CN116432469A - Data channel management system and method based on big data - Google Patents
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Abstract
The invention relates to the technical field of computers, in particular to a data channel management system and a data channel management method based on big data.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a data channel management system and method based on big data.
Background
Through the continuous development of communication technology in recent years, data transmission is increasingly paid attention to in the industry, and the speed of data transmission directly influences the data processing of a client to a certain extent, so that the user experience of the client is influenced, and network transmission refers to a process of carrying out communication according to a network transmission protocol by using a series of circuits through adjustment and change of circuits. Wherein the network transmission needs a medium, namely a physical path between a sender and a receiver in the network, which has a certain influence on the data communication of the network, and common transmission mediums are as follows: twisted pair, coaxial cable, optical fiber, wireless transmission medium, network protocol, i.e. some specifications of transmission and management information in the network, and most important in network transmission is real-time monitoring in data transmission to ensure stable and continuous network transmission.
However, due to the objective existence of the network instability factor, when the acceleration node server is unstable in data transmission in the network, the acceleration behavior of the server for data transmission is almost invalid, so that abnormal conditions of data occur in the data channel transmission process, and therefore, how to reasonably manage the data channel and early warn for the abnormal conditions of data transmission is one of the problems to be solved urgently at present.
Disclosure of Invention
The invention aims to provide an operation behavior early warning system and method based on a computer, which are used for solving the problems in the background technology, and the invention provides the following technical scheme:
a data channel management method based on big data, the method comprising the steps of:
s1, inquiring a safety diary report of an A-th computer through historical data to obtain a fault data analysis report set in the safety diary report, and constructing a historical fault evaluation model by combining the fault data analysis report;
s2, acquiring a fault data analysis report set in any period in any data transmission channel in real time, and analyzing an evaluation value corresponding to the fault data analysis report set by combining a historical fault evaluation model;
s3, acquiring an analysis result in the S2, and further analyzing the average occurrence rate of the faults to obtain a fault early warning condition value;
and S4, monitoring the data transmission process of the computer in real time, and adjusting a data channel interface corresponding to the data transmission in real time by combining the early warning signal.
Further, the method in S1 includes the following steps:
step 1001, recording the failure data analysis report set described in S1 as a set b= (B) 1 ，B 2 ，B 3 ，...，B n ) Wherein B is n The method comprises the steps of representing the fault type corresponding to the fault in an nth safety diary report, wherein n represents the total number of fault data analysis reports in an A-th computer safety diary report;
step 1002, constructing a fault influence factor set according to factors forming fault type generation in the fault analysis report set, and recording a fault influence factor set corresponding to the r-th safety diary report as G (B，r) ，
Wherein,,
representing an mth sub-fault influence factor set generated by faults formed by the fault influence factor set corresponding to the r security diary report;
step 1003, obtaining an mth sub-fault influence factor set generated by faults from the fault influence factor set corresponding to the r-th security diary report in step 1002,
wherein the method comprises the steps of
Wherein the method comprises the steps of
Representing the p-th fault influence factor in the m-th sub-fault influence factor set generated by the fault and formed by the fault influence factor set corresponding to the r-th safety diary report, wherein p represents the m-th sub-event generated by the fault and formed by the fault influence factor set corresponding to the r-th safety diary reportThe total number of elements in the barrier influencing factor set;
step 1004, constructing an influence degree evaluation set according to the influence degrees corresponding to different sub-fault influence factors, and recording as
V＝(V 1 ，V 2 ，V 3 ，V 4 ，V 5 ) Wherein V is 1 The influence degree of the sub-fault influence factors on the data channel is shown as V level and V 2 The influence degree of the sub-fault influence factors on the data channel is IV level and V 3 Indicating the influence degree of the sub-fault influence factors on the data channel as III level and V 4 The influence degree of the sub-fault influence factors on the data channel is represented as II level and V level 5 The influence degree of the sub-fault influence factors on the data channel is represented as I level, wherein the influence degree corresponding to the different sub-fault influence factors can be queried through a database preset form;
step 1005, sequentially performing membership calculation on each element in the influence degree evaluation set by each element in the mth sub-fault influence factor set generated by fault formation in the fault influence factor set corresponding to the mth security diary report, and recording as
wherein the fact that each element in the mth sub-fault influence factor set generated by the fault formed in the mth fault influence factor set corresponding to the r security diary report sequentially carries out membership calculation on each element in the influence degree evaluation set is
Step 1006, according to the calculation result of step 1005, obtaining an evaluation value of the corresponding influence degree of each element in the mth sub-fault influence factor set generated by the fault formed by the fault influence factor set corresponding to the mth security diary report, and recording the evaluation value as
Step 1007, repeating step 1002-step 1006 to obtain an evaluation membership matrix corresponding to the fault influence factor set, and recording as
Wherein the method comprises the steps of
Representing the mth sub-fault influence factor (M) generated by fault and formed by the corresponding fault influence factor set of the r-th security diary report>
The degree of influence in (c) was evaluated as V 1 The corresponding degree of membership is determined,
wherein the method comprises the steps of
Representing the mth sub-fault influence factor (M) generated by fault and formed by the corresponding fault influence factor set of the r-th security diary report>
The degree of influence in (c) was evaluated as V 2 The corresponding degree of membership is determined,
wherein the method comprises the steps of
Representing the mth sub-fault influence factor (M) generated by fault and formed by the corresponding fault influence factor set of the r-th security diary report>
The degree of influence in (c) was evaluated as V 5 Corresponding membership degree;
step 1008, constructing a historical fault assessment model,
wherein P is B Representation of
Corresponding matrix determinant value, omega r Representing a weight value corresponding to a fault type corresponding to the r-th security diary report, wherein the weight value is queried through a database preset form, and each fault type in the database preset form has a unique weight value
According to the method, a corresponding computer security diary report in a region to be monitored is obtained through historical data, fault data information in the security diary report is extracted, a fault data analysis report set is constructed, a fault influence factor set is constructed according to factors generated by forming faults in the extracted fault data analysis report set, an evaluation set is constructed according to influence degrees corresponding to different fault influence factors, fuzzy mapping of the fault influence factor set to the influence degree evaluation set is constructed, a historical fault evaluation model is constructed based on the fuzzy mapping, and data reference is provided for follow-up construction of predicted fault condition values and real-time adjustment of a data transmission channel.
Further, the method in S2 includes the following steps:
step 2001, setting the analysis period of the computer security diary report as alpha;
step 2002, randomly acquiring a data analysis report set in any period in any data transmission channel, and recording the data analysis report set as a set D= (D) 1 ，D 2 ，D 3 ，...，D j ) Wherein D is j Representing the data type corresponding to the j-th data in the data analysis report set, wherein j represents the total number of the 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.u.D| represents the number of elements in set B that intersect set D, |B| represents the number of elements in set B, |D| represents the number of elements in set D,
if x=0, then it is determined that there is no fault in set D,
if X is not equal to 0, judging that the faults exist in the set D, extracting an analysis report set with the faults in the data analysis report set, and recording the analysis report set as a set
Wherein->
Indicating the fault type corresponding to the h th fault in the analysis report set of faults, h indicating the total number of the fault types in the analysis report set of faults, judging whether the fault types in the analysis report set of faults are the same,
if the fault types in the fault analysis report set are the same, the fault types of the same type in the fault analysis report set are marked as gamma, the gamma is matched with the fault types in the historical data fault influence factor set to obtain a union of the historical data fault influence factor set corresponding to the gamma,
if the fault types in the fault analysis report set are different, counting the number of elements corresponding to each fault type, marking the fault type with the largest number of corresponding elements as delta, and deleting the set D * Matching delta with the fault types in the historical data fault influence factor sets to obtain a union of the historical data fault influence factor sets corresponding to gamma in the fault types,
step 2004, according to the analysis result of step 2003, the union is carried into steps 1003-1008 to obtain corresponding determinant values in the history evaluation model, and the determinant values are marked as Y D* 。
According to the invention, a data analysis set in any period in a data transmission channel is randomly acquired, the similarity degree of the acquired data analysis set and a fault data analysis report set in a historical database is calculated to obtain a fault analysis report set, the obtained fault analysis report set is further analyzed to judge whether faults in the fault analysis report set are of the same type, and further, the union of historical data fault influence factor sets corresponding to fault types in the historical data fault influence factor sets is matched, the evaluation value of the corresponding fault type is matched by combining with a historical fault evaluation model, data reference is provided for the subsequent construction of fault early warning condition values, and secondary limitation of early warning signals is carried out by different fault influence degrees.
Further, the method in S3 includes the following steps:
step 3001, obtain the analysis result of step 2004, and convert Y D* As a value of the fault pre-warning condition,
if the fault early warning condition value
An early warning signal is sent out,
if the fault early warning condition value
No warning signal is sent out, wherein +.>
Constants are preset for the database.
According to the invention, the evaluation value corresponding to the fault type existing in one period in the data channel to be monitored is analyzed and compared with the preset value of the database, and then the condition value of the early warning signal is judged according to the comparison result, so that data reference is provided for the data transmission channel interface of the subsequent computer according to the early warning signal.
Further, the method in S4 adjusts the data channel interface corresponding to the data transmission in real time by monitoring the data transmission process of the computer in real time and combining with the early warning signal, when one of the data transmission channels fails to have the early warning signal, the subsequent data in the corresponding data transmission channel automatically selects the subsequent data channel for transmission until the current data channel is selected again for normal transmission after the failure of the current data transmission channel is eliminated.
A big data based data channel management system, the system comprising the following modules:
historical fault assessment module: the historical fault evaluation module acquires a computer security diary report through historical data, extracts fault data information in the security diary report, and builds a historical fault evaluation model by combining the influence degree of corresponding faults;
and the fault monitoring and analyzing module is used for: the fault monitoring analysis module is used for acquiring a fault information report in any period in any one data transmission channel in real time, and obtaining an evaluation value corresponding to the monitored data transmission channel by combining the historical fault evaluation module through analyzing similar conditions of elements in the fault information report and a fault analysis report set in the historical safety diary report;
the fault early warning condition setting module: the fault early-warning condition setting module is used for judging the early-warning condition value of the fault by combining the analysis result of the fault monitoring and analyzing module;
a data transmission channel selection module: the data transmission channel selection module is used for monitoring the data transmission process of the computer in real time, adjusting the data channel interface corresponding to the data transmission in real time by combining with the early warning signal, and when one of the data transmission channels has the fault early warning signal, automatically selecting the next data channel for transmission according to the subsequent data in the corresponding data transmission channel until the current data channel is selected for normal transmission again after the fault of the current data transmission channel is eliminated.
Further, the historical fault evaluation module comprises a data acquisition unit, a data analysis unit and a model construction unit;
the data acquisition unit is used for acquiring a computer security diary report through historical data and extracting fault data information in the corresponding security diary report;
the data analysis unit is used for further analyzing and obtaining factors forming faults by combining the data obtained by the data obtaining unit, and further analyzing the influence degrees corresponding to different fault factors;
the model construction unit is used for constructing a historical fault evaluation model by combining the analysis result of the data analysis unit.
Further, the fault monitoring and analyzing module comprises a fault similarity analyzing unit, a fault type analyzing unit and a fault influence evaluating unit:
the fault similarity analysis unit is used for calculating the similarity degree between elements in the randomly acquired fault information report and the historical fault evaluation model;
the fault type analysis unit is used for determining a specific fault type of the data transmission channel to be monitored by combining the analysis result of the fault similarity analysis unit;
the fault influence evaluation unit is used for combining analysis results in the fault type analysis unit and the fault similarity analysis unit and performing matching evaluation values according to the fault type of the data transmission channel to be monitored.
Further, the fault early warning condition setting module comprises a fault early warning condition value analysis unit and an early warning signal analysis unit:
the fault early warning condition value analysis unit is used for comparing the evaluation value of the fault influence evaluation unit with a database preset value;
the early warning signal analysis unit is used for combining the comparison result of the fault early warning condition value analysis unit, and judging whether an early warning signal needs to be sent or not according to the comparison result.
Further, 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 for monitoring the process of computer data transmission in real time;
the early warning signal receiving unit is used for receiving the judgment result of the early warning signal analysis unit in real time;
the data transmission channel selection unit is used for adjusting the data channel interface corresponding to the data transmission in real time in combination with the early warning signal, when one of the data transmission channels fails to have the early warning signal, the subsequent data in the corresponding data transmission channel automatically selects the subsequent data channel for transmission until the current data transmission channel fails to be eliminated, and then the current data channel is selected again for normal transmission.
According to the invention, the computer safety report information in the historical data is obtained, the fault data in the safety report information is combined for analysis, so that the influence degree conditions of different faults on the data transmission channel are obtained, the fault type corresponding to the data transmission channel is monitored in real time, the monitored fault type is matched with the historical fault evaluation model, an early warning signal is sent out according to the fault influence degree corresponding to the matching result, and the data transmission channel is adjusted according to the early warning signal, so that the data transmission efficiency is improved.
Drawings
FIG. 1 is a flow chart of a data channel management method based on big data according to the present invention;
FIG. 2 is a block diagram of a data channel management system based on big data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: referring to fig. 1, in this embodiment:
the data channel management system and method based on big data are realized, and the method comprises the following steps:
s1, inquiring a safety diary report of an A-th computer through historical data to obtain a fault data analysis report set in the safety diary report, and constructing a historical fault evaluation model by combining the fault data analysis report;
the method in S1 comprises the following steps:
step 1001, recording the failure data analysis report set described in S1 as a set b= (B) 1 ，B 2 ，B 3 ，...，B n ) Wherein B is n The method comprises the steps of representing the fault type corresponding to the fault in an nth safety diary report, wherein n represents the total number of fault data analysis reports in an A-th computer safety diary report;
step 1002, constructing a fault influence factor set according to factors forming fault type generation in the fault analysis report set, and recording a fault influence factor set corresponding to the r-th safety diary report as G (B，r) ，
Wherein,,
representing an mth sub-fault influence factor set generated by faults formed by the fault influence factor set corresponding to the r security diary report;
step 1003, obtaining an mth sub-fault influence factor set generated by faults from the fault influence factor set corresponding to the r-th security diary report in step 1002,
wherein the method comprises the steps of
Wherein the method comprises the steps of
The p-th fault influence factor in the m-th sub-fault influence factor set generated by the fault is represented by the fault influence factor set corresponding to the r-th safety diary report, and p represents the total number of elements in the m-th sub-fault influence factor set generated by the fault is represented by the fault influence factor set corresponding to the r-th safety diary report;
step 1004, constructing an influence degree evaluation set according to the influence degrees corresponding to different sub-fault influence factors, and recording as
V＝(V 1 ，V 2 ，V 3 ，V 4 ，V 5 ) Wherein V is 1 The influence degree of the sub-fault influence factors on the data channel is shown as V level and V 2 The influence degree of the sub-fault influence factors on the data channel is IV level and V 3 Indicating the influence degree of the sub-fault influence factors on the data channel as III level and V 4 The influence degree of the sub-fault influence factors on the data channel is represented as II level and V level 5 The influence degree of the sub-fault influence factors on the data channel is represented as I level;
step 1005, sequentially performing membership calculation on each element in the influence degree evaluation set by each element in the mth sub-fault influence factor set generated by fault formation in the fault influence factor set corresponding to the mth security diary report, and recording as
step 1006, according to the calculation result of step 1005, obtaining an evaluation value of the corresponding influence degree of each element in the mth sub-fault influence factor set generated by the fault formed by the fault influence factor set corresponding to the mth security diary report, and recording the evaluation value as
Step 1007, repeating step 1002-step 1006 to obtain an evaluation membership matrix corresponding to the fault influence factor set, and recording as
Wherein the method comprises the steps of
Representing the mth sub-fault influence factor (M) generated by fault and formed by the corresponding fault influence factor set of the r-th security diary report>
The degree of influence in (c) was evaluated as V 1 The corresponding degree of membership is determined,
wherein the method comprises the steps of
Representing the corresponding fault influence factor set of the r-th safety diary reportM < th > sub-fault influencing factors constituting fault generation>
The degree of influence in (c) was evaluated as V 2 The corresponding degree of membership is determined,
wherein the method comprises the steps of
Representing the mth sub-fault influence factor (M) generated by fault and formed by the corresponding fault influence factor set of the r-th security diary report>
The degree of influence in (c) was evaluated as V 5 Corresponding membership degree;
step 1008, constructing a historical fault assessment model,
wherein P is B Representation of
Corresponding matrix determinant value, omega r And representing a weight value corresponding to the fault type corresponding to the r-th security diary report, wherein the weight value is queried through a database preset form.
S2, acquiring a fault data analysis report set in any period in any data transmission channel in real time, and analyzing an evaluation value corresponding to the fault data analysis report set by combining a historical fault evaluation model;
the method in S2 comprises the steps of:
step 2001, setting the analysis period of the computer security diary report as alpha;
step 2002, randomly acquiring a data analysis report set in any period in any data transmission channel, and recording the data analysis report set as a set D= (D) 1 ，D 2 ，D 3 ，…，D j ) Wherein D is j Representing the numberJ 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.u.D| represents the number of elements in set B that intersect set D, |B| represents the number of elements in set B, |D| represents the number of elements in set D,
if x=0, then it is determined that there is no fault in set D,
if X is not equal to 0, judging that the faults exist in the set D, extracting an analysis report set with the faults in the data analysis report set, and recording the analysis report set as a set
Wherein->
Indicating the fault type corresponding to the h th fault in the analysis report set of faults, h indicating the total number of the fault types in the analysis report set of faults, judging whether the fault types in the analysis report set of faults are the same,
if the fault types in the fault analysis report set are the same, the fault types of the same type in the fault analysis report set are marked as gamma, the gamma is matched with the fault types in the historical data fault influence factor set to obtain a union of the historical data fault influence factor set corresponding to the gamma,
if the fault types in the fault analysis report set are different, counting the number of elements corresponding to each fault type, marking the fault type with the largest number of corresponding elements as delta, and deleting the set D * Matching delta with the fault types in the historical data fault influence factor sets to obtain a union of the historical data fault influence factor sets corresponding to gamma in the fault types,
step 2004, according to the analysis result of step 2003, the union is carried into steps 1003-1008 to obtain corresponding determinant values in the history evaluation model, and the determinant values are recorded as
S3, acquiring an analysis result in the S2, and further analyzing the average occurrence rate of the faults to obtain a fault early warning condition value;
the method in S3 comprises the following steps:
step 3001, obtain the analysis result of step 2004, and convert Y D* As a value of the fault pre-warning condition,
if the fault early warning condition value
An early warning signal is sent out,
if the fault early warning condition value
No warning signal is sent out, wherein +.>
Constants are preset for the database.
And S4, monitoring the data transmission process of the computer in real time, and adjusting a data channel interface corresponding to the data transmission in real time by combining the early warning signal.
And the method in S4 adjusts the data channel interface corresponding to the data transmission in real time by combining the early warning signal through the real-time monitoring computer data transmission process, and when one data transmission channel fails to have the early warning signal, the subsequent data in the corresponding data transmission channel automatically selects the subsequent data channel for transmission until the current data transmission channel fails to be eliminated, and then the current data channel is selected again for normal transmission.
In this embodiment:
a data channel management system based on big data (as shown in fig. 2) is disclosed, which is used to implement the specific scheme content of the method.
Example 2: setting 10 sub-fault influence factor sets generated by faults in the fault influence factor set corresponding to the r-th safety diary report, wherein 3 influence evaluation grades are V grade, 1 influence evaluation grade is IV grade, 3 influence evaluation grades are III grade, 2 influence evaluation grades are II grade, 1 influence evaluation grade is I grade,
the r-th security diary reports that the corresponding fault influence factors in the set form each element in each sub-fault influence factor set generated by faults, and the membership degree calculation result of each element in the influence degree evaluation set is as follows:
wherein L is V Indicating the concentrated influence degree of the sub-fault influence factors as V 1 Membership degree of L IV Indicating the concentrated influence degree of the sub-fault influence factors as V 2 Membership degree of L III Indicating the concentrated influence degree of the sub-fault influence factors as V 3 Membership degree of L II Indicating the concentrated influence degree of the sub-fault influence factors as V 4 Membership degree of L I Indicating the concentrated influence degree of the sub-fault influence factors as V 5 Is used for the degree of membership of the group (a),
the evaluation value of the corresponding influence degree can be expressed as
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A data channel management method based on big data, the method comprising the steps of:
s1, inquiring a safety diary report of an A-th computer through historical data to obtain a fault data analysis report set in the safety diary report, and constructing a historical fault evaluation model by combining the fault data analysis report;
s2, acquiring a fault data analysis report set in any period in any data transmission channel in real time, and analyzing an evaluation value corresponding to the fault data analysis report set by combining a historical fault evaluation model;
s3, acquiring an analysis result in the S2, and further analyzing the average occurrence rate of the faults to obtain a fault early warning condition value;
and S4, monitoring the data transmission process of the computer in real time, and adjusting a data channel interface corresponding to the data transmission in real time by combining the early warning signal.
2. The method for managing a data channel based on big data according to claim 1, wherein the method in S1 comprises the steps of:
step 1001, recording the failure data analysis report set described in S1 as a set b= (B) 1 ，B 2 ，B 3 ，...，B n ) Wherein B is n The method comprises the steps of representing the fault type corresponding to the fault in an nth safety diary report, wherein n represents the total number of fault data analysis reports in an A-th computer safety diary report;
step 1002, constructing a fault influence factor set according to factors forming fault type generation in the fault analysis report set, and recording a fault influence factor set corresponding to the r-th safety diary report as G (B，r) ，
Wherein,,
representing an mth sub-fault influence factor set generated by faults formed by the fault influence factor set corresponding to the r security diary report;
step 1003, obtaining an mth sub-fault influence factor set generated by faults from the fault influence factor set corresponding to the r-th security diary report in step 1002,
wherein the method comprises the steps of
Wherein the method comprises the steps of
Representing the corresponding fault influencing factors of the r-th safety diary reportThe p-th fault influence factor in the m-th sub-fault influence factor set which is generated by the concentrated formation fault, wherein p represents the total number of elements in the m-th sub-fault influence factor set which is generated by the concentrated formation fault of the fault influence factor corresponding to the r-th security diary report;
step 1004, constructing an influence degree evaluation set according to the influence degrees corresponding to different sub-fault influence factors, and recording as
V＝(V 1 ，V 2 ，V 3 ，V 4 ，V 5 ) Wherein V is 1 The influence degree of the sub-fault influence factors on the data channel is shown as V level and V 2 The influence degree of the sub-fault influence factors on the data channel is IV level and V 3 Indicating the influence degree of the sub-fault influence factors on the data channel as III level and V 4 The influence degree of the sub-fault influence factors on the data channel is represented as II level and V level 5 The influence degree of the sub-fault influence factors on the data channel is represented as I level;
step 1005, sequentially performing membership calculation on each element in the influence degree evaluation set by each element in the mth sub-fault influence factor set generated by fault formation in the fault influence factor set corresponding to the mth security diary report, and recording as
step 1006, according to the calculation result of step 1005, obtaining an evaluation value of the corresponding influence degree of each element in the mth sub-fault influence factor set generated by the fault formed by the fault influence factor set corresponding to the mth security diary report, and recording the evaluation value as
Step 1007, repeating step 1002-step 1006 to obtain an evaluation membership matrix corresponding to the fault influence factor set, and recording as
Wherein the method comprises the steps of
Representing the mth sub-fault influence factor (M) generated by fault and formed by the corresponding fault influence factor set of the r-th security diary report>
The degree of influence in (c) was evaluated as V 1 The corresponding degree of membership is determined,
wherein the method comprises the steps of
Representing the mth sub-fault influence factor (M) generated by fault and formed by the corresponding fault influence factor set of the r-th security diary report>
The degree of influence in (c) was evaluated as V 2 Corresponding toIs used for the degree of membership of the group (a),
wherein the method comprises the steps of
Representing the mth sub-fault influence factor (M) generated by fault and formed by the corresponding fault influence factor set of the r-th security diary report>
The degree of influence in (c) was evaluated as V 5 Corresponding membership degree;
step 1008, constructing a historical fault assessment model,
wherein P is B Representation of
Corresponding matrix determinant value, omega r And representing a weight value corresponding to the fault type corresponding to the r-th security diary report, wherein the weight value is queried through a database preset form.
3. The data channel management method based on big data according to claim 2, wherein the method in S2 comprises the steps of:
step 2001, setting the analysis period of the computer security diary report as alpha;
step 2002, randomly acquiring a data analysis report set in any period in any data transmission channel, and recording the data analysis report set as a set D= (D) 1 ，D 2 ，D 3 ，...，D j ) Wherein D is j Representing the data type corresponding to the j-th data in the data analysis report set, wherein j represents the total number of the 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.u.D| represents the number of elements in set B that intersect set D, |B| represents the number of elements in set B, |D| represents the number of elements in set D,
if x=0, then it is determined that there is no fault in set D,
if X is not equal to 0, judging that the faults exist in the set D, extracting an analysis report set with the faults in the data analysis report set, and recording the analysis report set as a set
Wherein->
Indicating the fault type corresponding to the h th fault in the analysis report set of faults, h indicating the total number of the fault types in the analysis report set of faults, judging whether the fault types in the analysis report set of faults are the same,
if the fault types in the fault analysis report set are the same, the fault types of the same type in the fault analysis report set are marked as gamma, the gamma is matched with the fault types in the historical data fault influence factor set to obtain a union of the historical data fault influence factor set corresponding to the gamma,
if the fault types in the fault analysis report set are different, counting the number of elements corresponding to each fault type, marking the fault type with the largest number of corresponding elements as delta, and deleting the set D * Matching delta with the fault types in the historical data fault influence factor sets to obtain a union of the historical data fault influence factor sets corresponding to gamma in the fault types,
step 2004, according to the analysis result of step 2003, the union is carried into steps 1003-1008 to obtain corresponding determinant values in the history evaluation model, and the determinant values are marked as Y D* 。
4. A data channel management method based on big data according to claim 3, wherein the method in S3 comprises the steps of:
step 3001, obtain the analysis result of step 2004, and convert Y D* As a value of the fault pre-warning condition,
if the fault early warning condition value
An early warning signal is sent out,
if the fault early warning condition value
No warning signal is sent out, wherein +.>
Constants are preset for the database.
5. The method for managing data channels based on big data according to claim 4, wherein the method in S4 adjusts the data channel interface corresponding to the data transmission in real time by monitoring the data transmission process of the computer in real time and combining with the early warning signal, when one of the data transmission channels fails to have the early warning signal, the subsequent data in the corresponding data transmission channel automatically selects the subsequent data channel for transmission until the current data channel is selected again for normal transmission after the failure of the current data transmission channel is eliminated.
6. A big data based data channel management system, the system comprising the following modules:
historical fault assessment module: the historical fault evaluation module acquires a computer security diary report through historical data, extracts fault data information in the security diary report, and builds a historical fault evaluation model by combining the influence degree of corresponding faults;
and the fault monitoring and analyzing module is used for: the fault monitoring analysis module is used for acquiring a fault information report in any period in any one data transmission channel in real time, and obtaining an evaluation value corresponding to the monitored data transmission channel by combining the historical fault evaluation module through analyzing similar conditions of elements in the fault information report and a fault analysis report set in the historical safety diary report;
the fault early warning condition setting module: the fault early-warning condition setting module is used for judging the early-warning condition value of the fault by combining the analysis result of the fault monitoring and analyzing module;
a data transmission channel selection module: the data transmission channel selection module is used for monitoring the data transmission process of the computer in real time, adjusting the data channel interface corresponding to the data transmission in real time by combining with the early warning signal, and when one of the data transmission channels has the fault early warning signal, automatically selecting the next data channel for transmission according to the subsequent data in the corresponding data transmission channel until the current data channel is selected for normal transmission again after the fault of the current data transmission channel is eliminated.
7. The big data based data channel management system of claim 6, wherein the historical failure evaluation module comprises a data acquisition unit, a data analysis unit, and a model construction unit;
the data acquisition unit is used for acquiring a computer security diary report through historical data and extracting fault data information in the corresponding security diary report;
the data analysis unit is used for further analyzing and obtaining factors forming faults by combining the data obtained by the data obtaining unit, and further analyzing the influence degrees corresponding to different fault factors;
the model construction unit is used for constructing a historical fault evaluation model by combining the analysis result of the data analysis unit.
8. The big data based data channel management system of claim 7, wherein the fault monitoring analysis module comprises a fault similarity analysis unit, a fault type analysis unit, and a fault impact assessment unit:
the fault similarity analysis unit is used for calculating the similarity degree between elements in the randomly acquired fault information report and the historical fault evaluation model;
the fault type analysis unit is used for determining a specific fault type of the data transmission channel to be monitored by combining the analysis result of the fault similarity analysis unit;
the fault influence evaluation unit is used for combining analysis results in the fault type analysis unit and the fault similarity analysis unit and performing matching evaluation values according to the fault type of the data transmission channel to be monitored.
9. The data channel management system based on big data according to claim 7, wherein the fault early warning condition setting module comprises a fault early warning condition value analysis unit and an early warning signal analysis unit:
the fault early warning condition value analysis unit is used for comparing the evaluation value of the fault influence evaluation unit with a database preset value;
the early warning signal analysis unit is used for combining the comparison result of the fault early warning condition value analysis unit, and judging whether an early warning signal needs to be sent or not according to the comparison result.
10. The big data based data channel management system of claim 7, wherein the data transmission channel selection module comprises 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 for monitoring the process of computer data transmission in real time;
the early warning signal receiving unit is used for receiving the judgment result of the early warning signal analysis unit in real time;
the data transmission channel selection unit is used for adjusting the data channel interface corresponding to the data transmission in real time in combination with the early warning signal, when one of the data transmission channels fails to have the early warning signal, the subsequent data in the corresponding data transmission channel automatically selects the subsequent data channel for transmission until the current data transmission channel fails to be eliminated, and then the current data channel is selected again for normal transmission.
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