Patent Document ID: 20170015339
Application ID: 15123684
Patent Flag: 0

Claim One:
1. A complex network-based high speed train system safety evaluation method, comprising the following steps: Step 1, constructing a network model G(V, E) of a high speed train according to a physical structure relationship of the high speed train, wherein 1.1. components in a high speed train system are abstracted as nodes, that is, V={v 1 , v 2 ,. .. , v n }, wherein V is a set of nodes, v i is a node in the high speed train system, and n is the number of the nodes in the high speed train system; 1.2. physical connection relationships between components are abstracted as connection sides, that is, E={e 12 , e 13 ,. .. e ij }, i, j≦n; wherein E is a set of connection sides, and e ij is a connection side between the node i and the node j; 1.3. a functional attribute degree value {tilde over (d)} i of a node is calculated based on the network model of the high speed train: a functional attribute degree of the node i is 
 {tilde over (d)} i =λ i *k i (1) wherein λ i is a failure rate of the node i, and k i is the degree of the node i in a complex network theory, that is, the number of sides connected with the node; Step 2, by mean of analyzing operational fault data of the high speed train and combining a physical structure of the high speed train system, extracting the functional attribute degree value {tilde over (d)} i , the failure rate λ i and Mean Time Between Failures (MTBF) of the component as a training sample set, to normalize the training sample set, wherein 2.1. a calculation formula of the failure rate λ i is λ i = the number of times of fault running kilometers , 2.2. the MTBF is obtained from fault time recorded in the fault data, that is, MTBF i = ∑ difference of fault time intervals the total number of times of fault - 1 , 2.3. samples are trained by using a support vector machine (SVM), Step 3, dividing safety levels of the samples by using a kNN-SVM; wherein 3.1. training samples in k safety levels are differentiated in pairs, and an optimal classification face is established for k ( k - 1 ) 2 SVM classifiers respectively, of which an expression is as follows: f ij ( x ) = sgn ( ? a , y , K ( x ij , x ) + b ij ) ? indicates text missing or illegible when filed ( 2 ) wherein 1 is the number of samples in the ith safety level and the jth safety level, K(x ij , x) is a kernel function, x is a support vector, a i is a weight coefficient of the SVM, and b ij is an offset coefficient; 3.2. for a component to be tested, a safety level of the component is voted by combining the above two kinds of classifiers and using a voting method; the kind with the most votes is the safety level of the component; 3.3. as an operating environment of the high speed train system is complicated, it is easy to lead to a situation where classification is impossible when classification is carried out by using the SVM, therefore, a weighted kNN-based discrimination function is defined, and safety levels of the components are divided once again, which comprises steps as follows: in a training set {x i , y i },. .. , {x n , y n }, there is a total of one safety level, that is, ca 1 , ca 2 ,. .. , ca l , a sample center of the ith safety level is c i = 1 n i ? x j , ? indicates text missing or illegible when filed wherein n i is the number of samples of the ith safety level, and the Euclidean distance from a component x j to the sample center of the ith safety level is d ( x j , o i ) = ? ( x jm - c im ) 2 ? indicates text missing or illegible when filed ( 3 ) wherein, in the formula: x jm is an mth feature attribute of a jth sample point in a test sample; and c im is an mth feature attribute in an ith-category sample center a distance discrimination function is defined as s j ( x ) = max ( d ( x , c ) ) - d ( x , c i ) max ( d ( x , c ) ) - min ( d ( x , c ) ) ( 4 ) tightness of weighted kNN-based different-category samples is defined as μ i ( x ) = 1 - ? u i ( ? ) d ( ? ) ? d ( x , ? ) ? indicates text missing or illegible when filed ( 5 ) wherein m is the number of k neighbors; u i (x) is the tightness membership degree at which a test sample belongs to the ith training data; and u i (x (j) ) is the membership degree at which the jth neighbor belongs to the ith safety level, that is, u i ( ? ) = { 1 , x ∈ ca i 0 , x ∉ ca i ; ? indicates text missing or illegible when filed and a classification discrimination function of the sample point is 
 d i ( x )= s i ( x )×μ i ( x )  (6) the tightness d i (x) at which a sample belongs to each safety level is calculated, and the category with the greatest value of d i (x) is a sample point prediction result.