warning signal is performed through different fault impact degrees.

[0047] Furthermore, the method in S3 comprises the following steps:

[0048] Step 3001, obtain the analysis result of step 2004, and convert Y D* As the fault warning condition value,

[0049] If the fault warning condition value A warning signal is issued.

[0050] If the fault warning condition value No warning signal is issued. Preset constants for the database.

[0051] The present invention analyzes the evaluation value corresponding to the fault type existing in a data channel to be monitored within a cycle and compares it with the preset value of the database, and then determines the conditional value of the early warning signal according to the comparison result, providing a data reference for the subsequent adjustment of the computer's data transmission channel interface according to the early warning signal.

[0052] Furthermore, the method in S4 monitors the computer data transmission process in real time, and adjusts the data channel interface corresponding to the data transmission in real time in combination with the early warning signal. When a fault early warning signal appears in one of the data transmission channels, the subsequent data in the corresponding data transmission channel automatically selects the next data channel for transmission until the fault of the current data transmission channel is eliminated and the current data channel is selected again for normal transmission.

[0053] A data channel management system based on big data, the system includes the following modules:

[0054] Historical fault assessment module: The historical fault assessment module obtains computer security diary reports through historical data, extracts fault data information from the security diary reports, and constructs a historical fault assessment model based on the impact of the corresponding faults;

[0055] Fault monitoring and analysis module: The fault monitoring and analysis module is used to obtain the fault information report in any period of any data transmission channel in real time, analyze the elements in the fault information report and the similarities in the fault analysis report set in the historical security diary report, and combine the historical fault evaluation module to obtain the evaluation value corresponding to the monitored data transmission channel;

[0056] Fault warning condition setting module: The fault warning condition setting module is used to determine the warning condition value of the fault in combination with the analysis result of the fault monitoring and analysis module;

[0057] Data transmission channel selection module: The data transmission channel selection module is used to monitor the computer data transmission process in real time, and adjust the data channel interface corresponding to the data transmission in real time in combination with the early warning signal. When a fault early warning signal appears in one of the data transmission channels, the subsequent data in the corresponding data transmission channel automatically selects the next data channel for transmission until the fault of the current data transmission channel is eliminated and the current data channel is selected again for normal transmission.

[0058] Further, the historical fault assessment module includes a data acquisition unit, a data analysis unit and a model building unit;

[0059] The data acquisition unit is used to acquire a computer security diary report through historical data, and extract fault data information in the corresponding security diary report;

[0060] The data analysis unit is used to combine the data obtained by the data acquisition unit to further analyze the factors that cause the fault, and then analyze the impact degree corresponding to different fault factors;

[0061] The model building unit is used to build a historical fault assessment model in combination with the analysis results of the data analysis unit.

[0062] Furthermore, the fault monitoring and analysis module includes a fault similarity analysis unit, a fault type analysis unit and a fault impact assessment unit:

[0063] The fault similarity analysis unit is used to calculate the similarity between the elements in the randomly acquired fault information report and the historical fault assessment model;

[0064] The fault type analysis unit is used to determine the specific fault type of the data transmission channel to be monitored in combination with the analysis result of the fault similarity analysis unit;

[0065] The fault impact assessment unit is used to combine the analysis results of the fault type analysis unit and the fault similarity analysis unit to match the assessment value according to the fault type of the data transmission channel to be monitored.

[0066] Furthermore, the fault warning condition setting module includes a fault warning condition value analysis unit and a warning signal analysis unit:

[0067] The fault warning condition value analysis unit is used to compare the evaluation value of the fault impact evaluation unit with the preset value in the database;

[0068] The warning signal analysis unit is used to determine whether it is necessary to issue a warning signal based on the comparison result of the fault warning condition value analysis unit.

[0069] Furthermore, the data transmission channel selection module includes a data transmission channel monitoring unit, an early warning signal receiving unit and a data transmission channel selection unit:

[0070] The data transmission channel monitoring unit is used to monitor the process of computer data transmission in real time;

[0071] The warning signal receiving unit is used to receive the judgment result of the warning signal analyzing unit in real time;

[0072] The data transmission channel selection unit is used to adjust the data channel interface corresponding to the data transmission in real time in combination with the warning signal. When a fault warning signal appears in one of the data transmission channels, the subsequent data in the corresponding data transmission channel automatically selects the next data channel for transmission until the fault of the current data transmission channel is eliminated and the current data channel is selected again for normal transmission.

[0073] The present invention obtains computer security report information from historical data, analyzes it in combination with the fault data in the security report information, and then obtains the degree of influence of different faults on the data transmission channel, and monitors the fault type corresponding to the data transmission channel in real time. By matching the monitored fault type with the historical fault assessment model, an early warning signal is issued according to the fault influence degree corresponding to the matching result, and the data transmission channel is adjusted according to the early warning signal, thereby improving data transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a flow chart of a data channel management method based on big data of the present invention;

[0075] Figure 2 It is a module schematic diagram of a data channel management system based on big data of the present invention. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] Example 1: Please refer to Figure 1 , in this embodiment:

[0078] A data channel management system and method based on big data is implemented, the method comprising the following steps:

[0079] S1. Query the security diary report of the Ath computer through historical data to obtain a set of fault data analysis reports in the security diary report, and build a historical fault assessment model in combination with the fault data analysis report;

[0080] The method in S1 comprises the following steps:

[0081] Step 1001: record the fault data analysis report set described in S1 as set B = (B1, B2, B3, ..., B n ), where B n Indicates the fault type corresponding to the fault in the nth security diary report, where n represents the total number of fault data analysis report sets in the security diary report of the Ath computer;

[0082] Step 1002: construct a set of fault influencing factors based on the factors that constitute the fault type in the fault analysis report set, and record the set of fault influencing factors corresponding to the rth security diary report as G(B，r) ,

[0083]

[0084] in, Indicates that the fault influencing factor set corresponding to the rth safety diary report constitutes the mth sub-fault influencing factor set of the fault;

[0085] Step 1003: Obtain the mth sub-fault influencing factor set that constitutes the fault influencing factor set corresponding to the rth security diary report in step 1002,

[0086] in

[0087] in represents the pth fault influencing factor in the mth sub-fault influencing factor set that constitutes the fault in the fault influencing factor set corresponding to the rth security diary report, and p represents the total number of elements in the mth sub-fault influencing factor set that constitutes the fault in the fault influencing factor set corresponding to the rth security diary report;

[0088] Step 1004: construct an impact assessment set according to the impact levels corresponding to different sub-fault impact factors, denoted as

[0089] V=(V1, V2, V3, V4, V5), where V1 indicates that the influence degree of the sub-fault influence factor on the data channel is level V, V2 indicates that the influence degree of the sub-fault influence factor on the data channel is level IV, V3 indicates that the influence degree of the sub-fault influence factor on the data channel is level III, V4 indicates that the influence degree of the sub-fault influence factor on the data channel is level II, and V5 indicates that the influence degree of the sub-fault influence factor on the data channel is level I;

[0090] Step 1005: The fault influencing factor set corresponding to the rth security diary report constitutes the mth sub-fault influencing factor set that generates the fault, and the membership degree of each element in the impact degree assessment set is calculated in turn, which is recorded as

[0091] where Q∈[1,5],

[0092] It represents the total number of fault influencing factors with an influence degree of Q in the mth sub-fault influencing factor set that constitutes the fault caused by the fault influencing factor set corresponding to the rth security diary report, Indicates the total number of elements in the mth sub-fault influencing factor set that constitutes the fault generated by the fault influencing factor set corresponding to the rth safety diary report;

[0093] Step 1006: According to the calculation result of step 1005, obtain the evaluation value of the influence degree of each element in the mth sub-fault influencing factor set that constitutes the fault caused by the fault influencing factor set corresponding to the rth security diary report, which is recorded as

[0094]

[0095] Step 1007: Repeat steps 1002 to 1006 to obtain the evaluation membership matrix corresponding to the fault influencing factor set, recorded as

[0096]

[0097] in Indicates that the fault influencing factor set corresponding to the rth security diary report constitutes the mth sub-fault influencing factor of the fault The impact level in the evaluation is the membership degree corresponding to V1,

[0098] in Indicates that the fault influencing factor set corresponding to the rth security diary report constitutes the mth sub-fault influencing factor of the fault The impact level in the evaluation is the membership degree corresponding to V2,

[0099] in Indicates that the fault influencing factor set corresponding to the rth security diary report constitutes the mth sub-fault influencing factor of the fault The medium impact level is assessed as the membership corresponding to V5;

[0100] Step 1008: construct a historical fault assessment model.

[0101]

[0102] Where P B express The corresponding matrix determinant value, ω r Indicates the weight value corresponding to the fault type corresponding to the rth security diary report, and the weight value is queried through a preset form in the database.

[0103] S2. Acquire a set of fault data analysis reports in any period in any data transmission channel in real time, and analyze the evaluation value corresponding to the set of fault data analysis reports in combination with a historical fault evaluation model;

[0104] The method in S2 comprises the following steps:

[0105] Step 2001, setting the computer security diary report analysis period to α;

[0106] Step 2002: randomly obtain a set of data analysis reports in any period of any data transmission channel, and record the set of data analysis reports as set D = (D1, D2, D3, ..., D j ), where D j represents the data type corresponding to the j-th data in the data analysis report set, and j represents the total number of data types in the data analysis report set;

[0107] Step 2003: Calculate the similarity between each set and the fault analysis report set in the historical security diary report, denoted as X.

[0108]

[0109] Where |B∩D| represents the number of elements in the intersection of set B and set D, |B| represents the number of elements in set B, and |D| represents the number of elements in set D.

[0110] If X=0, then there is no fault in the judgment set D.

[0111] If X≠0, it is determined that there is a fault in the set D, and the analysis report set with the fault in the data analysis report set is extracted, which is recorded as the set in Indicates the fault type corresponding to the hth fault in the fault analysis report set, h indicates the total number of fault types in the fault analysis report set, and determines whether the fault types in the fault analysis report set are the same.

[0112] If the fault types in the fault analysis report set are all the same, the fault types of the same type in the fault analysis report set are recorded as γ, and γ is matched with the fault types in the historical data fault influencing factor set to obtain the union of the historical data fault influencing factor set corresponding to the fault type γ.

[0113] If the fault types in the fault analysis report set are different, then count the number of elements corresponding to each fault type, record the fault type with the largest number of corresponding elements as δ, and delete the set D * For all elements whose fault type is not δ, match δ with the fault type in the historical data fault influencing factor set to obtain the union of the historical data fault influencing factor set corresponding to the fault type γ.

[0114] Step 2004: According to the analysis result of step 2003, the union is brought into step 1003-step 1008 to obtain the corresponding determinant value in the historical evaluation model, which is recorded as

[0115] S3, obtaining the analysis results in S2, and further analyzing the average fault occurrence rate to obtain the fault warning condition value;

[0116] The method in S3 comprises the following steps:

[0117] Step 3001, obtain the analysis result of step 2004, and convert Y D* As the fault warning condition value,

[0118] If the fault warning condition value A warning signal is issued.

[0119] If the fault warning condition value No warning signal is issued. Preset constants for the database.

[0120] S4. Monitor the computer data transmission process in real time, and adjust the data channel interface corresponding to the data transmission in real time based on the early warning signal.

[0121] The method in S4 monitors the computer data transmission process in real time, and adjusts the data channel interface corresponding to the data transmission in real time in combination with the early warning signal. When a fault early warning signal appears in one of the data transmission channels, the subsequent data in the corresponding data transmission channel automatically selects the next data channel for transmission until the fault of the current data transmission channel is eliminated and the current data channel is selected again for normal transmission.

[0122] In this embodiment:

[0123] A data channel management system based on big data is disclosed (such as Figure 2 As shown), the system is used to implement the specific solution content of the method.

[0124] Example 2: It is assumed that there are 10 sub-fault influencing factor sets constituting the fault generation in the fault influencing factor set corresponding to the rth security diary report, of which 3 have an impact assessment level of V, 1 has an impact assessment level of IV, 3 have an impact assessment level of III, 2 have an impact assessment level of II, and 1 has an impact assessment level of I.

[0125] Then the calculation result of the membership degree of each element in the fault influencing factor set corresponding to the rth safety diary report and each element in the impact degree assessment set is:

[0126]

[0127] Where L V Indicates the membership degree of the concentrated influence degree of sub-fault influencing factors V1, L IV Indicates the membership degree of the concentrated influence degree of sub-fault influencing factors as V2, L III Indicates the membership degree of the concentrated influence degree of the sub-fault influencing factors as V3, L IIIndicates the membership degree of the concentrated influence degree of the sub-fault influencing factors as V4, L I Indicates the membership degree of the concentrated influence degree of the sub-fault influencing factors as V5,

[0128] The corresponding impact evaluation value can be expressed as

[0129] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0130] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0131] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data channel management method based on big data, characterized in that: The method comprises the following steps: S1. Query the security diary report of the Ath computer through historical data to obtain a set of fault data analysis reports in the security diary report, and build a historical fault assessment model in combination with the fault data analysis report; Step 1001: record the fault data analysis report set described in S1 as set B = (B1, B2, B3, ..., B n ), where B n Indicates the fault type corresponding to the fault in the nth security diary report, where n represents the total number of fault data analysis report sets in the security diary report of the Ath computer; Step 1002: construct a set of fault influencing factors based on the factors that constitute the fault type in the fault analysis report set, and record the set of fault influencing factors corresponding to the rth security diary report as G (B，r) , in, Indicates that the fault influencing factor set corresponding to the rth safety diary report constitutes the mth sub-fault influencing factor set of the fault; Step 1003: Obtain the mth sub-fault influencing factor set that constitutes the fault influencing factor set corresponding to the rth security diary report in step 1002, in in represents the pth fault influencing factor in the mth sub-fault influencing factor set that constitutes the fault in the fault influencing factor set corresponding to the rth security diary report, and p represents the total number of elements in the mth sub-fault influencing factor set that constitutes the fault in the fault influencing factor set corresponding to the rth security diary report; Step 1004: construct an impact assessment set according to the impact levels corresponding to different sub-fault impact factors, denoted as V=(V1, V2, V3, V4, V5), where V1 indicates that the influence degree of the sub-fault influence factor on the data channel is level V, V2 indicates that the influence degree of the sub-fault influence factor on the data channel is level IV, V3 indicates that the influence degree of the sub-fault influence factor on the data channel is level III, V4 indicates that the influence degree of the sub-fault influence factor on the data channel is level II, and V5 indicates that the influence degree of the sub-fault influence factor on the data channel is level I; Step 1005: The fault influencing factor set corresponding to the rth security diary report constitutes the mth sub-fault influencing factor set that generates the fault, and the membership degree of each element in the impact degree assessment set is calculated in turn, which is recorded as where Q∈[1,5], It represents the total number of fault influencing factors with an influence degree of Q in the mth sub-fault influencing factor set that constitutes the fault caused by the fault influencing factor set corresponding to the rth security diary report, Indicates the total number of elements in the mth sub-fault influencing factor set that constitutes the fault generated by the fault influencing factor set corresponding to the rth safety diary report; Step 1006: According to the calculation result of step 1005, obtain the evaluation value of the influence degree of each element in the mth sub-fault influencing factor set that constitutes the fault caused by the fault influencing factor set corresponding to the rth security diary report, which is recorded as Step 1007: Repeat steps 1002 to 1006 to obtain the evaluation membership matrix corresponding to the fault influencing factor set, recorded as in Indicates that the fault influencing factor set corresponding to the rth security diary report constitutes the mth sub-fault influencing factor of the fault The impact level in the evaluation is the membership degree corresponding to V1, in Indicates that the fault influencing factor set corresponding to the rth security diary report constitutes the mth sub-fault influencing factor of the fault The impact level in the evaluation is the membership degree corresponding to V2, in Indicates that the fault influencing factor set corresponding to the rth security diary report constitutes the mth sub-fault influencing factor of the fault The medium impact level is assessed as the membership corresponding to V5; Step 1008: construct a historical fault assessment model. Where P B express The corresponding matrix determinant value, ω r Indicates the weight value corresponding to the fault type corresponding to the rth security diary report, and the weight value is queried through a preset table in the database; S2. Acquire a set of fault data analysis reports in any period in any data transmission channel in real time, and analyze the evaluation value corresponding to the set of fault data analysis reports in combination with a historical fault evaluation model; S3, obtaining the analysis results in S2, and further analyzing the average fault occurrence rate to obtain the fault warning condition value; S4. Monitor the computer data transmission process in real time, and adjust the data channel interface corresponding to the data transmission in real time based on the early warning signal.

2. The data channel management method based on big data according to claim 1 is characterized in that: The method in S2 comprises the following steps: Step 2001, setting the computer security diary report analysis period to α; Step 2002: randomly obtain a set of data analysis reports in any period of any data transmission channel, and record the set of data analysis reports as set D = (D1, D2, D3, ..., D j ), where D j represents the data type corresponding to the j-th data in the data analysis report set, and j represents the total number of data types in the data analysis report set; Step 2003: Calculate the similarity between each set and the fault analysis report set in the historical security diary report, denoted as X. Where |B∩D| represents the number of elements in the intersection of set B and set D, |B| represents the number of elements in set B, and |D| represents the number of elements in set D. If X=0, then there is no fault in the judgment set D. If X≠0, it is determined that there is a fault in the set D, and the analysis report set with the fault in the data analysis report set is extracted, which is recorded as the set in Indicates the fault type corresponding to the hth fault in the fault analysis report set, h indicates the total number of fault types in the fault analysis report set, and determines whether the fault types in the fault analysis report set are the same. If the fault types in the fault analysis report set are all the same, the fault types of the same type in the fault analysis report set are recorded as γ, and γ is matched with the fault types in the historical data fault influencing factor set to obtain the union of the historical data fault influencing factor set corresponding to the fault type γ. If the fault types in the fault analysis report set are different, then count the number of elements corresponding to each fault type, record the fault type with the largest number of corresponding elements as δ, and delete the set D * For all elements whose fault type is not δ, match δ with the fault type in the historical data fault influencing factor set to obtain the union of the historical data fault influencing factor set corresponding to the fault type γ. Step 2004: According to the analysis result of step 2003, the union is brought into step 1003-step 1008 to obtain the corresponding determinant value in the historical evaluation model, which is recorded as 3. The data channel management method based on big data according to claim 2 is characterized in that: The method in S3 comprises the following steps: Step 3001, obtain the analysis result of step 2004, As the fault warning condition value, If the fault warning condition value A warning signal is issued. If the fault warning condition value No warning signal is issued. Preset constants for the database.

4. The data channel management method based on big data according to claim 3 is characterized in that: The method in S4 monitors the computer data transmission process in real time, and adjusts the data channel interface corresponding to the data transmission in real time in combination with the early warning signal. When a fault early warning signal appears in one of the data transmission channels, the subsequent data in the corresponding data transmission channel automatically selects the next data channel for transmission until the fault of the current data transmission channel is eliminated and the current data channel is selected again for normal transmission.

5. A data channel management system based on big data using a data channel management method based on big data as described in any one of claims 1 to 4, characterized in that: The system includes the following modules: Historical fault assessment module: The historical fault assessment module obtains computer security diary reports through historical data, extracts fault data information from the security diary reports, and constructs a historical fault assessment model based on the impact of the corresponding faults; Fault monitoring and analysis module: The fault monitoring and analysis module is used to obtain the fault information report in any period of any data transmission channel in real time, analyze the elements in the fault information report and the similarities in the fault analysis report set in the historical security diary report, and combine the historical fault evaluation module to obtain the evaluation value corresponding to the monitored data transmission channel; Fault warning condition setting module: The fault warning condition setting module is used to determine the warning condition value of the fault in combination with the analysis result of the fault monitoring and analysis module; Data transmission channel selection module: The data transmission channel selection module is used to monitor the computer data transmission process in real time, and adjust the data channel interface corresponding to the data transmission in real time in combination with the early warning signal. When a fault early warning signal appears in one of the data transmission channels, the subsequent data in the corresponding data transmission channel automatically selects the next data channel for transmission until the fault of the current data transmission channel is eliminated and the current data channel is selected again for normal transmission.

6. A data channel management system based on big data according to claim 5, characterized in that: The historical fault assessment module includes a data acquisition unit, a data analysis unit and a model building unit; The data acquisition unit is used to acquire a computer security diary report through historical data, and extract fault data information in the corresponding security diary report; The data analysis unit is used to combine the data obtained by the data acquisition unit to further analyze the factors that cause the fault, and then analyze the impact degree corresponding to different fault factors; The model building unit is used to build a historical fault assessment model in combination with the analysis results of the data analysis unit.

7. A data channel management system based on big data according to claim 6, characterized in that: The fault monitoring and analysis module includes a fault similarity analysis unit, a fault type analysis unit and a fault impact assessment unit: The fault similarity analysis unit is used to calculate the similarity between the elements in the randomly acquired fault information report and the historical fault assessment model; The fault type analysis unit is used to determine the specific fault type of the data transmission channel to be monitored in combination with the analysis result of the fault similarity analysis unit; The fault impact assessment unit is used to combine the analysis results of the fault type analysis unit and the fault similarity analysis unit to match the assessment value according to the fault type of the data transmission channel to be monitored.

8. The data channel management system based on big data according to claim 6, characterized in that: The fault warning condition setting module includes a fault warning condition value analysis unit and a warning signal analysis unit: The fault warning condition value analysis unit is used to compare the evaluation value of the fault impact evaluation unit with the preset value in the database; The warning signal analysis unit is used to determine whether it is necessary to issue a warning signal based on the comparison result of the fault warning condition value analysis unit.

9. The data channel management system based on big data according to claim 6, characterized in that: The data transmission channel selection module includes a data transmission channel monitoring unit, an early warning signal receiving unit and a data transmission channel selection unit: The data transmission channel monitoring unit is used to monitor the process of computer data transmission in real time; The warning signal receiving unit is used to receive the judgment result of the warning signal analyzing unit in real time; The data transmission channel selection unit is used to adjust the data channel interface corresponding to the data transmission in real time in combination with the warning signal. When a fault warning signal appears in one of the data transmission channels, the subsequent data in the corresponding data transmission channel automatically selects the next data channel for transmission until the fault of the current data transmission channel is eliminated and the current data channel is selected again for normal transmission.

Images