CN108683564B - Network simulation system reliability evaluation method based on multidimensional decision attributes - Google Patents
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Abstract
The invention discloses a network simulation system credibility evaluation method based on multidimensional decision attributes. The invention provides a method for evaluating the reliability of a network simulation system from five latitudes of simulation scale, deployment time, simulation nodes, network topology and network performance, aiming at the network simulation system. The invention solves the problem that the existing simulation credibility assessment method can not clearly determine influence factors and quantitative analysis thereof aiming at a specific simulation system, thereby ensuring that the credibility assessment of the network simulation system is more accurate and reasonable.
Description
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a network simulation system credibility assessment method based on multidimensional decision attributes.
Background
With the continuous development of the simulation technology and the continuous deep understanding of the application value of the simulation technology, the application field of the simulation technology is more and more extensive. Meanwhile, requirements on simulation correctness and reliability are higher and higher. Whether the final result of the simulation system has availability for the intended application will directly impact the subsequent series of applications or decision processes that are performed based on the simulation results. In a certain sense, only if the correctness and the credibility of the simulation are ensured, the finally obtained simulation result has the value and the meaning of practical application, and the simulation system really has vitality. Therefore, how to evaluate the correctness and reliability of the simulation system is always a problem that cannot be ignored in the development of the simulation technology.
The research of the simulation credibility assessment method mainly comprises the research of technologies, strategies and the like required by assessment tasks at different stages in the simulation credibility assessment process. Existing credibility assessment methods can be generalized into three major categories: qualitative method, quantitative method and comprehensive method. The qualitative method is simple and easy to understand, has strong operability, and can often detect the characteristic difference which is easily ignored by the quantitative method. But because of its strong subjectivity, its application effect is greatly influenced by the outside world. The quantitative method has the advantages of clear principle, strong objectivity, solid theoretical basis, convenience for automatic realization and the like. However, the quantitative method mostly ignores the precious experience of domain experts, and is only suitable for the situation that both simulation data and reference data exist, and this condition is difficult to be satisfied for economic simulation systems, social simulation systems, especially strategic attack and defense confrontation simulation systems. The comprehensive method focuses on examining the credibility of the simulation system from multiple angles by using different qualitative methods and quantitative methods, and integrates the information to obtain the overall judgment on the credibility of the simulation system. For example, the Yangxizhen method and the like propose that the weight of each factor is determined by adopting an analytic hierarchy process, the credibility of the simulation system is investigated from multiple factors, and the overall credibility of the simulation system is comprehensively obtained. Zhang et al indicate that the inherent ambiguity and complexity exist in the evaluation of the simulation credibility, and therefore, a fuzzy comprehensive evaluation method of the simulation credibility is provided. Willow scientific et al verified the validity of the model based on a similar theory. The comprehensive method can fully exert respective advantages of qualitative analysis and quantitative analysis, and is particularly suitable for solving the problem of reliability evaluation of a complex simulation system. However, how to determine the influence factors capable of evaluating the reliability of the simulation system and accurately quantitatively evaluate the influence factors for the specific simulation system is a bottleneck of a comprehensive method.
The invention provides a network simulation system credibility assessment method based on multidimensional decision attributes aiming at a network simulation system, so as to realize reasonable and accurate assessment of the network simulation system credibility.
Disclosure of Invention
The invention provides a network simulation system credibility evaluation method based on multidimensional decision attributes, which overcomes the defects of the prior art.
The invention provides a network simulation system credibility evaluation method based on multidimensional decision attributes, which comprises the following steps:
evaluating the reliability of simulation scale;
evaluating the reliability of the simulation time;
evaluating the credibility of the simulation node;
evaluating the reliability of the simulation topology;
evaluating the reliability of the simulation performance;
and evaluating the comprehensive credibility based on an analytic hierarchy process.
Further, the reliability evaluation of the simulation scale judges the reliability of the scale of the nodes of the network simulation system according to the ratio of the number of the physical hosts in the simulation system to the number of the simulation nodes.
Furthermore, the simulation time reliability is to estimate the deployment time of the whole network simulation system according to the deployment time of a single machine point in the simulation system and a parallel strategy, and to judge the deployment time reliability of the network simulation system according to the ratio of the estimated deployment time value and the statement value of the whole network simulation system.
Further, the simulation node reliability can verify the ratio of the recognizable value and the declaration value of the simulation system from the number of the nodes, the memory and the three latitudes of the supported application program, and further judge the reliability of the simulation node.
Further, the reliability of the simulation topology judges the reliability of the network topology in the simulation system from the range of the IP address of the simulation system and the two latitudes of the routing performance respectively.
Further, the simulation performance reliability is determined by analyzing three important performance parameters of the simulation network and the real network: and judging the performance reliability of the simulation network by the difference of the bandwidth, the time delay and the packet loss rate.
Furthermore, the comprehensive credibility assessment based on the weight analysis method combines the credibility assessment results of five latitudes including simulation scale, deployment time, simulation nodes, network topology and network performance, and adopts an analytic hierarchy process to realize the assessment of the comprehensive credibility of the network simulation system.
Compared with the prior art, the invention has the following positive effects:
the invention provides a method for evaluating the reliability of a network simulation system from five latitudes of simulation scale, deployment time, simulation nodes, network topology and network performance, aiming at the network simulation system. The method solves the problem that the existing simulation reliability evaluation method can not clearly determine influence factors and quantitative analysis thereof aiming at a specific simulation system, so that the reliability evaluation of the network simulation system is more accurate and reasonable.
Drawings
FIG. 1 is a block diagram of a method for assessing the trustworthiness of a network simulation system based on multidimensional decision attributes;
FIG. 2 is a flow chart of a network simulation system reliability evaluation method based on multidimensional decision attributes;
FIG. 3 is a block diagram of simulation scale credibility evaluation;
FIG. 4 is a block diagram of simulation time trustworthiness evaluation;
FIG. 5 is a block diagram of a simulation node confidence evaluation;
FIG. 6 is a block diagram of a simulation network confidence evaluation;
FIG. 7 is a block diagram of simulation performance confidence evaluation;
FIG. 8 is a block diagram of a comprehensive credibility assessment based on analytic hierarchy process.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative only and should not be construed as limiting the invention.
In the description of the present invention, it is to be understood that the terms "first", "second" and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, features defined as "first", "second", "third" may explicitly or implicitly include one or more of the features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise. The system architecture of the present invention is shown in fig. 1, and the method of the present invention is described in detail below with reference to fig. 2.
Step 110: as shown in fig. 3, the reliability of the simulation scale is evaluated. The treatment process is as follows: firstly, the number n of physical hosts and the number x of declared simulation nodes are obtained, and then the reliability of the simulation scale is judged according to the membership function. According to general knowledge, the following membership functions are set.
Where n represents the number of physical hosts building the emulated network and x represents the number of emulated nodes declared.
Step 120: as shown in fig. 4, the evaluation of the trustworthiness of the simulation time. The treatment process is as follows: firstly, obtaining a declaration value of the deployment time of the simulation network, then deploying a simulation system template on a single host, estimating a minimum simulation network deployment time estimate according to a parallel deployment principle, and finally determining the reliability of the deployment time according to the ratio of the declaration value of the deployment time to the estimation value.
Wherein deployytimeeAn estimated value of the deployment time of the simulated network,
wherein T represents the time for deploying the simulation system template by a single host, x represents the number of simulation nodes declared by the simulation system, and M represents the number of systems which can be deployed at most simultaneously and parallelly according to the parallel strategy.
Step 130: as shown in fig. 5, the evaluation of the trustworthiness of the node is simulated. The treatment process is as follows: firstly, acquiring the Node number of the simulation network statementaMemory size MemoryaAnd number of applications supported ApplicationaThen, the network resource environment unified description file of the simulation network is obtained, and the Node number which can be actually identified by the simulation network is extracted by counting and analyzing the file dataiMemory size MemoryiAnd number of applications supported ApplicationiAnd finally, according to the number of the nodes, the size of the memory and the ratio of the statement value and the estimated value of the number of the supported applications, carrying out weighted analysis on the reliability of the simulation nodes.
Re liabilitynode＝w1Re liabilitycpu+w2Re liabilitymemory+w3Reliabilityappication
Wherein XiRepresenting identifiable parameter values, X, in a unified description of the network resource environmentaAnd expressing the parameter values of the simulation network statement, and respectively taking the number of nodes, the size of the memory and the number of supported applications by X. Weight value wiThe determination is carried out by using an averaging method.
Step 140: as shown in fig. 6, the evaluation of the trustworthiness of the simulated network topology will be evaluated from both the point of view of IP address and routing performance. The treatment process is as follows: firstly, obtaining IP Address range Address Block of simulation network, route support protocol list protocol and log record of topological mapping, and testing response time sequence X of route in real physical network with same scale1And a commit time series Y1Then, based on the network resource environment unified description file, the recognizable network card number interface and the supported routing protocol are analyzed, and simultaneously, the topology is extracted according to the log fileMapping the number of successes and failures and testing the response time sequence X of the route in the simulation network2And a commit time series Y2. And finally, determining the reliability of each factor by adopting a ratio method and a similarity function, and solving the reliability of the simulation network topology by adopting a weight analysis method. The application of the time series similarity function is illustrated as follows:
X1route response time series for real networks, X2To emulate the route response time sequence of the network,
single-dimensional time series data X of response time1＝{x1(t1),x1(t2),....x1(tn)}
X2＝{x2(t1),x2(t2),....x2(tn)}
X1And X2The similarity of (A) is as follows:
wherein
Reflecting the degree of deviation of the development process of the two response time series,
wherein f isx(t) is a connectivity function of the sequence X
fx(t)＝x(ti)+(x(ti+1)-x(ti))(t-ti)/(ti+1-ti) ti<t<ti+1,i＝1,2,3...n
Wherein f'x(t) is a slope function of the sequence X.
Y1Submitting a time series for the routing of a real network, Y2Submitting a time sequence for the route of the simulation network;
single-dimensional time sequence Y of commit times1＝{Y1(t1),Y1(t2),....Y1(tn)}
Y2＝{Y2(t1),Y2(t2),....Y2(tn)}
Y1And Y2The similarity of (A) is as follows:
wherein
Reflecting the degree of deviation of the development process of the two submission time series,
reflecting the degree of dissimilarity of the trends of the two submission time series.
After the IP address block and the routing performance parameters are determined, the reliability of the simulation network topology can be obtained by adopting a weight analysis method. The specific formula is as follows:
Reliabilityroute＝w1Reliabilityprotocol+w2Reliabilityresponstime+w3Reliabilityforwardingtime
Reliabilitytopology＝w1ReliabilityIP+w2Reliabilityroute+w3Reliabilitymapping
wherein, Interface represents the recognizable network card number in the unified description file of network resource environment, Address block represents the IP address range of the simulation network statement, ReliabilityIPRepresenting the credibility of the IP address; protocoliProtocol type number capable of being identified in network resource environment unified description fileaNumber of supportable protocol classes, Reliability, representing emulated network statementsprotocolRepresenting the trustworthiness of the protocol; dRS (0)(X1,X2) Representing a response time sequence X1And X2Degree of deviation of development process, dRS (1)(X1,X2) Representing a response time sequence X1And X2Degree of dissimilarity in the development trends, where X1Representing the response time sequence in a real network, X2Representing a sequence of response times, Reliability, in a simulated networkresponsetimeRepresenting the credibility of the response time parameter in the simulation network; dRS (0)(Y1,Y2) Represents a commit time sequence Y1And Y2Degree of deviation of development process, dRS (1)(Y1,Y2) Represents a commit time sequence Y1And Y2The degree of dissimilarity in the development trends, where Y1Representing a sequence of commit times in a real network, Y2Representing a simulation networkTime series of submissions, ReliabilityforwardingRepresenting the credibility of the submission time parameter in the simulation network; success represents the number of times of successful topology mapping recorded in the log file, fail represents the number of times of failed topology mapping recorded in the log file, ReliabilitymappingRepresenting the credibility of the mapping in the simulation network; weight value wiThe weight can be determined by averaging or by different weight analysis methods according to different requirements for different performance parameters.
Step 150: as shown in fig. 7, the reliability evaluation of the performance of the simulation network determines the performance reliability of the simulation network by analyzing the differences between three important performance parameters of the simulation network and the real network. The treatment process is as follows: in a real network with the same scale or reduced in proportion, measuring the Bandwidth Bandwidth, the time Delay and the packet loss rate Packetloss of the real network, and carrying out data preprocessing to obtain a reasonable reference value and a sequence thereof; and measuring the Bandwidth, the Delay and the Packet loss rate of the simulation network in the same way in the simulation network, comparing the test data with a reference sequence acquired by a real network after preprocessing the test data, and finally obtaining the performance reliability of the simulation network by adopting a weight analysis method, wherein a specific formula is as follows.
Reliabilityperformance＝w1Reliabilitybandwidth+w2Reliabilitydelay+w3Reliabilitypacketloss
Wherein average (X)simulation) Represents the mean value of multiple test values of the influence factor X in the simulation network, average (X)real) Watch (A)Showing the mean, Reliability, of multiple test values of the impact factor X in the real networkXAnd the credibility of the influence factor X is shown, and the X can take the Bandwidth Bandwidth, Delay and Packetloss. Weight value wiCan be determined by averaging.
Step 160: as shown in fig. 8, the comprehensive reliability evaluation of the simulation network evaluates the reliability of the network simulation system by using a weight analysis method on the basis of the simulation scale reliability, the deployment time reliability, the simulation node reliability, the simulation network topology reliability and the simulation performance reliability.
Reliabilitynetwork＝∑wiReliabilityii∈{scale,deploytime,node,topology,performance}
Wherein wiThe method can be determined by adopting an averaging method, or can be determined by adopting different weight analysis methods according to different concerns of the network simulation system. The invention adopts an analytic hierarchy process to determine the relative weight of each factor, and further analyzes the credibility of the simulation network system.
Firstly, a hierarchical structure model of the reliability evaluation of the simulation system is constructed based on a frame diagram of the comprehensive reliability evaluation. The lower layer is the comprehensive reliability of the simulation network system, and the upper layer is five influencing factors of network scale, deployment time, network nodes, network topology and network performance according to the influence of each factor on the reliability of the simulation network. The analytic hierarchy process establishes a pair comparison matrix by analyzing importance ratio values among elements, and determines the weight of relative importance among the elements. According to the invention, through comparing the importance among the elements, a pair comparison matrix is constructed, and the weight of each influence factor relative to the comprehensive reliability of the simulation network is obtained.
And then, a pair comparison matrix is constructed according to the hierarchical structure model, and the step is the key for determining the weight of each influencing factor. In the evaluation of the reliability of the simulation network, the influence of the reliability of the network scale is considered to be the most important, the deployment time of the simulation network is the second, and the influence of the reliability of the network nodes is weaker than the representation of the reliability of the simulation network relative to the network scale and the deployment time. While the impact of network topology and network performance is weaker. The relative importance scale matrix (as shown in table 1) is formed by numerically identifying the relative importance using the 1-9 scale method of the analytic hierarchy process. The matrix inherently contains the relative importance degree of each influence factor to the reliability of the simulation network, and from another perspective, also contains the weight distribution relation of each influence factor.
Table 1 is a relative importance scale matrix for simulation network confidence
| Network scale | Deployment time | Network node | Network topology | Network performance | |
| Network scale | 1 | 3 | 5 | 7 | 7 |
| Deployment time | 1/3 | 1 | 3 | 5 | 5 |
| Network node | 1/5 | 1/3 | 1 | 3 | 3 |
| Network topology | 1/7 | 1/5 | 1/3 | 1 | 1 |
| Network performance | 1/7 | 1/5 | 1/3 | 1 | 1 |
And finally, determining the weight, wherein the weight of each influence factor is calculated by adopting a geometric mean method, and the method comprises the following specific steps:
(1) calculating the product of each element of each row of the matrix to obtain a matrix M of n rows and one column;
M＝{735,25,0.6,0.009524,0.009524}
(2) calculating the n-th power root of each element in the matrix M to obtain a matrix W;
W＝{3.74,1.90,0.90,0.39,0.39}
(3) carrying out normalization processing on the matrix W to obtain a matrix W;
w＝{0.51,0.26,0.12,0.055,0.055}
the consistency of the contrast matrix is acceptable, and the process of weight definition is effective. The network scale, the deployment time, the network nodes, the network topology and the weight of the network performance relative to the reliability of the simulation network are respectively 0.51, 0.26,0.12,0.055 and 0.055, and the comprehensive reliability of the network simulation system can be evaluated through the steps.
The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it should be understood that various changes and modifications can be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A network simulation system credibility assessment method based on multidimensional decision attributes comprises the following steps:
acquiring the number n of physical hosts of the network simulation system and the number x of declared simulation nodes, and then judging the simulation scale reliability of the network simulation system according to the ratio of n to x;
estimating the deployment time deployytime of the network simulation system according to the deployment time of a single machine point in the network simulation system and a parallel strategyeAnd according to deployytimeeAnd a deployment time declaration value deployytime of the network simulation systemaDetermining the deployment time credibility of the network simulation system according to the ratio;
determining the simulation node reliability of the network simulation system according to the number of nodes of the network simulation system, the size of a memory and the ratio of the statement value of the number of the supported application programs to the corresponding estimated value;
according to the IP address range in the network simulation system, the simulation network topology credibility of the network simulation system can be determined by the type of the supported routing protocol, the routing response time sequence and the routing submission time sequence;
determining the simulation performance reliability of the network simulation system according to the ratio of the bandwidth, the time delay and the packet loss rate of the network simulation system to the corresponding performance parameter of the real network;
and performing weighted calculation on the obtained simulation scale reliability, deployment time reliability, simulation node reliability, simulation network topology reliability and simulation performance reliability by adopting a weight analysis method to obtain the comprehensive reliability of the network simulation system.
2. The method of claim 1, wherein the simulation scale confidence level is determined according to a membership function; wherein the membership function
3. The method of claim 1, in which the deployment time
Wherein, T represents the time of single machine deploying the network simulation system, M represents the number of single machines which can deploy the network simulation system at most simultaneously according to the parallel strategy, and x represents the number of simulation nodes declared by the simulation system.
4. The method of claim 1, wherein the method of determining the trustworthiness of the simulation node is: node for acquiring number of nodes declared by network simulation systemaMemory size MemoryaAnd number of applications supported Applicationa(ii) a Node for estimating actually recognizable Node number of network simulation system according to network resource environment unified description fileiMemory size MemoryiAnd number of applications supported Applicationi(ii) a Finally, according to the number of nodes, the size of the memory, the statement value and the corresponding estimation value of the number of the supported applicationsAnd determining the simulation node reliability of the network simulation system.
5. The method of claim 4, wherein the simulation node trustworthiness is: re liabilitynode＝w1Re liabilitycpu+w2Re liabilitymemory+w3Re liabilityappication(ii) a Wherein,
weight value w1、w2、w3The determination is carried out by using an averaging method.
6. The method of claim 1, wherein the method of determining the trustworthiness of the simulated network topology is: obtaining IP Address range Address Block and routing support protocol of network simulation systemaAnd the log record of the topology mapping, and the response time sequence X of the route is tested in a real physical network with the same scale1And a commit time series Y1(ii) a Then, based on the network resource environment uniform description file of the real physical network with the same scale, the network card number interface which can be identified and the routing protocol which is supported are obtainedi(ii) a Extracting success times and failure times of topology mapping according to the log record, and testing a response time sequence X of the route in the network simulation system2And a commit time series Y2(ii) a And then determining the reliability of the simulated network topology according to the reliability of the response time sequence and the reliability of the submission time sequence.
7. The method of claim 6, wherein the confidence level of the response time series, X1And X2Has a similarity of
Wherein,
reflecting the degree of deviation of the development process of the two response time series,
reflecting the degree of dissimilarity of the development trends of the two response time sequences; confidence of the commit time sequence, i.e. Y1And Y2Has a similarity of
Wherein,
reflecting the degree of deviation of the development process of the two submission time series,
reflecting the degree of dissimilarity of the trends of the two submission time series.
8. The method of claim 7, wherein the simulated network topology trustworthiness is Reliabilitytopology＝w21Re liabilityIP+w22Re liabilityroute+w23Re liabilitymapping(ii) a Wherein, Re liabilityroute＝w31Re liabilityprotocol+w32Re liabilityresponstime+w33Reliabilityforwardingtime，
w21、w22、w23、w31、w32、w33Is a weight value.
9. The method of claim 1, wherein the simulated network performance confidence is: reliability of a memory cellperformance＝w1Re liabilitybandwidth+w2Re liabilitydelay+w3ReliabilitypacketlossWherein
Bandwidthsimulationbandwidth and Bandwidth for simulation networkrealBandwidth, Delay for real networksimulationFor simulating Delay, Delay of networkrealTime delay, Packetloss for real networkssimulationPacket loss ratio, Packetloss, for emulated networksrealThe packet loss rate of the real network is weighted value w1、w2、w3The determination is carried out by using an averaging method.
10. The method of claim 1, wherein the integrated trustworthiness of the simulation network is: reliability of a memory cellnetwork＝∑wiReliabilityii belongs to scale, deployytime, node, topology, performance }; wherein, wiDetermining by adopting an analytic hierarchy process, wherein the analytic hierarchy process firstly constructs a hierarchical structure model for reliability evaluation of the simulation system based on a frame graph for comprehensive reliability evaluation, then constructs a pair comparison matrix according to the hierarchical structure model, and finally calculates the weight of each influence factor by adopting a geometric mean method, thereby determining the simulation scale reliability scale, the deployment time reliability deploytime, the simulation node reliability node, the simulation topology reliabilityRelative weights of topology and fidelity performance.
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| CN111274109B (en) * | 2020-01-20 | 2023-06-02 | 国网甘肃省电力公司信息通信公司 | Evaluation method and system for system software and hardware topology based on request processing simulation |
| CN113392536B (en) * | 2021-06-30 | 2024-01-30 | 中国南方电网有限责任公司 | Power communication network simulation evaluation method, device, computer equipment and medium |
| CN114726601B (en) * | 2022-03-28 | 2023-06-02 | 北京计算机技术及应用研究所 | Information security simulation modeling and verification evaluation method based on graph structure |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102306353A (en) * | 2011-09-01 | 2012-01-04 | 王钰 | Method and system for estimating credibility of simulation system |
| CN103246762A (en) * | 2013-04-10 | 2013-08-14 | 哈尔滨工程大学 | Method of comprehensive evaluation for simulation credibility of electric propulsion system |
| JP2013206361A (en) * | 2012-03-29 | 2013-10-07 | Nec Corp | Simulation system, method and program |
| CN106960065A (en) * | 2016-01-11 | 2017-07-18 | 北京仿真中心 | The robust appraisal procedure and system of a kind of Complex simulation systems confidence level |
-
2018
- 2018-05-21 CN CN201810489556.XA patent/CN108683564B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102306353A (en) * | 2011-09-01 | 2012-01-04 | 王钰 | Method and system for estimating credibility of simulation system |
| JP2013206361A (en) * | 2012-03-29 | 2013-10-07 | Nec Corp | Simulation system, method and program |
| CN103246762A (en) * | 2013-04-10 | 2013-08-14 | 哈尔滨工程大学 | Method of comprehensive evaluation for simulation credibility of electric propulsion system |
| CN106960065A (en) * | 2016-01-11 | 2017-07-18 | 北京仿真中心 | The robust appraisal procedure and system of a kind of Complex simulation systems confidence level |
Non-Patent Citations (2)
| Title |
|---|
| Design and reliability evaluation of simulation system for fire control radar network;Zhiqiang Li等;《IEEE》;20120618;全文 * |
| 层次分析法在仿真系统可信度评估中的应用;刘荷明等;《航天电子对抗》;20160513;全文 * |
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