CN116862234A - Risk assessment method for track traffic self-consistent energy system - Google Patents
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Abstract
The invention discloses a risk assessment method of a self-consistent energy system based on improved evidence theory rail transit, which comprises the following steps: a. combing risk factors of human, equipment, environment and management in a track traffic self-consistent energy system, and establishing an evaluation index system; b. constructing a Gaussian membership function of a risk level of the track traffic self-consistent energy system and a membership matrix thereof; c. and (3) carrying out risk factor fusion of an improved DS evidence theory based on matrix analysis, simultaneously converting the internal influence relation of each evaluation index into a topological structure of a Bayesian network, establishing a Bayesian network model, carrying out Bayesian network reasoning, and calculating to obtain probability values of 5 risk levels of the whole track traffic self-consistent energy system. The method has the advantages of high calculation speed and high precision when evaluating the rail traffic risk, and can better fuse multiple evidence information.
Description
Technical Field
The invention belongs to the field of energy and traffic fusion, and particularly relates to a risk assessment method of a track traffic self-consistent energy system.
Background
The electrified railway has the advantages of small pollution, high efficiency, high traction power and the like, and has been widely used worldwide. Future electrified railways will continue to evolve in the direction of "large scale, high speed, high density". However, there will be an increasing demand for significant electrical energy. The electrified railway is urgent to realize the efficient, green and high-elasticity development of self energy consumption while ensuring the safe and reliable power supply of the electrified railway. The self-consistent energy system of the rail transit converts the self-resources of the rail transit into energy, and is obtained if the self-energy supply of the rail transit can be met.
The 'network-source-storage-vehicle' collaborative energy supply system integrating new energy and energy storage has the uncertainty of supply and demand in the whole operation process, so that the source of risk factors is complex, and the assessment system is imperfect. Therefore, in order to enable the future safe and efficient development of the rail transit system, a risk assessment method of the rail transit self-consistent energy system is established, and the method has important significance in assisting the related technical development and engineering landing of the rail transit self-consistent energy system.
In the prior art, the risk assessment of the track traffic self-consistent energy system mainly adopts the following methods: analytic hierarchy process, expert investigation method, event tree method, fuzzy comprehensive evaluation method, gray system method, etc. The principles of the methods, the data required and the effects achieved can be broadly divided into qualitative, semi-quantitative and quantitative categories, however, these methods have the following problems: expert investigation and other qualitative evaluation methods, which are greatly influenced by subjective factors such as working experience, cognition level, operation attitude and the like of users, have strong randomness in the evaluation process and possibly influence the objectivity and accuracy of the evaluation result; semi-quantitative evaluation methods such as analytic hierarchy process and the like have less quantitative data, more qualitative ingredients, difficult convincing, large data statistics when indexes are too many and difficult weight determination; the quantitative evaluation methods such as the fuzzy comprehensive evaluation method and the like need to establish a complex mathematical model in the evaluation process, and have the problems of high requirements on the completeness of basic data, large modeling workload, complex model solving and the like.
Object of the Invention
The invention aims to solve the problems of the prior art, and provides a risk assessment method based on an improved evidence theory track traffic self-consistent energy system, which solves the problems of complex risk factor relationship and imperfect assessment system.
Disclosure of Invention
The invention provides a risk assessment method of a self-consistent energy system of rail transit, which comprises the following steps of:
a. the method comprises the steps of carding risk factors of human, equipment, environment and management in the operation process of a track traffic self-consistent energy system, and establishing an evaluation index system of risks;
b. constructing a Gaussian membership function of the risk level of the track traffic self-consistent energy system;
c. and (3) carrying out risk factor fusion of an improved DS evidence theory based on matrix analysis, simultaneously establishing a Bayesian network, carrying out Bayesian network reasoning, and calculating to obtain probability values of 5 risk levels of the whole track traffic self-consistent energy system respectively.
Preferably, the method comprises the steps of,step a further comprises: an evaluation index system covering a track traffic self-consistent energy system is constructed from four primary index angles of human factors P, mechanical equipment factors E, environmental factors S and management factors M respectively, and the evaluation index system comprises a secondary operation error rate P 1 Rate of illegal operation P 2 Evaluating human factors; from vehicle performance condition E 1 Maintenance E of equipment 2 Failure of photovoltaic device E 3 Insufficient energy storage element device digestion E 4 The failure rate of these typical devices evaluates the device factors; from weather conditions S 1 In-station environment S 2 And road condition S 3 These environmental indicators evaluate environmental factors; assessment of environmental management factors from the integrity of the operating management regime, including laws and regulations M 1 Safety management system and measure M 2 Staff education training M 3 The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the human factor P, the mechanical equipment factor E, the environmental factor S and the management factor M are 4 primary indexes, and the operation error rate P 1 Rate of illegal operation P 2 Vehicle performance condition E 1 Maintenance E of equipment 2 Failure of photovoltaic device E 3 Insufficient energy storage element device digestion E 4 Weather conditions S 1 In-station environment S 2 And road condition S 3 Law and regulation M 1 Safety management system and measure M 2 Staff education training M 3 13 secondary indexes;
preferably, step b further comprises:
step b1: the evaluation set is divided into five grades, namely dangerous, general, safe and safe, and the quantitative values are respectively set to be 0.9, 0.7, 0.5, 0.3 and 0.1; according to the evaluation target score, the score was divided into 1 score, and the scores were respectively dropped into the interval (0.8-1]、(0.6-0.8]、(0.4-0.6]、(0.2-0.4]、(0-0.2]The risk levels of (a) respectively correspond to emergency, alarm, early warning, attention, safety, and the target evaluation levels respectively correspond to V 1 、V 2 、V 3 、V 4 、V 5 The target evaluation set is expressed as v= (V 1 ，V 2 ，V 3 ，V 4 ，V 5 )
Step b2: determining a membership function, defining a function center point mu= (0,0.25,0.5,0.75,1) of 5 risk grades, and respectively representing the membership function and a membership matrix as shown in formulas (1) - (5):
wherein x refers to expert quantization value, and delta refers to standard deviation of normal distribution;
the membership matrix of the 13 secondary indexes is expressed as shown in formula (6):
wherein p is 1 、p 2 Expert quantized values, m, respectively refer to secondary indexes under human factors 1 、m 2 、m 3 、m 4 Expert quantized values, e, respectively refer to the secondary index under the management factors 1 、e 2 、e 3 、e 4 Expert quantized values s of secondary indexes under mechanical equipment factors 1 、s 2 、s 3 Expert quantization values respectively refer to secondary indexes under environmental factors,respectively refer to the second under human factorsStandard deviation of grade index ∈>Respectively the standard deviation of the secondary index under the management factors,standard deviation of secondary index under mechanical equipment factors, respectively +.>Respectively refer to standard deviation of the secondary index under the environmental factors.
Preferably, step c further comprises:
step c 1 : improving a DS algorithm, and calculating probability values of 5 risk levels after fusion by adopting a DS synthesis algorithm based on matrix analysis and weight distribution; the expert opinion is fused by adopting a mode of evidence recurrence combination through matrix analysis, n experts are assumed to evaluate an index system, and the evaluation result of the n experts on a certain risk influence factor is determined through a given risk grade membership function of the rail transit self-consistent energy system and is expressed as shown in formulas (7) - (10):
A＝[a 1 a 2 a 3 a 4 a 5 ] (7)，
B＝[b 1 b 2 b 3 b 4 b 5 ] (8)，
C＝[c 1 c 2 c 3 c 4 c 5 ] (9)，
N＝[n 1 n 2 n 3 n 4 n 5 ] (10)，
wherein, any element a in the matrix A ％ Probability value, a, representing the risk of class 1 expert evaluation as class i 1 +a 2 +a 3 +a 4 +a 5 ＝1；
With transpose A of matrix A T Multiplying the matrix B to obtain a new matrix M 1 Expressed as shown in formula (11):
matrix M 1 The sum of non-main diagonal elements is the degree of conflict K1 of evidence A and evidence B; m is M 1 Multiplying a column matrix formed by main diagonal lines by a matrix A to obtain a matrix M 2 Expressed as shown in formula (12):
matrix M 1 And M 2 The sum of all non-main diagonal elements of (a) is the degree of conflict K2 for evidence A, evidence B and evidence C; repeating the steps until all expert opinions are fused;
calculating probability values of 5 risk levels after fusion by using a DS evidence theory synthesis algorithm based on weight distribution improvement, and expressing a formula after weight distribution improvement as shown in a formula (13):
wherein, for one decision problem, the recognizable possible results are represented by a set Θ, m is the basic credibility allocation on the recognition framework Θ;m 1 (A i ) Is A i Is a mass function of (A) reflecting that of A i The supporting degree of the proposition is that the value of the supporting degree is assigned to the basic trust of the proposition, the basic trust value of the empty set is zero, and K is the conflict degree between evidences; f (A) is a probability distribution function of evidence conflict;
let f (a) =kq (a), where q (a) represents the average support degree of all evidences to a, and the calculation formula is expressed as shown in formula (14):
wherein m is i (A) Is the degree of support of the ith expert on A;
step c2: firstly, constructing a Bayesian network model, particularly according to an established self-consistent energy system of rail transit, utilizing GENIE software to convert the internal influence relation of each evaluation index into the topological structure of the Bayesian network, enabling network nodes to correspond to index factors of each level of an index system one by one, namely, enabling target child nodes to correspond to the rail transit running state of a criterion layer, enabling intermediate nodes to correspond to 4 primary indexes of the criterion layer, including personnel factors P, mechanical equipment factors E, environmental factors S and management factors M, enabling father nodes to correspond to the criterion layer, including operation error rate P 1 Rate of illegal operation P 2 Vehicle performance condition E 1 Maintenance E of equipment 2 Failure of photovoltaic device E 3 Insufficient energy storage element device digestion E 4 Weather conditions S 1 In-station environment S 2 And road condition S 3 Law and regulation M 1 Safety management system and measure M 2 Staff education training M 3 13 secondary indexes of the track traffic self-consistent energy system, thereby constructing a Bayesian network model of the track traffic self-consistent energy system; and substituting the data in the conditional probability matrix into each node in the Bayesian network model of the constructed track traffic self-consistent energy system by using GENIE software, solving an updated Bayesian network model, and calculating probability values of the whole track traffic self-consistent energy system at 5 risk levels respectively to obtain probability value distribution of each evaluation index.
Drawings
Fig. 1 is a flowchart of a risk assessment method of the track traffic self-consistent energy system.
FIG. 2 is a Bayesian network model constructed in accordance with 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.
It will be appreciated by those skilled in the art that the step numbers used herein are for convenience of description only and are not limiting as to the order in which the steps are performed. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. The term "and/or" refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
The present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is a flowchart of a risk assessment method of the track traffic self-consistent energy system. As shown in fig. 1, a rail transit self-consistent energy system risk assessment method based on an improved evidence theory includes the following steps:
the self-consistent energy system for certain track traffic is a track traffic system which mainly utilizes photovoltaic and energy storage equipment to realize self-consistent. The system is supposed to be qualified in daily inspection and not maintained in the current month, and the vehicles in the current year are not subjected to larger traffic accidents, so that other safety devices are complete. And 3 field experts are selected to perform risk evaluation and analysis on the whole operation process of the system, wherein one expert is a manager of rail transit related work with working experience of more than 5 years, one expert is a manager and a researcher in the field of rail transit risk management research, and the other expert is a teaching of the North China electric university profession.
Step a: an evaluation index system capable of comprehensively covering the self-consistent energy system of the rail transit is constructed from the aspects of man-made, equipment, environment and management 4. And meanwhile, adopting a risk expression mode of 5 grades of risk, more risk, general, safer and safer, and obtaining a final risk evaluation result from the evaluation set. The evaluation indexes of the constructed track traffic self-consistent energy system are shown in table 1:
table 1 track traffic self-consistent energy system evaluation index
Step b: and constructing a membership function. And (3) giving an evaluation value and uncertainty of a secondary index in an evaluation system by an expert, and enabling centers of membership functions corresponding to 5 different risk grades to be 1, 0.75, 0.5, 0.25 and 0 respectively according to Gaussian membership functions to obtain a risk grade membership function in the system.
The target evaluation set is shown in table 2. The expert gives the evaluation level and the corresponding standard deviation of the secondary index in the evaluation system, and the expert evaluation index table is shown in table 3.
TABLE 2 target evaluation set
TABLE 3 expert evaluation index
Step b2: determining a membership function, defining a function center point mu= (0,0.25,0.5,0.75,1) of 5 risk grades, and respectively representing the membership function and a membership matrix as shown in formulas (1) - (5):
wherein x refers to expert quantization value, and delta refers to standard deviation of normal distribution;
the membership matrix of the 13 secondary indexes is expressed as shown in formula (6):
wherein p is 1 、p 2 Expert quantized values, m, respectively refer to secondary indexes under human factors 1 、m 2 、m 3 、m 4 Expert quantized values, e, respectively refer to the secondary index under the management factors 1 、e 2 、e 3 、e 4 Expert quantized values s of secondary indexes under mechanical equipment factors 1 、s 2 、s 3 Expert quantization values respectively refer to secondary indexes under environmental factors,standard deviation of secondary index under human factor, respectively, ">Respectively the standard deviation of the secondary index under the management factors,standard deviation of secondary index under mechanical equipment factors, respectively +.>Respectively refer to standard deviation of the secondary index under the environmental factors.
For step c, comprising:
step c1: the DS algorithm is improved, and a DS synthesis algorithm based on matrix analysis and weight distribution is adopted to calculate probability values of 5 risk levels after fusion. The matrix analysis-based algorithm is used for reducing the calculated amount, and the weight distribution-based algorithm is used for solving the problem of evidence conflict.
And carrying out data fusion for improving DS evidence theory based on matrix analysis. The patent fuses expert opinion through matrix analysis first and by adopting a mode of evidence recurrence combination. Assuming that n experts are used for evaluating the index system, the evaluation result of the n experts on a certain risk influence factor is determined through a given risk grade membership function of the rail transit self-consistent energy system, as shown in table 4.
TABLE 4 expert evaluation results of certain factors
Determining the evaluation results of n experts on a certain risk influence factor, wherein the evaluation results are expressed as shown in formulas (7) - (10):
A＝[a 1 a 2 a 3 a 4 a 5 ] (7)，
B＝[b 1 b 2 b 3 b 4 b 5 ] (8)，
C＝[c 1 c 2 c 3 c 4 c 5 ] (9)，
N＝[n 1 n 2 n 3 n 4 n 5 ] (10)，
wherein, any element a in the matrix A ％ Probability value, a, representing the risk of class 1 expert evaluation as class i 1 +a 2 +a 3 +a 4 +a 5 ＝1；
With transpose A of matrix A + Multiplying the matrix B to obtain a new matrix M 1 Expressed as shown in formula (11):
matrix M 1 The sum of non-main diagonal elements is the degree of conflict K1 of evidence A and evidence B; m is M 1 Multiplying a column matrix formed by main diagonal lines by a matrix A to obtain a matrix M 2 Expressed as shown in formula (12):
matrix M 1 And M 2 The sum of all non-main diagonal elements of (a) is the degree of conflict K2 for evidence A, evidence B and evidence C; repeating the steps until all expert opinions are fused;
calculating probability values of 5 risk levels after fusion by using a DS evidence theory synthesis algorithm based on weight distribution improvement, and expressing a formula after weight distribution improvement as shown in a formula (13):
wherein for one decision problem, the perceived possible results are represented by the set Θ, m is the basic confidence allocation on the recognition framework Θ;m 1 (A i ) Is A i Is a mass function of (A) reflecting that of A i The supporting degree of the proposition is that the value of the supporting degree is assigned to the basic trust of the proposition, the basic trust value of the empty set is zero, and K is the conflict degree between evidences; f (A) is a probability distribution function of evidence conflict;
let f (a) =kq (a), where q (a) represents the average support degree of all evidences to a, and the calculation formula is expressed as shown in formula (14):
wherein m is i (A) Is the degree of support of the ith expert on A;
step c2: bayesian network reasoning.
And constructing a Bayesian network model. According to the established track traffic self-consistent energy system, the internal influence relation of each evaluation index is converted into a topological structure of the Bayesian network in the GENIE software, the network nodes are in one-to-one correspondence with index factors of each level of the index system, namely, the target child nodes correspond to the track traffic running state of the criterion layer, the intermediate nodes correspond to 4 primary indexes of the criterion layer, such as personnel factors, mechanical equipment factors and the like, and the father nodes correspond to 13 secondary indexes of the criterion layer, such as operation failure rate, weather conditions, safety management system and the like, so that the track traffic self-consistent energy system is a Bayesian network model.
Substituting the data in the conditional probability matrix into each node in the Bayesian network model of the track traffic self-consistent energy system by using the GENIE software, solving out and updating the Bayesian network model, calculating probability values of the whole track traffic self-consistent energy system at 5 risk levels respectively, and obtaining probability value distribution of each evaluation index.
Examples
And (3) sequentially grading the indexes according to the established track traffic self-consistent energy system evaluation indexes by taking expert 1 as an example, wherein the results are shown in tables 5 and 6.
TABLE 5 expert 1 evaluation index
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TABLE 6 expert 1 target evaluation set
Substituting the data into membership degree matrixes of 5 primary indexes, and normalizing the matrix rows to obtain probability value distribution data of each evaluation index of the expert 1, as shown in table 7.
TABLE 7 basic probability distribution of expert 1 evaluation index
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Similarly, probability value distribution data of each evaluation index of the experts 2 and 3 can be obtained, data fusion is carried out by utilizing a synthesis algorithm based on matrix analysis and weight distribution, and the fused results are shown in table 8.
TABLE 8 results after data fusion
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Step c:
according to the established risk evaluation index system of the track traffic self-consistent energy system, the internal influence relation of each evaluation index is converted into a topological structure of the Bayesian network by utilizing GENIE software, index factors of each layer of the network node index system are in one-to-one correspondence, namely, the target child node corresponds to the 4 primary indexes of the criterion layer, such as human factors, equipment factors and the like, and the father node corresponds to 13 secondary indexes of the criterion layer, such as physiological psychological quality, vehicle performance condition, safety management system and the like, so as to construct the Bayesian network model of the system. And then, carrying out Bayesian network reasoning, substituting the data in the conditional probability matrix into each node in the Bayesian network model of the track traffic self-consistent energy system by using GENIE software, solving an updated Bayesian network model, calculating probability values of the whole system at 5 risk levels respectively, and obtaining probability value distribution of each evaluation index by using the Bayesian network model constructed by the invention as shown in figure 2.
As can be seen from fig. 2, the risk level of the estimated self-consistent rail traffic energy system is V is obtained through the reasoning function of the bayesian network 2 In a more dangerous stage.
The above embodiments are described in detail with respect to the technical solution of the present invention. It is obvious that the invention is not limited to the described embodiments. Based on the embodiments of the present invention, those skilled in the art can make various changes thereto, but any changes equivalent or similar to the present invention are within the scope of the present invention.
Claims (4)
1. A risk assessment method for a self-consistent energy system of rail transit, wherein the rail transit contains photovoltaic energy, and the risk assessment method comprises the following steps:
a. the method comprises the steps of carding risk factors of human, equipment, environment and management in the operation process of a track traffic self-consistent energy system, and establishing an evaluation index system of risks;
b. constructing a Gaussian membership function of the risk level of the track traffic self-consistent energy system;
c. and (3) carrying out risk factor fusion of an improved DS evidence theory based on matrix analysis, simultaneously establishing a Bayesian network, carrying out Bayesian network reasoning, and calculating to obtain probability values of 5 risk levels of the whole track traffic self-consistent energy system respectively.
2. The risk assessment method for a rail transit self-consistent energy system of claim 1, wherein step a further comprises: an evaluation index system covering a track traffic self-consistent energy system is constructed from four primary index angles of human factors P, mechanical equipment factors E, environmental factors S and management factors M respectively, and the evaluation index system comprises a secondary operation error rate P 1 Rate of illegal operation P 2 Evaluating human factors; from vehicle performance condition E 1 Maintenance E of equipment 2 Failure of photovoltaic device E 3 Insufficient energy storage element device digestion E 4 The failure rate of these typical devices evaluates the device factors; from weather conditions S 1 In-station environment S 2 And road condition S 3 These environmental indicators evaluate environmental factors; assessment of environmental management factors from the integrity of the operating management regime, including laws and regulations M 1 Safety management system and measure M 2 Staff education training M 3 The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the human factor P, the mechanical equipment factor E, the environmental factor S and the management factor M are 4 primary indexes, and the operation error rate P 1 Rate of illegal operation P 2 Vehicle performance condition E 1 Maintenance E of equipment 2 Failure of photovoltaic device E 3 Insufficient energy storage element device digestion E 4 Weather conditions S 1 In-station environment S 2 And road condition S 3 Law and regulation M 1 Safety management system and measure M 2 Staff education training M 3 13 secondary indexes.
3. The risk assessment method for a rail transit self-consistent energy system of claim 1, wherein step b further comprises:
step b1: the evaluation set is divided into five grades, namely dangerous, general, safe and safe, and the quantitative values are respectively set to be 0.9, 0.7, 0.5, 0.3 and 0.1; according to the evaluation target score, the score was divided into 1 score, and the scores were respectively dropped into the interval (0.8-1]、(0.6-0.8]、(0.4-0.6]、(0.2-0.4]、(0-0.2]The risk levels of (a) respectively correspond to emergency, alarm, early warning, attention, safety, and the target evaluation levels respectively correspond to V 1 、V 2 、V 3 、V 4 、V 5 The target evaluation set is expressed as v= (V 1 ，V 2 ，V 3 ，V 4 ，V 5 )
Step b2: determining a membership function, defining a function center point mu= (0,0.25,0.5,0.75,1) of 5 risk grades, and respectively representing the membership function and a membership matrix as shown in formulas (1) - (5):
wherein x refers to expert quantization value, and delta refers to standard deviation of normal distribution;
the membership matrix of the 13 secondary indexes is expressed as shown in formula (6):
wherein p is 1 、p 2 Expert quantized values, m, respectively refer to secondary indexes under human factors 1 、m 2 、m 3 、m 4 Expert quantized values, e, respectively refer to the secondary index under the management factors 1 、e 2 、e 3 、e 4 Expert quantized values s of secondary indexes under mechanical equipment factors 1 、s 2 、s 3 Expert quantization values respectively refer to secondary indexes under environmental factors,standard deviation of secondary index under human factor, respectively, ">Respectively the standard deviation of the secondary index under the management factors,standard deviation of secondary index under mechanical equipment factors, respectively +.>Respectively refer to standard deviation of the secondary index under the environmental factors.
4. The risk assessment method for a rail transit self-consistent energy system of claim 1, wherein step c further comprises:
step c 1 : improving a DS algorithm, and calculating probability values of 5 risk levels after fusion by adopting a DS synthesis algorithm based on matrix analysis and weight distribution; the expert opinion is fused by adopting a mode of evidence recurrence combination through matrix analysis, n experts are assumed to evaluate an index system, and the evaluation result of the n experts on a certain risk influence factor is determined through a given risk grade membership function of the rail transit self-consistent energy system and is expressed as shown in formulas (7) - (10):
A＝[a 1 a 2 a 3 a 4 a 5 ] (7)，
B＝[b 1 b 2 b 3 b 4 b 5 ] (8)，
C＝[c 1 c 2 c 3 c 4 c 5 ] (9)，
N＝[n 1 n 2 n 3 n 4 n 5 ] (10)，
wherein, any element a in the matrix A ％ Probability value, a, representing the risk of class 1 expert evaluation as class i 1 +a 2 +a 3 +a 4 +a 5 ＝1；
With transpose A of matrix A T Multiplying the matrix B to obtain a new matrix M 1 Expressed as shown in formula (11):
matrix M 1 The sum of non-main diagonal elements is the degree of conflict K1 of evidence A and evidence B; m is M 1 Multiplying a column matrix formed by main diagonal lines by a matrix A to obtain a matrix M 2 Expressed as shown in formula (12):
matrix M 1 And M 2 The sum of all non-main diagonal elements of (a) is the degree of conflict K2 for evidence A, evidence B and evidence C; repeating the steps until all expert opinions are fused;
calculating probability values of 5 risk levels after fusion by using a DS evidence theory synthesis algorithm based on weight distribution improvement, and expressing a formula after weight distribution improvement as shown in a formula (13):
wherein, for one decision problem, the recognizable possible results are represented by a set Θ, m is the basic credibility allocation on the recognition framework Θ;m 1 (A i ) Is A i Is a mass function of (A) reflecting that of A i The supporting degree of the proposition is that the value of the supporting degree is assigned to the basic trust of the proposition, the basic trust value of the empty set is zero, and K is the conflict degree between evidences; f (A) is a probability distribution function of evidence conflict;
let f (a) =kq (a), where q (a) represents the average support degree of all evidences to a, and the calculation formula is expressed as shown in formula (14):
wherein m is i (A) Is the degree of support of the ith expert on A;
step c2: firstly, constructing a Bayesian network model, particularly according to an established self-consistent energy system of rail transit, utilizing GENIE software to convert the internal influence relation of each evaluation index into the topological structure of the Bayesian network, enabling network nodes to correspond to index factors of each level of an index system one by one, namely, enabling target child nodes to correspond to the rail transit running state of a criterion layer, enabling intermediate nodes to correspond to 4 primary indexes of the criterion layer, including personnel factors P, mechanical equipment factors E, environmental factors S and management factors M, enabling father nodes to correspond to the criterion layer, including operation error rate P 1 Rate of illegal operation P 2 Vehicle performance condition E 1 Maintenance E of equipment 2 Failure of photovoltaic device E 3 Insufficient energy storage element device digestion E 4 Weather conditions S 1 In-station environment S 2 And road condition S 3 Law and regulation M 1 Safety management system and measure M 2 Staff education training M 3 13 secondary indexes of the track traffic self-consistent energy system, thereby constructing a Bayesian network model of the track traffic self-consistent energy system; and substituting the data in the conditional probability matrix into each node in the Bayesian network model of the constructed track traffic self-consistent energy system by using GENIE software, solving an updated Bayesian network model, and calculating probability values of the whole track traffic self-consistent energy system at 5 risk levels respectively to obtain probability value distribution of each evaluation index.
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