CN113742194A - Block chain system environment three-dimensional scoring method based on analytic hierarchy process - Google Patents
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Abstract
The invention discloses a block chain system environment three-dimensional scoring method based on an analytic hierarchy process. The three dimensions of hardware, software and an operating system of a block chain system environment are monitored, and the corresponding score is solved for the monitoring result by an analytic hierarchy process. The method comprises the following steps: s1, acquiring monitoring index information of each item of the block chain system environment, and dividing the monitoring index information into a variable index and a non-variable index. S2, carrying out hash storage on the unchanged indexes for comparison of subsequent results; and for the variation index, calculating a weight coefficient corresponding to each parameter index by an analytic hierarchy process. And S3, performing hash comparison on the index information and calculating the score by using an analytic hierarchy process. The invention has the beneficial effects that the invention provides the block chain system environment three-dimensional scoring method based on the analytic hierarchy process, and aims to monitor the credible condition of the block chain system environment in real time in a quantitative index mode.
Description
Technical Field
The invention relates to a method for scoring a blockchain system environment, in particular to a three-dimensional scoring method for the blockchain system environment based on an analytic hierarchy process.
Background
The blockchain is essentially a distributed and multi-centralized chain type ledger system, the basic environment of operation of the blockchain comprises hardware, software and an operating system, and the reliability and health condition of the basic environment determine the safety and stability of the whole blockchain system. In the operation process of the block chain system, indexes such as the health degree and the credibility of the block chain link point environment change all the time under the influence of various factors in the operation process of the system, so that various non-quantitative factors need to be considered when necessary evaluation is carried out on the health degree and the credibility of the block chain link point. It is a great challenge to reasonably and quantitatively analyze the credibility and health degree of the blockchain system environment and visually represent the credibility and health degree in a numerical form.
Disclosure of Invention
The invention aims to provide a block chain system environment three-dimensional scoring method based on an analytic hierarchy process. And evaluating the credibility of the block chain node by three dimensions of hardware, software and an operating system. By monitoring the factors influencing the three dimensions, the obtained monitoring values are analyzed in an analytic hierarchy process mode, and corresponding weight values are divided to solve the grade of the system state and performance.
Wherein the hardware scoring rule comprises the steps of:
and S1, acquiring information of each monitoring index of the hardware, and dividing the information into a variable index and a non-variable index.
And S2, for the unchanged index, carrying out hash storage on the specific parameter information of the unchanged index for comparison of subsequent results. And for the variation index, obtaining the weight coefficient corresponding to each parameter index by an analytic hierarchy process.
And S3, after acquiring the information of each index, comparing the hash value of the information before and after and obtaining each index score and corresponding weight by using a set scoring rule to obtain a final score.
In step S1, information of each monitoring index of the hardware is obtained and divided into two types, namely a variable index and a non-variable index. The method specifically comprises the following steps.
S11: collecting parameter values of each index of hardware, dividing a plurality of parameters such as serial numbers of the hardware and the like which are fixed and unchangeable into unchangeable indexes, and dividing instantly changeable indexes such as CPU utilization rate, memory utilization rate, network bandwidth rate, disk utilization rate and the like into changeable indexes.
S12: the method comprises the steps of monitoring the variable indexes of a CPU, a memory, a network, a disk and the like in hardware in real time, and recording and storing monitoring information such as the CPU utilization rate, the memory utilization rate, the network bandwidth rate, the disk space utilization rate and the like.
In the step S2, for the non-changing index, hash storage is performed on specific parameter information of the index, so as to compare subsequent results. And for the variation index, obtaining the weight coefficient corresponding to each parameter index by an analytic hierarchy process. The method specifically comprises the following steps.
S21: monitoring and obtaining non-variable index parameter information, and sequentially forming an ordered measurement value sequence M-M by the collected parameter information1，M2，M3…MnAnd hashing the values in the sequence in sequence, directly hashing the first measurement value and performing P1＝Hash(M1) Then calculating the hash sequence P of the constant measured valuei+1＝Hash(Pi||Mi) And stored for later comparison.
S22: monitoring and obtaining variation indexes, and forming a judgment matrix [ a ] by using an analytic hierarchy process for each variation indexij]Wherein a isijRepresenting the relative importance between the various indicators of variation. Then calculating the weight vector W ═ W of each layer according to the analytic hierarchy process1，W2，…WnThen calculating the combination weight vector C ═ C of each index1，c2…cjAnd multiplying the scores of the items by the combined weight, and then summing to obtain a total score.
After the information is obtained in step S3, the hash values of the system information are compared before and after, and the score of each index and the corresponding weight are obtained by using the established scoring rule, so as to obtain the final score. The method specifically comprises the following steps.
S31: and for each index single item score, setting the utilization rate of the index as x, and if the index is fully divided into P, taking a reduction function f (x) (f (x) epsilon [0, 1]) related to the utilization rate, and multiplying the reduction function by P to obtain the single item index score.
S32: and when each index is within the normal use range, determining the weight of each index according to an analytic hierarchy process, and finally solving a corresponding score.
S33: and when one or more indexes exceed the normal utilization rate range (the normal range is set as H), selecting the index with the highest utilization rate from the index parameters, and taking the score of the index as a final score.
S34: and if the stored hardware hash is consistent, grading the variable index monitoring value by using an analytic hierarchy process on the basis of the full score P to obtain a final score.
S35: and if the stored hardware hash is inconsistent, grading the variable index monitoring value by using an analytic hierarchy process on the basis of P/2 to obtain a final score.
Wherein the operating system scoring rules comprise the steps of:
and S1, acquiring monitoring index information of each item of the operating system, and dividing the monitoring index information into a variable index and a non-variable index.
And S2, for the unchanged indexes, carrying out hash storage on the specific parameter information of the indexes for comparison of subsequent results. And for the variation index, obtaining the weight coefficient corresponding to each parameter index by an analytic hierarchy process.
And S3, after the information is obtained, carrying out front-back comparison on the hash value of the system information, and obtaining each index score and corresponding weight by utilizing a set scoring rule to obtain a final score.
In step S1, information of each monitoring index of the operating system is obtained and divided into two types, namely a variable index and a non-variable index. The method specifically comprises the following steps.
S11: collecting each index of the operating system, dividing invariable index parameters such as system version number and the like into non-variable indexes, and dividing instant variable index parameters such as existence or nonexistence of viruses, system directory non-system file occupation ratio, system main partition utilization rate and the like into variable indexes.
S12: monitoring and obtaining the change index of the operating system, and recording and storing the existence of viruses, the non-system file proportion of a system directory, the utilization rate of a system main partition and the like.
In the step S2, for the non-changing index, hash storage is performed on specific parameter information of the index, so as to compare subsequent results. And for the variation index, obtaining the weight coefficient corresponding to each parameter index by an analytic hierarchy process. The method specifically comprises the following steps.
S21: monitoring and obtaining non-variable index parameter information, and sequentially forming an ordered measurement value sequence M-M by the collected parameter information1，M2，M3…MnAnd hashing the values in the sequence in sequence. The first measurement is directly Hash-processed P1＝Hash(M1) Then calculating the hash sequence P of the constant measured valuei+1＝Hash(Pi||Mi) And stored for later comparison.
S22: monitoring and obtaining variation indexes, and forming a judgment matrix [ a ] by using an analytic hierarchy process for each variation indexij]Wherein a isijRepresenting the relative importance between the various indicators of variation. Then calculating the weight vector W ═ W of each layer according to the analytic hierarchy process1，W2，…WnThen calculating the combination weight vector C ═ C of each index1，c2…cjAnd multiplying the scores of the items by the combined weight, and then summing to obtain a total score.
After the information is obtained in step S3, the hash values of the system information are compared before and after, and the score of each index and the corresponding weight are obtained by using the established scoring rule, so as to obtain the final score. The method specifically comprises the following steps.
S31: and for each index single item score, setting the utilization rate of the index as x, and if the index is fully divided into P, taking a reduction function f (x) (f (x) epsilon [0, 1]) related to the utilization rate, and multiplying the reduction function by P to obtain the single item index score.
S32: when a virus is detected, x becomes a risk value H, and if no virus is present, x is 0.
S33: and under the condition of no virus, determining the weight according to an analytic hierarchy process when each index is within a normal use range, and calculating to obtain a final score.
S34: under the condition of no virus, when one or more indexes exceed the normal utilization rate range (the normal range is set as N), the index with the highest utilization rate in all index parameters is selected, and the score of the index is used as the final score.
S35: and if the hash of the stored operating system is consistent, grading the variable index measurement value by using an analytic hierarchy process on the basis of the full score P to obtain a final score.
S36: and if the stored operation systems are inconsistent before and after the Hash, grading the variable index measurement value by using an analytic hierarchy process on the basis of P/2 to obtain a final score.
Wherein the software scoring rules comprise the steps of:
and S1, monitoring the software working directory.
And S2, scoring by using a preset rule.
And S3, obtaining a final software score after the monitoring of the operation of adding, deleting and modifying is completed.
The software work directory is monitored in step S1. The method specifically comprises the following steps of,
s11: and periodically monitoring the working directory where the software is located, periodically recording the operations of adding, deleting and modifying within a certain time, and feeding back.
The scoring in step S2 is performed by using a predetermined rule. The method specifically comprises the following steps.
S21: and constructing a white list of a software working directory for normal and safe operation of the software.
S22: : and (4) monitoring abnormal operation of the software to obtain the software single score.
In the step S3, after the detection of the add, delete and modify operation is completed, a final software score is obtained. The method specifically comprises the following steps.
S31: and analyzing the monitored software working catalog, and performing corresponding scoring by using a scoring rule to obtain the score of the software in the system.
Drawings
FIG. 1 is a schematic diagram of the operation of the specific method provided by the present invention.
Fig. 2 is a schematic diagram of a specific scoring process provided by the present invention.
Fig. 3 and 4 are schematic diagrams of embodiments of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The same or similar reference numerals in the drawings of the present embodiment correspond to the same or similar components; in the description of the present invention, it is to be noted that, unless otherwise specified, "a plurality" means two or more; furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The embodiment of the present invention will be further explained with reference to the detailed scoring flowcharts of fig. 1 and fig. 2, and the hardware-based schematic diagrams of fig. 3 and fig. 4.
And S1, acquiring information of each monitoring index of the hardware, and dividing the information into a variable index and a non-variable index, as shown in figure 1.
And S2, for the unchanged indexes, carrying out hash storage on the specific parameter information of the indexes for comparison of subsequent results. And for the variation index, obtaining the weight coefficient corresponding to each parameter index by an analytic hierarchy process.
And S3, after the information is obtained, carrying out front-back comparison on the hash value of the system information, and obtaining each index score and corresponding weight by utilizing a set scoring rule to obtain a final score.
In step S1, information of each monitoring index of the hardware is obtained and divided into two types, namely a variable index and a non-variable index. The method specifically comprises the following steps.
S11: collecting parameter values of each index of hardware, dividing a plurality of parameters such as serial numbers of the hardware and the like which are fixed and unchangeable into unchangeable indexes, and dividing instantly changeable indexes such as CPU utilization rate, memory utilization rate, network bandwidth rate, disk utilization rate and the like into changeable indexes.
S12: the method comprises the steps of monitoring the variable indexes of a CPU, a memory, a network, a disk and the like in hardware in real time, and recording and storing monitoring information such as the CPU utilization rate, the memory utilization rate, the network bandwidth rate, the disk space utilization rate and the like.
In the step S2, for the non-changing index, hash storage is performed on specific parameter information of the index, so as to compare subsequent results. And for the variation index, obtaining the weight coefficient corresponding to each parameter index by an analytic hierarchy process. The method specifically comprises the following steps.
S21: monitoring and obtaining non-variable index parameter information, and sequentially forming an ordered measurement value sequence M-M by the collected parameter information1，M2，M3…MnAnd hashing the values in the sequence in sequence. The first measurement is directly Hash-processed P1＝Hash(M1) Then calculating the hash sequence P of the constant measured valuei+1＝Hash(Pi||Mi) And stored for later comparison, as in fig. 2.
S22: monitoring and obtaining variation indexes, and forming a judgment matrix [ a ] by using an analytic hierarchy process for each variation indexij]Wherein a isijRepresenting the relative importance between the various modification indicators, as shown in fig. 4. Then calculating the weight vector W ═ W of each layer according to the analytic hierarchy process1，W2，…WnAs shown in fig. 3, and then calculates a combination weight vector C ═ C for each index1，c2…cjAnd multiplying the scores of the items by the combined weight, and then summing to obtain a total score.
After the information is obtained in step S3, the hash values of the system information are compared before and after, and the score of each index and the corresponding weight are obtained by using the established scoring rule, so as to obtain the final score. The method specifically comprises the following steps.
S31: and for each index single item score, setting the utilization rate of the index as x, and if the index is fully divided into P, taking a reduction function f (x) (f (x) epsilon [0, 1]) related to the utilization rate, and multiplying the reduction function by P to obtain the single item index score.
S32: and when each index is within the normal use range, determining the weight of each index according to an analytic hierarchy process, and finally solving a corresponding score.
S33: and when one or more indexes exceed the normal utilization rate range (the normal range is set as H), selecting the index with the highest utilization rate from the index parameters, and taking the score of the index as a final score.
S34: and if the stored hardware hash is consistent, grading the variable index monitoring value by using an analytic hierarchy process on the basis of the full score P to obtain a final score.
S35: and if the stored hardware hash is inconsistent, grading the variable index monitoring value by using an analytic hierarchy process on the basis of P/2 to obtain a final score.
Wherein the operating system scoring rules comprise the steps of:
and S1, acquiring monitoring index information of each item of the operating system, and dividing the monitoring index information into a variable index and a non-variable index, as shown in figure 1.
And S2, for the unchanged indexes, carrying out hash storage on the specific parameter information of the indexes for comparison of subsequent results. And for the variation index, obtaining the weight coefficient corresponding to each parameter index by an analytic hierarchy process.
And S3, after the information is obtained, carrying out front-back comparison on the hash value of the system information, and obtaining each index score and corresponding weight by utilizing a set scoring rule to obtain a final score.
In step S1, information of each monitoring index of the operating system is obtained and divided into two types, namely a variable index and a non-variable index. The method specifically comprises the following steps.
S11: collecting each index of the operating system, dividing invariable index parameters such as system version number and the like into non-variable indexes, and dividing instant variable index parameters such as existence or nonexistence of viruses, system directory non-system file occupation ratio, system main partition utilization rate and the like into variable indexes.
S12: monitoring and obtaining the change index of the operating system, and recording and storing the existence of viruses, the non-system file proportion of a system directory, the utilization rate of a system main partition and the like.
In the step S2, for the non-changing index, hash storage is performed on specific parameter information of the index, so as to compare subsequent results. And for the variation index, obtaining the weight coefficient corresponding to each parameter index by an analytic hierarchy process. The method specifically comprises the following steps.
S21: monitoring and obtaining non-variable index parameter information, and sequentially forming an ordered measurement value sequence M-M by the collected parameter information1，M2，M3…MnAnd hashing the values in the sequence in sequence. The first measurement is directly Hash-processed P1＝Hash(M1) Then calculating the hash sequence P of the constant measured valuei+1＝Hash(Pi||Mi) And stored for later comparison.
S22: monitoring and obtaining variation indexes, and forming a judgment matrix [ a ] by using an analytic hierarchy process for each variation indexij]Wherein a isijRepresenting the relative importance between the various modification indicators, as shown in fig. 4. Then calculating the weight vector W ═ W of each layer according to the analytic hierarchy process1，W2，…WnAs shown in fig. 3, and then calculates a combination weight vector C ═ C for each index1，c2…cjAnd multiplying the scores of the items by the combined weight, and then summing to obtain a total score.
After the information is obtained in step S3, the hash values of the system information are compared before and after, and the score of each index and the corresponding weight are obtained by using the established scoring rule, so as to obtain the final score. The method specifically comprises the following steps.
S31: and for each index single item score, setting the utilization rate of the index as x, and if the index is fully divided into P, taking a reduction function f (x) (f (x) epsilon [0, 1]) related to the utilization rate, and multiplying the reduction function by P to obtain the single item index score.
S32: when a virus is detected, x becomes a risk value H, and if no virus is present, x is 0.
S33: and under the condition of no virus, determining the weight according to an analytic hierarchy process when each index is within a normal use range, and calculating to obtain a final score.
S34: under the condition of no virus, when one or more indexes exceed the normal utilization rate range (the normal range is set as N), the index with the highest utilization rate in all index parameters is selected, and the score of the index is used as the final score.
S35: and if the hash of the stored operating system is consistent, grading the variable index measurement value by using an analytic hierarchy process on the basis of the full score P to obtain a final score.
S36: and if the stored operation systems are inconsistent before and after the Hash, grading the variable index measurement value by using an analytic hierarchy process on the basis of 2/P to obtain a final score.
Wherein the software scoring rules comprise the steps of:
and S1, monitoring the software working directory.
And S2, scoring by using a preset rule.
And S3, obtaining a final software score after the monitoring of the operation of adding, deleting and modifying is completed.
The software work directory is monitored in step S1. The method specifically comprises the following steps of,
s11: and periodically monitoring the working directory where the software is located, recording the operations of adding, deleting and modifying within a certain period of time, and feeding back.
The scoring in step S2 is performed by using a predetermined rule. The method specifically comprises the following steps.
S21: and constructing a white list of a software working directory for normal and safe operation of the software.
S22: : and (4) monitoring abnormal operation of the software to obtain the software single score.
In the step S3, after the detection of the add, delete and modify operation is completed, a final software score is obtained. The method specifically comprises the following steps.
S31: and analyzing the monitored software working catalog, and performing corresponding scoring by using a scoring rule to obtain the score of the software in the system.
Finally, the description is as follows: the above-described embodiments are merely preferred embodiments of the present invention, which should not be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (5)
1. A block chain system environment three-dimensional scoring method based on an analytic hierarchy process is characterized by comprising the following steps:
s1, acquiring monitoring index information of each item of the block chain system environment, and dividing the monitoring index information into a variable index and a non-variable index.
S2, carrying out hash storage on the unchanged indexes for comparison of subsequent results; and for the variation index, calculating a weight coefficient corresponding to each parameter index by an analytic hierarchy process.
And S3, performing hash comparison on the index information and calculating the score by using an analytic hierarchy process.
2. The analytic hierarchy process-based block chain system environment three-dimensional scoring method as claimed in claim 1, wherein: in step S1, various monitoring index information of the system is obtained and divided into two types, namely a variable index and a non-variable index, for comparison of subsequent results and parameter scoring.
3. The analytic hierarchy process-based block chain system environment three-dimensional scoring method as claimed in claim 1, wherein: in the step S2, for the unchanged index, hash storage is performed on the specific information of the index, so as to compare the subsequent results.
4. The analytic hierarchy process-based block chain system environment three-dimensional scoring method as claimed in claim 1, wherein: in the step S2, for the variation index, a weight coefficient corresponding to each index is obtained by an analytic hierarchy process, and is used as a parameter for subsequent scoring.
5. The analytic hierarchy process-based block chain system environment three-dimensional scoring method as claimed in claim 1, wherein: in step S3, the hash values of the system information are compared before and after, and the obtained scores of the indexes and the corresponding weights are used to obtain final scores, which are used to calculate the three-dimensional score of the system by combining the hash comparison results of the subsequent variable indexes and the non-variable indexes.
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