CN114331039A - Railway track connecting scheme determination method based on hierarchical analysis and multi-level fuzzy evaluation - Google Patents
Railway track connecting scheme determination method based on hierarchical analysis and multi-level fuzzy evaluation Download PDFInfo
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Abstract
The invention relates to a railway track connecting scheme determining method based on hierarchical analysis and multi-level fuzzy evaluation, which comprises the following steps of: s1, determining the number k of station track connecting schemes; s2, analyzing w elements influencing the selection of the track connecting scheme, and establishing a hierarchical analysis model which is respectively a target layer, a criterion layer and an index layer; s3, constructing a judgment matrix in each hierarchy; s4, determining the weight of each element by using the judgment matrix; s5, carrying out consistency check on the judgment matrix, and checking whether the weight setting is reasonable; s6, determining the total weight of each element influencing the track connecting scheme; s7, performing normalization processing on the w elements of each track connecting scheme, and constructing a fuzzy comprehensive evaluation table; s8, merging the fuzzy evaluation matrix of the index layer and the element weight of the layer to obtain a comprehensive evaluation matrix of the criterion layer; and S9, carrying out merging operation on the fuzzy evaluation matrix of the criterion layer and the element weight of the layer to obtain a comprehensive evaluation matrix of the target layer, sequencing all the rail connecting schemes, and determining the optimal rail connecting scheme.
Description
Technical Field
The invention relates to the field of railway engineering, in particular to an optimal railway track connecting scheme determining method based on hierarchical analysis and multi-level fuzzy evaluation.
Background
When a newly-built railway is connected with an existing railway, a plurality of rail connection schemes are provided according to the general profile of the existing railway and various influence factors; wherein: each rail connecting scheme has different advantages and disadvantages, and the comparison of the advantages and the disadvantages of each scheme and the determination of the optimal scheme generally adopt the form of an advantage-disadvantage comparison table, but under the conditions of more selection schemes and more influence factors, the comparison of the advantages and the disadvantages has strong subjectivity, the objective and comprehensive evaluation of each scheme cannot be realized, and then the optimal rail connecting scheme is difficult to accurately select; therefore, how to quickly and accurately obtain the optimal rail connection scheme for a plurality of different rail connection schemes becomes an urgent technical problem to be solved.
Disclosure of Invention
Based on the defects that the existing railway track connection scheme is strong in subjectivity and cannot comprehensively evaluate each track connection scheme, the invention provides a method for realizing comprehensive sequencing of a plurality of track connection schemes by adopting a mode of combining hierarchical analysis and multi-level fuzzy comprehensive evaluation aiming at the plurality of railway track connection schemes so as to obtain the track connection scheme with the highest reference value.
The technical scheme adopted by the invention for solving the technical problems is as follows: a railway track connecting scheme determining method based on hierarchical analysis and multi-level fuzzy evaluation comprises the following steps:
s1, determining a plurality of rail connecting schemes according to the existing railway profile, wherein the number of the rail connecting schemes is k, and k is more than 1;
s2, comprehensively comparing the plurality of rail connecting schemes determined in the step S1, and dividingAnalyzing and determining w influence factors influencing the selection of the rail connecting scheme, wherein w is more than 1, and establishing a hierarchical analysis model; the hierarchical analysis model consists of a target layer, a rule layer and an index layer; wherein: the factors of the criterion layer are as follows: a. the1、...、Ai、...、Am(ii) a The index layer sets of factors corresponding to the factors of the criterion layer are as follows: a. the11、...、A1j、...、A1n，...，Ai1、...、 Aij、...、Ain，...，Am1、...、Amj、...、Amn(ii) a Wherein: m is the total number of the influencing factors of the criterion layer, and n is the total number of the influencing factors of each group of the index layer; i is more than or equal to 1, j is more than or equal to 1; w is equal to the sum of all elements of the index layer, i.e.:
s3, according to the layer analysis model determined in step S2, aligning each factor structure of the layer to judge the matrix P, P ═ aij)m×m，aij≥0，aji＝1/(aij)，(i，j＝1，2，...，m)；aijIs aiTo ajRelative importance value of; and constructing a judgment matrix P for each group of elements corresponding to each factor of the criterion layer in the index layer in sequence in the same wayi (i＝1，2，、，m)；
S4, calculating the weight t of each element in the criterion layer judgment matrix Pi(ii) a And calculating to obtain a lower layer index judgment matrix P corresponding to each factor of the criterion layeriWeight T of each element ini；
S5, determining matrix P for criterion layer and each determination matrix P for index layer according to the weight of each element in criterion layer and index layer determined in step S4iCarrying out consistency check, and when the consistency index CI is less than 0.1 and the check coefficient CR is less than 0.1, determining that the judgment matrix passes the consistency check; if the consistency requirements are not met, reconstructing the judgment matrixes until the consistency indexes and the check coefficients of all the judgment matrixes are less than 0.1;
s6, multiplying the weight of each element in the criterion layer by the weight of each element in the index layer under the criterion layer element to obtain the total weight T of each element in the index layeri'; wherein: t isi′＝ti·Ti；
S7, normalizing the w elements of each track connecting scheme respectively based on the k track connecting schemes determined in the step S1 and the w influence factors which influence the selection of the track connecting schemes determined in the step S2, and constructing a fuzzy comprehensive evaluation table;
s8, according to the fuzzy comprehensive evaluation table obtained in the step S7, transversely comparing the lower-layer indexes corresponding to the factors in the k track connecting scheme criterion layers to construct an index layer fuzzy evaluation matrix:
wherein: k is the number of the track connecting schemes, and m is the total number of the influencing factors of the criterion layer; n is the total number of each group of factors of the index layer;
then, fuzzy evaluation matrix C of each index layeriAnd the total weight T of each factor of the index layer in the matrixi' a join operation is performed, namely: t isi′·CiObtaining all fuzzy analysis matrixes of the lower-layer indexes of the criterion layer for transverse comparison, and constructing all the obtained fuzzy analysis matrixes into a comprehensive evaluation matrix V of the criterion layer;
s9, combining the criterion layer comprehensive evaluation matrix V obtained in the step S8 with the weight t of each element in the criterion layeriAnd calculating to obtain a target layer comprehensive evaluation matrix B, wherein B is tiV; and finally, determining the sequencing of the rail connecting scheme according to the numerical sequencing in the target layer comprehensive evaluation matrix B, wherein: the track connecting scheme corresponding to the maximum value is as follows: and (5) optimizing a railway track connection scheme.
Further, in the hierarchical analysis model in step S2, the selection of the joining scheme is used as a target layer, and the economic and technical rationality, the existing railway influence and the location condition are used as criterion layers; the index layers of the economic and technical rationality are as follows: line length, engineering investment and implementability; the index layers of the existing railway influence are as follows: effects on existing stations, effects on existing positive lines, and effects on transport organization; the index layer of the location condition is the occupied area, the influence on a water source protection area and the matching with a logistics park.
Further, in step S3, in the determination matrix, the importance between each layer element is compared pairwise by using a nine-scale method, and the quantization values are as follows: the equally important quantization value is 1, the slightly important quantization value is 3, the strongly important quantization value is 5, the strongly important quantization value is 7, the extremely important quantization value is 9, and the median of two adjacent judgments is 2, 4, 6, 8.
Further, in step S5, the calculation formula of the consistency index CI is:
the test coefficient CR is calculated as:
wherein: lambda [ alpha ]maxThe maximum eigenvalue is obtained by calculation according to the weight of each element in the judgment matrix; RI is a random consistency index; m is the number of elements in the decision matrix.
Further, step S7 is specifically: the w influencing factors corresponding to each track connecting scheme are respectively as follows: the method comprises the following steps of (1) line length, engineering investment, implementability, influence on an existing station, influence on an existing positive line, influence on a transportation organization, occupied area, influence on a water source protection area and matching with a logistics park; wherein: the line length, the engineering investment and the floor area in each scheme are calculated according to the actual scheme conditions, the evaluation values of the other elements are determined according to the evaluation standard, then the fuzzy vector of each factor in each scheme is calculated by using a normalization method, and a fuzzy comprehensive evaluation table is constructed and obtained.
Compared with the prior art, the invention has the following advantages and effects:
aiming at a plurality of railway track connection schemes, the invention adopts an analytic hierarchy process to obtain the weight of each influence element of a criterion layer and the total weight of each element of an index layer by constructing a judgment matrix for each element influencing the selection of the track connection scheme, then adopts a multi-level fuzzy comprehensive evaluation method to transversely compare each element influencing the selection of the track connection scheme to obtain a criterion layer comprehensive evaluation matrix and a target layer comprehensive evaluation matrix, and takes the evaluation result with the highest evaluation value in the target layer comprehensive evaluation matrix as the optimal track connection scheme; compared with the existing method for determining the railway track connection scheme, the method effectively quantifies the original qualitative comparison and selection method, can effectively sequence each track connection scheme, enables the selection of the track connection scheme to be more visual and effective, enables the constructed evaluation and screening system to be more scientific, objective and comprehensive, enables the obtained track connection scheme to better conform to the general situation of the actual railway, has more reference value, and has the minimum influence on the existing railway.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without paying creative efforts.
Fig. 1 is a flowchart of a method for determining a railway track joining scheme based on hierarchical analysis and multi-level fuzzy evaluation according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a hierarchical analysis model according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples, which are illustrative of the present invention and are not to be construed as being limited thereto.
Example 1: as shown in fig. 1 to 2, a method for determining a railway track-connecting scheme based on hierarchical analysis and multi-level fuzzy evaluation specifically includes the following steps:
s1, determining a plurality of rail connecting schemes of the proposed yard and the existing railway according to the general profile of the existing railway, wherein the number of the rail connecting schemes is k, and k is more than 1;
s2, comprehensively comparing a plurality of rail connecting schemes, analyzing and determining w influence factors influencing the selection of the rail connecting schemes, wherein w is larger than 1, and establishing a hierarchical analysis model; the hierarchical analysis model consists of a target layer, a criterion layer and an index layer; wherein: the factors of the criterion layer are as follows: a. the1、...、Ai、...、Am(ii) a The index layer sets of factors corresponding to the factors of the criterion layer are as follows: a. the11、...、A1j、...、A1n，...，Ai1、...、Aij、...、Ain，...， Am1、...、Amj、...、Amn(ii) a Wherein: m is the total number of the influencing factors of the criterion layer, and n is the total number of the influencing factors of each group of the index layer; i is more than or equal to 1, and j is more than or equal to 1; w is equal to the sum of all elements of the index layer, i.e.:
s3, according to the layer analysis model determined in step S2, aligning each factor structure of the layer to judge the matrix P, P ═ aij)m×m，aij≥0，aji＝1/(aij)，(i，j＝1，2，...，m)；aijIs aiTo ajRelative importance value of; and constructing a judgment matrix P for each group of elements corresponding to each factor of the criterion layer in the index layer in sequence in the same wayi (i＝1，2，、，m)；
S4, calculating the weight t of each element in the criterion layer judgment matrix PiAnd calculating a lower index judgment matrix P corresponding to each factor of the criterion layeriWeight T of each element ini；
S5, determining matrix P for criterion layer and each determination matrix P for index layer according to the weight of each element in criterion layer and index layer determined in step S4iCarrying out consistency check, and when the consistency index CI is less than 0.1 and the check coefficient CR is less than 0.1, determining that the judgment matrix passes the consistency check; if the consistency requirements are not met, reconstructing the judgment matrixes until the consistency indexes and the check coefficients of all the judgment matrixes are less than 0.1;
s6, multiplying the weight of each element in the criterion layer by the weight of each element in the lower index layer corresponding to the element in the criterion layer to obtain the total weight T of each element in the index layeri'; wherein: t isi′＝ti·Tx；
S7, normalizing the w elements of each track connecting scheme respectively based on the k track connecting schemes determined in the step S1 and the w influence factors which influence the selection of the track connecting schemes determined in the step S2, and constructing a fuzzy comprehensive evaluation table;
s8, according to the fuzzy comprehensive evaluation table obtained in the step S7, transversely comparing all lower-layer indexes corresponding to all factors in the k track connecting scheme criterion layers, and constructing an index layer fuzzy evaluation matrix, wherein the matrix expression is as follows:
wherein: k is the number of the track connecting schemes, and m is the total number of the influencing factors of the criterion layer; n is the total number of the influence factors of each group of the index layer;
then, fuzzy evaluation matrix C of each index layeriAnd the total weight T of each factor of the index layer in the matrixi' a join operation is performed, namely: t isi′·CiObtaining all fuzzy analysis matrixes of the lower-layer indexes of the criterion layer for transverse comparison, and constructing all the obtained fuzzy analysis matrixes into a comprehensive evaluation matrix V of the criterion layer;
s9, combining the criterion layer comprehensive evaluation matrix V obtained in the step S8 with the weight t of each element in the criterion layeriAnd calculating to obtain a target layer comprehensive evaluation matrix B, wherein B is tiV; and finally, determining the sequencing of the track connecting scheme according to the numerical sequencing in the target layer comprehensive evaluation matrix B, wherein: maximum number ofThe track connecting scheme corresponding to the value is as follows: and (5) optimizing a railway track connection scheme.
Specifically, in this embodiment 1, the effectiveness of the method for determining a railway track connection scheme of the present invention is described by using an example of the railway logistics center of kunyu and kunyu station track connection;
according to the general view of the existing railway, the invention researches 5 track connection schemes (namely (scheme I, scheme II, scheme III, scheme IV and scheme V) in total, comprehensively compares the schemes to determine that 9 elements influencing the selection of the track connection scheme are 9, hierarchically analyzes the 9 elements influencing the track connection scheme to establish an evaluation index system, and the hierarchical structure model is divided into three layers which are respectively a target layer, a criterion layer and an index layer, as shown in figure 2, the selection of the track connection scheme is taken as a target layer A, and the economic and technical rationality A is taken as a1Existing railway impact A2And location condition A3Is a criterion layer; economic and technical rationality A1The index layers are as follows: line length A11Project investment A12And implementability A13(ii) a Existing railway impact A2The index layers are as follows: influence on existing stations A21Influence of the line on the existing Positive line A22And transport organization A23The influence of (a); zone bit condition A3The index layer is a floor area A31Influence on Water conservation area A32And compatibility with a logistics park A33。
Comparing every two influencing factors through corresponding evaluation standards, determining the importance ratio among all the factors, and obtaining a judgment matrix, thereby distributing weights to the factors among layers and in the layers, and obtaining the corresponding weight of each evaluation index; wherein: the 9 element importance levels and their assignments are as follows: the equivalent important quantization value is 1, the slightly important quantization value is 3, the stronger important quantization value is 5, the strongly important quantization value is 7, the extreme important quantization value is 9, and the intermediate values of two adjacent judgments are 2, 4, 6 and 8; the specific weights are shown in the decision matrices in tables 1 to 4.
Table 1: A-A1-3Judgment matrix (criterion layer)
| A | A1 | A2 | A3 | Weight (t)i) |
| A1 | 1 | 3 | 5 | 0.65 |
| A2 | 0.33 | 1 | 2 | 0.23 |
| A3 | 0.2 | 0.5 | 1 | 0.12 |
According to the following formula for A-A1-3And (3) judging the matrix to carry out consistency check:
wherein: m is the number of elements in the judgment matrix; RI is a random consistency index and is obtained by searching an average random consistency index table; lambda [ alpha ]maxThe maximum eigenvalue is obtained by calculation according to the weight of each element in the judgment matrix; A-A1-3In the judgment matrix, λ max is 3.001; CI ═ 0.0005 < 0.1, CR ═ 0.0007 < 0.1, by the consistency test.
Table 2: a. the1-A11-13Judgment matrix (A)1Corresponding index layer)
| A1 | A11 | A12 | A13 | Weight (T)i′) |
| A11 | 1 | 0.33 | 3 | 0.26 |
| A12 | 3 | 1 | 5 | 0.64 |
| A13 | 0.33 | 0.2 | 1 | 0.10 |
A1-A11-13Judging in the matrix: λ max is 3.035, CI is 0.017 < 0.1, and CR is 0.030 < 0.1, as determined by identity.
Table 3: a. the2-A21-23Judgment matrix (A)2Corresponding index layer)
| A2 | A21 | A22 | A23 | Weight (T)i′) |
| A21 | 1 | 0.5 | 0.2 | 0.12 |
| A22 | 2 | 1 | 0.33 | 0.23 |
| A23 | 5 | 3 | 1 | 0.65 |
A2-A21-23Judging in the matrix: λ max ═ 3.001, CI ═ 0.0005 < 0.1, and CR ═ 0.0007 < 0.1, by consistency test.
Table 4: a. the3-A31-33Judgment matrix (A)3Corresponding index layer)
| A3 | A31 | A32 | A33 | Weight (T)i′) |
| A31 | 1 | 0.25 | 3 | 0.20 |
| A32 | 4 | 1 | 5 | 0.70 |
| A33 | 0.33 | 0.2 | 1 | 0.10 |
A3-A31-33Judging in the matrix: λ max is 3.097, CI 0.049 < 0.1, CR 0.084 < 0.1, by identity test.
Comprehensively analyzing according to the weight coefficients of the factors in each layer and each layer, and determining the total weight T of the nine elements influencing the track connecting schemei′(Ti′＝ti·Ti) See table 5 for distribution of index weight of the track-joining scheme.
Table 5: rail connecting scheme index weight distribution table
Furthermore, the number of railway track connection schemes is 5, and a fuzzy comprehensive evaluation table of all factors is obtained after normalization processing is carried out according to 9 influencing factors of each scheme.
Table 6: fuzzy comprehensive evaluation table for 5 rail connecting schemes
| Factors of the fact | Scheme I | Scheme II | Scheme III | Scheme IV | Scheme V |
| A11 | 0.64 | 0.58 | 0.93 | 0.93 | 0.93 |
| A12 | 0.76 | 0.64 | 0.87 | 0.86 | 0.86 |
| A13 | 0.60 | 0.20 | 0.80 | 0.40 | 1.00 |
| A21 | 0.20 | 0.20 | 1.00 | 1.00 | 1.00 |
| A22 | 1.00 | 1.00 | 0.40 | 0.20 | 0.60 |
| A23 | 0.60 | 0.60 | 0.80 | 1.00 | 0.20 |
| A31 | 0.67 | 0.38 | 0.98 | 0.98 | 0.98 |
| A32 | 0.20 | 1.00 | 0.80 | 0.80 | 0.80 |
| A33 | 0.60 | 0.60 | 0.80 | 1.00 | 0.60 |
Wherein: line length a in each scheme11Engineering investment A12An area A of land31According to the actual prescriptionAnd calculating the case conditions, determining the evaluation values of the other elements according to the evaluation criteria, calculating the fuzzy vector of each factor in each scheme by using a normalization method, and constructing to obtain a fuzzy comprehensive evaluation table.
Specifically, the influencing factors are: implementability A13Influence on existing station A21Influence of existing Positive line A22Effects on transport organization A23Influence on Water conservation area A32And compatibility with a logistics park A33The evaluation values of (a) were obtained by the following evaluation criteria:
implementability A13: when in construction, the existing Kunmu station is subjected to small influence of marking for 5 points, small influence of marking for 4 points, medium influence of marking for 3 points, large influence of marking for 2 points and large influence of marking for 1 point;
influence on existing stations A21: connecting rails at an existing station, performing transformation on a throat area of the existing station for 1 minute, simply connecting rails at the existing station, performing transformation on the throat area for 3 minutes, and not performing transformation on the throat area for 5 minutes;
influence of alignment A22: the track is not hit for 5 minutes when the track is connected on the main line, only the switch is inserted on the main line for 4 minutes, the line is hit for 3 minutes when the main line is provided, the station is opened on the main line without softening the slope of the main line for 2 minutes, the station is opened on the main line, and the slope of the softened main line is hit for 1 minute;
effect on transport organization A23: the method has the advantages that 5 points are scored with little influence on the existing karting and line transportation organization, 4 points are scored with little influence, 3 points are scored with medium influence, 4 points are scored with large influence, and 1 point is scored with large influence;
effect on Water conservation area A32: marking the water source protection area for 5 minutes without influence, marking the traveling line into a buffer area for 4 minutes, marking the traveling line into an experimental area for 3 minutes, marking the traveling line into a core area for 2 minutes, and marking the cargo yard into a core area for 1 minute;
compatibility with logistic park A33: the method has the advantages that the method can be well matched with a logistics park for 5 points, better for 4 points, medium for 3 points, not better for 2 points and not better for 1 point.
Further, according to fuzzy comprehensive evaluation tables of the track connecting schemes (I, II, III, IV and V), economic and technical rationality of each scheme criterion layer, existing railway influence and lower layer indexes corresponding to location conditions are transversely compared respectively to form fuzzy evaluation matrixes of each single factor of the criterion layer:
fuzzy evaluation matrix C1、C2、C3Respectively with the total weight T of each factor of the index layer in the matrixi' carry out the sum operation, i.e.: t isi′·CiObtaining the fuzzy analysis matrix D of the lower layer indexes of the criterion layer of the transverse comparison1、D2And D3Constructing each obtained fuzzy analysis matrix into a criterion layer comprehensive evaluation matrix V;
finally, combining the criterion layer comprehensive evaluation matrix V with the criterion layerWeight t of each elementiAnd calculating to obtain a target layer comprehensive evaluation matrix B, wherein B is ti·V；
The estimated values of the five schemes are respectively scheme III, scheme IV, scheme V, scheme I and scheme II from large to small, and the estimated value of the scheme III is the highest and is 0.422, so that the scheme III can be judged to be the optimal scheme in the railway track connecting scheme.
In conclusion, in the method for determining the railway track-joining scheme, 9 influence factors which influence the selection of the railway track-joining scheme, such as line length, engineering investment, feasibility, influence on an existing station, influence on an existing positive line, influence on a transportation organization, floor area, influence on a water source protection area, matching with a logistics park and the like are comprehensively considered, an evaluation index system is established by layering the 9 factors by using an analytic hierarchy process, the two factors are compared with each other through analysis of relevant experts, the ratio of importance among the factors is determined to obtain a judgment matrix, so that the distribution weights of the factors among layers and in the layers are obtained, the corresponding weights of the evaluation indexes are obtained, then a multi-layer fuzzy comprehensive evaluation method is adopted to transversely compare the factors of each scheme to obtain a fuzzy evaluation matrix, and thus the comprehensive evaluation of each scheme is obtained, the evaluation result with the highest evaluation value is selected as the optimal rail connecting scheme, so that the defects that the selection of indexes is not comprehensive enough, part of indexes are not practical and have poor operability, the subjectivity is strong, and each rail connecting scheme cannot be comprehensively evaluated when the conventional railway rail connecting scheme is selected are effectively overcome.
In addition, it should be noted that the specific embodiments described in the present specification may differ in the components, the shapes of the components, the names of the components, and the like. All equivalent or simple changes of the structure, the characteristics and the principle of the invention which are described in the patent conception of the invention are included in the protection scope of the patent of the invention. Various modifications, additions and substitutions for the specific embodiments described may be made by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
Claims (5)
1. A railway track connecting scheme determining method based on hierarchical analysis and multi-level fuzzy evaluation is characterized by comprising the following steps:
s1, determining a plurality of rail connecting schemes according to the existing railway profile, wherein the number of the rail connecting schemes is k, and k is more than 1;
s2, comprehensively comparing the plurality of rail connecting schemes determined in the step S1, analyzing and determining w influence factors influencing the selection of the rail connecting schemes, wherein w is larger than 1, and establishing a hierarchical analysis model; the hierarchical analysis model consists of a target layer, a criterion layer and an index layer; wherein: the factors of the criterion layer are as follows: a. the1、…、Ai、…、Am(ii) a The index layer sets of factors corresponding to the factors of the criterion layer are as follows: a. the11、…、A1j、…、A1n，…，Ai1、…、Aij、…、Ain，…，Am1、…、Amj、…、Amn(ii) a Wherein: m is the total number of the influencing factors of the criterion layer, and n is the total number of the influencing factors of each group of the index layer; i is more than or equal to 1, and j is more than or equal to 1; w is equal to the sum of all elements of the index layer, i.e.:
s3, according to the layer analysis model determined in step S2, aligning each factor structure of the layer to judge the matrix P, P ═ aij)m×m，aij≥0，aji＝1/(aij)，(i，j＝1,2，…，m)；aijIs aiTo ajRelative importance value of; and constructing a judgment matrix P for each group of elements corresponding to each factor of the criterion layer in the index layer in sequence in the same wayi(i＝1,2，、，m)；
S4, calculating the weight t of each element in the criterion layer judgment matrix Pi(ii) a And areCalculating to obtain a lower layer index judgment matrix P corresponding to each factor of the criterion layeriWeight T of each element ini；
S5, determining matrix P for criterion layer and each determination matrix P for index layer according to the weight of each element in criterion layer and index layer determined in step S4iPerforming consistency check, when the consistency index CI is<0.1 and test coefficient CR<0.1, the judgment matrix is considered to pass the consistency test; if the consistency requirements are not met, reconstructing the judgment matrixes until the consistency indexes and the check coefficients of all the judgment matrixes are less than 0.1;
s6, multiplying the weight of each element in the criterion layer by the weight of each element in the index layer under the criterion layer element to obtain the total weight T of each element in the index layeri'; wherein: t isi′＝ti·Ti；
S7, normalizing the w elements of each track connecting scheme respectively based on the k track connecting schemes determined in the step S1 and the w influence factors which influence the selection of the track connecting schemes determined in the step S2, and constructing a fuzzy comprehensive evaluation table;
s8, transversely comparing the lower-layer indexes corresponding to each factor in the k track connecting scheme criterion layers according to the fuzzy comprehensive evaluation table obtained in the step S7, and constructing an index layer fuzzy evaluation matrix Ci：
Wherein: k is the number of the track connecting schemes, and m is the total number of the influencing factors of the criterion layer; n is the total number of each group of factors of the index layer;
then, fuzzy evaluation matrix C of each index layeriAnd the total weight T of each factor of the index layer in the matrixi' perform a join operation, i.e.: t isi′·CiObtaining all fuzzy analysis matrixes of the lower-layer indexes of the criterion layer for transverse comparison, and constructing all the obtained fuzzy analysis matrixes into a comprehensive evaluation matrix V of the criterion layer;
S9，combining the criterion layer comprehensive evaluation matrix V obtained in the step S8 with the weight t of each element in the criterion layeriAnd calculating to obtain a target layer comprehensive evaluation matrix B, wherein B is tiV; and finally, determining the sequencing of the track connecting scheme according to the numerical sequencing in the target layer comprehensive evaluation matrix B, wherein: the track connecting scheme corresponding to the maximum value is as follows: and (5) optimizing a railway track connection scheme.
2. The method for determining a railway track joining scheme based on hierarchical analysis and multi-level fuzzy evaluation according to claim 1, wherein in the hierarchical analysis model of step S2, the selection of the track joining scheme is taken as a target layer, and economic and technical rationality, existing railway influence and location conditions are taken as criterion layers; the index layers of the economic and technical rationality are as follows: line length, engineering investment and implementability; the index layers of the existing railway influence are as follows: effects on existing stations, effects on existing positive lines, and effects on transport organization; the index layer of the location condition is the floor area, the influence on the water source protection area and the matching with the logistics park.
3. The method for determining a railway track-joining scheme based on hierarchical analysis and multi-level fuzzy evaluation as claimed in claim 1, wherein in step S3, the importance between each level element is compared pairwise by using a nine-scale method in the judgment matrix, and the quantitative values are as follows: the equally important quantization value is 1, the slightly important quantization value is 3, the strongly important quantization value is 5, the strongly important quantization value is 7, the extremely important quantization value is 9, and the median of two adjacent judgments is 2, 4, 6, 8.
4. The method for determining a railway track-contacting scheme based on hierarchical analysis and multi-level fuzzy evaluation as claimed in claim 1, wherein in step S5, the calculation formula of the consistency index CI is:
the test coefficient CR is calculated as:
wherein: lambda [ alpha ]maxThe maximum eigenvalue is obtained by calculation according to the weight of each element in the judgment matrix; RI is a random consistency index; m is the number of elements in the decision matrix.
5. The method for determining a railway track contacting scheme based on hierarchical analysis and multi-level fuzzy evaluation according to claim 2, wherein step S7 specifically comprises: the w influencing factors corresponding to each track connecting scheme are respectively as follows: the system comprises the following components, namely, the line length, the engineering investment, the implementability, the influence on an existing station, the influence on an existing positive line, the influence on a transportation organization, the occupied area, the influence on a water source protection area and the matching with a logistics park; wherein: the line length, the engineering investment and the floor area in each scheme are calculated according to the actual scheme conditions, the evaluation values of the other elements are determined according to the evaluation standard, then the fuzzy vectors of all the factors in each scheme are calculated by using a normalization method, and a fuzzy comprehensive evaluation table is constructed and obtained.
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