CN115358637A - Method for evaluating potential safety hazard easiness of railway external environment color steel house - Google Patents

Method for evaluating potential safety hazard easiness of railway external environment color steel house Download PDF

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CN115358637A
CN115358637A CN202211276239.2A CN202211276239A CN115358637A CN 115358637 A CN115358637 A CN 115358637A CN 202211276239 A CN202211276239 A CN 202211276239A CN 115358637 A CN115358637 A CN 115358637A
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color steel
potential safety
safety hazard
steel house
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宋国策
周文明
褚文君
郭湛
王高磊
甘俊
张冠军
李平苍
翟旭
张�浩
胡朝鹏
赵振洋
王大刚
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China State Railway Group Co Ltd
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Abstract

The invention relates to a method for evaluating potential safety hazard proneness of a color steel room in a railway external environment, which belongs to the field of railway external environment renovation and comprises the following steps of S1 research area basic data acquisition and standardization processing, S2 construction of a potential safety hazard proneness evaluation index set of the color steel room, S3 numerical processing of evaluation indexes, S4 group decision-making analytic hierarchy process calculation of evaluation index weight, S5 color steel room monomer potential safety hazard proneness calculation and S6 color steel room potential safety hazard proneness grade division. The system of the invention combs the potential safety hazard risk brought by the self attribute and the environmental influence of the railway external environment color steel house, has clear structure and comprehensive evaluation content, and more comprehensively reflects the uncertain relation contained in the evaluation indexes by using a group decision method, so that the evaluation result is more reliable, and the system provides decision support for further developing the subsequent formulation and treatment work of the railway external environment color steel house danger removal reinforcement scheme.

Description

Method for evaluating potential safety hazard easiness of railway external environment color steel house
Technical Field
The invention belongs to the technical field of railway external environment improvement, and particularly relates to a method for evaluating potential safety hazard proneness of a railway external environment color steel house.
Background
With the continuous increase of railway operation mileage in China, the problem that human activities around railways affect the railway operation safety is increasingly highlighted, and a large number of color steel houses distributed on two sides of railways become one of the types of potential safety hazards of railway external environments. The color steel house has the characteristics of low cost and convenience in construction, is widely applied to temporary houses in factory warehouses and construction sites and simple houses for agriculture, forestry, animal husbandry and fishery, and is very easy to scrape down within railway limits under the condition of strong wind weather due to the reason of random construction, overhaul waste and the like, railway equipment facilities are damaged, large-area delay and shutdown of trains are caused, and particularly serious consequences are very easy to cause when the color steel house collides with a high-speed running train.
In order to strengthen the safety management of the external environment of the railway, the safety risk of the external environment of the railway is effectively prevented and controlled. In 2020 and 5 months, the safety hidden danger problem of the color steel houses along the railway is emphasized by united issuing and implementing a high-speed railway safety protection and management method by multiple departments in China and issuing and implementing a railway external environment safety management method by national railway group limited company in China in the same year and 10 months. At present, the research on the risk evaluation of the potential safety hazard of the external environment of the railway is still in a starting stage, and a risk susceptibility evaluation system for color steel houses on two sides of the railway is not established, so that a relatively complete and scientific safety evaluation method capable of systematically and reasonably evaluating the potential safety hazard of the color steel houses in the external environment of the railway is urgently needed.
Disclosure of Invention
The invention provides a method for evaluating the potential safety hazard proneness of a railway external environment color steel house, aiming at solving the technical problems in the known technology.
The invention comprises the following technical scheme: a potential safety hazard susceptibility evaluation method for a railway external environment color steel house comprises the following steps: s1, acquiring geographic information data of a color steel house in a research area; s2, constructing an evaluation index set of potential safety hazard proneness of the color steel house; s3, carrying out numerical processing on the evaluation indexes; s4, calculating evaluation index weight by using a group decision analytic hierarchy process; s5, calculating the potential safety hazard easiness of the color steel house monomer; and S6, grading the potential safety hazard proneness grades of the color steel house.
Furthermore, in the S1, QGIS software is used for completing vectorization collection of color steel houses within a range of 500 meters on two sides of a railway, and a vector attribute table of the color steel houses is filled in through aerial photography remote sensing interpretation, field survey, data query and geographic information system space analysis, so that data entry of potential safety hazard susceptibility evaluation indexes is completed.
Further, the S2 comprises two index layers in total, wherein the index layer in total has 6 items, and is expressed as a
Figure 906796DEST_PATH_IMAGE001
}，t = 1,2,
Figure 355095DEST_PATH_IMAGE002
T, comprising: characteristics of the system, spatial relationships, existing statistics, wind factors, terrain factors and surface factors; the secondary index layer is subordinate to the primary index layer, and has 22 items in total, and is represented as a hard page
Figure 665990DEST_PATH_IMAGE003
}，j = 1,2,
Figure 592358DEST_PATH_IMAGE002
J, J is the number of the second level index contained under the corresponding first level index layer.
Further, S3 is to carry out numerical quantification on the secondary index attribute of the color steel house related in S1 according to the degree of closeness with the hidden danger and easiness, and the value range of the quantified value A of the easiness degree is more than or equal to 0 and less than or equal to 1.
Further, the specific step S3 includes: s3-1: classifying or grading the secondary indexes, and classifying each secondary index into x classes or x grades, wherein x is more than or equal to 2; s3-2: classifying or grading each secondary index, and performing forward ranking of potential safety hazard easiness according to historical data of potential safety hazard accidents of the color steel houses; s3-3: calculating a degree of vulnerability quantization value a = (x + 1-i)/(x + 1) for each class or class, i =1,2,
Figure 141151DEST_PATH_IMAGE004
x and i are the ranking of the hidden danger and the easiness of the ranking of the class or the class; s3-4: manually adjusting the easy-to-send degree quantized value A according to historical data of potential safety hazard accidents of the color steel house to form an evaluation index digitalized table, which is mainly used for evaluating the safety hazard accidents of the color steel houseAdjust the secondary index to "discard or not
Figure 494772DEST_PATH_IMAGE005
"," with or without reinforcement
Figure 558543DEST_PATH_IMAGE006
"," functional use
Figure 491864DEST_PATH_IMAGE007
"quantified value of incidence degree corresponding to all classes, and secondary index" wind direction of annual maximum wind speed
Figure 691901DEST_PATH_IMAGE008
"and" maximum wind direction over the years
Figure 482002DEST_PATH_IMAGE009
The value of the susceptibility to "downwind" in the "classification" was quantified, and the final data is shown in table 1.
TABLE 1 evaluation index numeralization table
Figure 970753DEST_PATH_IMAGE010
Figure 238923DEST_PATH_IMAGE011
Figure 559046DEST_PATH_IMAGE012
The projected area and the height of the house in table 1 are normalized according to the area or height of the color steel house involved in the project, and the formula is as follows:
Figure 520048DEST_PATH_IMAGE013
in the formula (I), the compound is shown in the specification,
Figure 496095DEST_PATH_IMAGE014
indicates the area or height of the ith color steel room,
Figure 302377DEST_PATH_IMAGE015
represents the minimum area or height of the color steel house in the project,
Figure 742585DEST_PATH_IMAGE016
representing the maximum area or height of the steel house in the project,
Figure 608910DEST_PATH_IMAGE017
the sequencing size of the area or height of the ith color steel room is represented, threshold division is carried out according to three grades, and the value is more than or equal to 0
Figure 72253DEST_PATH_IMAGE017
Less than 0.33 is small or low, and less than or equal to 0.33
Figure 682225DEST_PATH_IMAGE017
< 0.67 is medium, 0.67 <
Figure 976941DEST_PATH_IMAGE017
Greater or higher than 1.
The throat effect is quantitatively expressed by the valley integrated eigenvalues in table 1:
Figure 279746DEST_PATH_IMAGE018
wherein B is the average valley width and is calculated by 1/2 of the average width of the ridge lines at two sides of the canyon; l is the valley length, calculated as the length of the valley bottom line in the canyon; d is the average valley depth, calculated as the elevation from the valley bottom at the average elevation of the ridges on both sides of the canyon.
Further, the specific step S4 includes: s4-1: constructing an expert judgment matrix set; s4-2: calculating the weight coefficient of each index layer; s4-3: using maximum feature root
Figure 964805DEST_PATH_IMAGE019
Calculating a consistency index
Figure 378469DEST_PATH_IMAGE020
：
Figure 262111DEST_PATH_IMAGE021
And further calculates the consistency ratio
Figure 1397DEST_PATH_IMAGE022
(ii) a S4-4: aggregating and solving the index weight; the index weight calculation formula is as follows:
Figure 173753DEST_PATH_IMAGE023
；
in the formula (I), the compound is shown in the specification,
Figure 125528DEST_PATH_IMAGE024
the weight of the index is represented by,
Figure 394835DEST_PATH_IMAGE025
、
Figure 242706DEST_PATH_IMAGE026
the weighting coefficient and the index weight of the evaluation result of the kth expert are respectively, and u is the total number of the experts involved in the evaluation.
Further, the expert in S4-1 adopts a 1-9 scale method, as shown in Table 2, the importance of each two indexes is compared, and a judgment matrix is given
Figure 699095DEST_PATH_IMAGE027
，
Figure 720140DEST_PATH_IMAGE028
，
k = 1,2,
Figure 516058DEST_PATH_IMAGE002
K in the formulamIn order to evaluate the number of factors,kthe number of the experts participating in the evaluation.
TABLE 2 Scale scoring basis-9
Figure 331567DEST_PATH_IMAGE029
Further, the S4-2 specifically includes:
(1) calculating the product of row elements of the decision matrix
Figure 540832DEST_PATH_IMAGE030
，
Figure 99989DEST_PATH_IMAGE031
(i,j = 1,2,
Figure 15993DEST_PATH_IMAGE002
,n)；
(2) Calculating the row product
Figure 2403DEST_PATH_IMAGE030
The root of the square of degree n of (c),
Figure 433384DEST_PATH_IMAGE032
(i = 1,2,
Figure 733916DEST_PATH_IMAGE002
,n)；
(3) the weight of each index is obtained by adopting normalization processing,
Figure 566743DEST_PATH_IMAGE033
(i = 1,2,
Figure 724054DEST_PATH_IMAGE002
,n)；
(4) the root of the largest feature is calculated,
Figure 642332DEST_PATH_IMAGE034
。
further, the S4-3 specifically includes:
(1) using maximum feature root
Figure 746554DEST_PATH_IMAGE035
Calculating outIndex of consistency
Figure 433887DEST_PATH_IMAGE036
：
Figure 762101DEST_PATH_IMAGE037
(2) From the average random consistency index table, as shown in Table 3, the consistency ratio is calculated for RI under n
Figure 902095DEST_PATH_IMAGE022
：
TABLE 3 table of indexes of average random consistency
Figure 810008DEST_PATH_IMAGE038
Figure 351848DEST_PATH_IMAGE039
Further, the S4-4 specifically includes: calculating to obtain the weight coefficient of each factor after the consistency check of the expert individual judgment matrix is passed, and meanwhile, obtaining the consistency ratio of the judgment matrix according to the consistency ratio
Figure 116542DEST_PATH_IMAGE040
Calculating weights of experts
Figure 743832DEST_PATH_IMAGE041
Are combined with each other
Figure 986594DEST_PATH_IMAGE041
Normalization processing is carried out to obtain the proportion weight of each expert
Figure 586203DEST_PATH_IMAGE042
。
Figure 256219DEST_PATH_IMAGE043
In the formula
Figure 636385DEST_PATH_IMAGE044
Taking the value as 10, and obtaining the weight of the expert proportion
Figure 417259DEST_PATH_IMAGE045
And corresponding criterion weight
Figure 136953DEST_PATH_IMAGE046
And performing weighting operation to obtain the weight of the evaluation index of the potential safety hazard proneness of the colored steel house in the external environment of the railway.
Figure 977870DEST_PATH_IMAGE047
Further, the calculation formula in S5 is,
Figure 845332DEST_PATH_IMAGE048
(ii) a The above calculation formula can be converted into the following equation set:
Figure 633159DEST_PATH_IMAGE049
in the formula, F represents the easiness of the potential safety hazard of the color steel house in the external environment of the railway, S represents the evaluation attribute data of the easiness of the potential safety hazard of the color steel house,
Figure 4098DEST_PATH_IMAGE050
and (4) representing the weight of the easy-to-occur evaluation index of the potential safety hazard of the color steel house.
Further, in S6, a value interval of the potential safety hazard proneness value of the color steel house is utilized, normalization processing is performed on the calculated proneness value, and five-level division is performed on the calculated proneness of the color steel house by using an equidistant classification method, specifically, the division is as follows:
low [0,0.2): the potential safety hazard accident occurrence possibility of the color steel house is low, and the urgency of reinforcement or removal is small;
medium low [0.2,0.4): the potential safety hazard accident occurrence probability of the color steel house is low, and reinforcement treatment is recommended;
medium [0.4,0.6): the potential safety hazard accident occurrence possibility of the color steel house is moderate, and reinforcement treatment is required;
medium high [0.6,0.8): the potential safety hazard accident occurrence possibility of the color steel house is high, and reinforcement treatment or dismantling treatment is required;
high [0.8,1]: the potential safety hazard accident of the color steel house has high possibility and must be dismantled.
The invention has the advantages and positive effects that:
1. the system of the invention combs the potential safety hazard risk brought by the self attribute and the environmental influence of the railway external environment color steel house, has clear structure and comprehensive evaluation content, and more comprehensively reflects the uncertain relation contained in the evaluation indexes by using a group decision method, so that the evaluation result is more reliable, and the system provides decision support for further developing the subsequent formulation and treatment work of the railway external environment color steel house danger removal reinforcement scheme.
2. The invention creatively combs and quantifies the multi-factor indexes of the railway external environment color steel house, and introduces the calculation method of group decision level analysis to judge the easiness of the potential safety hazard of the color steel house, so that the railway external environment potential safety hazard troubleshooting and governing work is more accurate and efficient.
Drawings
FIG. 1 is a basic flow diagram of the present invention.
FIG. 2 is an index structure model diagram of the railway external environment color steel room potential safety hazard evaluation system of the invention.
Detailed Description
To further clarify the disclosure of the present invention, its features and advantages, reference is made to the following examples taken in conjunction with the accompanying drawings.
Example (b): referring to the attached figures 1-2, the method for evaluating the potential safety hazard of the railway external environment color steel house comprises the following steps: s1, acquiring geographic information data of a color steel house in a research area; s2, constructing an evaluation index set of potential safety hazard proneness of the color steel house; s3, carrying out numerical processing on the evaluation indexes; s4, calculating evaluation index weight by using a group decision analytic hierarchy process; s5, calculating the potential safety hazard easiness of the single color steel house; and S6, grading the potential safety hazard proneness grades of the color steel house.
S1, acquiring geographic information data of a color steel house in a research area: and (3) completing the vectorization collection of color steel rooms within the range of 500 meters on both sides of the railway by using QGIS software, and unifying the data into a shapefile format. The attribute table contains 22 fields, and the secondary evaluation index attributes of the color steel house correspond to each other. Classifying according to first-level evaluation indexes of the potential safety hazards of the color steel house and filling a vector attribute table, and specifically comprising the following steps of:
s1-1: 11 fields under the characteristics of the self are filled in by using aviation image interpretation and site survey with 0.2 meter resolution;
s1-2: filling in a 'plane distance according to a railway center line' and a 'relative height difference according to a railway center line' 2 fields in a spatial relationship through calculation of a spatial distance of a geographic information system, and filling in a 'visibility with the railway center line' field through analysis of visibility by combining a Digital Surface Model (DSM);
s1-3: the historical invasion limit frequency under the existing statistics is filled by counting the invasion limit events of the color steel houses occurring in the buffer area of 1 kilometer around;
s1-4: filling 3 fields under the wind power factor through meteorological data query;
s1-5: filling 3 fields under the terrain factors through calculation of a Digital Elevation Model (DEM);
s1-6: the underlying surface roughness under the surface factors is filled in by judging the geographical position through aerial images.
S2, constructing a railway external environment color steel house potential safety hazard evaluation index system: comprises two index layers as shown in FIG. 2, wherein the index layer has 6 items in total and is represented as a
Figure 281495DEST_PATH_IMAGE051
}，t = 1,2,
Figure 308357DEST_PATH_IMAGE004
T, T =6, comprising: its own features: (
Figure 696613DEST_PATH_IMAGE052
) Spatial relationship (
Figure 187638DEST_PATH_IMAGE053
) Existing statistics: (
Figure 573619DEST_PATH_IMAGE054
) Wind power factor (
Figure 150094DEST_PATH_IMAGE055
) Topographic factors (a)
Figure 342041DEST_PATH_IMAGE056
) And surface factors (
Figure 421993DEST_PATH_IMAGE057
) (ii) a The secondary index layer is subordinate to the primary index layer, and has 22 items in total and is expressed as a final page
Figure 244455DEST_PATH_IMAGE003
}，j = 1,2,
Figure 42647DEST_PATH_IMAGE004
J and J are the number of the second-level indexes contained under the corresponding first-level index layer.
S3, digitalization processing of evaluation indexes: and (3) carrying out numerical quantification on the secondary index attribute of the color steel house related to the S2 according to the tightness degree with the hidden danger and easiness, and specifically comprising the following steps of: s3-1: classifying or grading the secondary indexes, and classifying each secondary index into x classes or x grades, wherein x is more than or equal to 2; s3-2: the classification or classification of each secondary index is subjected to forward ranking of potential safety hazard proneness according to historical data of potential safety hazard accidents of the color steel houses; s3-3: calculating a degree of vulnerability quantization value a = (x + 1-i)/(x + 1) for each class or class, i =1,2,
Figure 38285DEST_PATH_IMAGE004
x and i are the hidden danger and easy-to-send ranking of the class or the class; s3-4: and manually adjusting the easy-to-send degree quantized value A according to historical data of the potential safety hazard accidents of the color steel houses to form an evaluation index numerical table, which is shown in table 1. Select 6 color steel houses according toThe evaluation indexes in table 1 were assigned and summarized to obtain table 4.
Table 4 table for assigning potential safety hazard indexes of color steel house units
Figure 972743DEST_PATH_IMAGE058
S4, calculating the evaluation index weight of the potential safety hazard of the color steel house by using a group decision analytic hierarchy process, wherein the specific steps comprise:
s4-1: constructing an expert judgment matrix set, determining an index importance judgment matrix by adopting an expert judgment method, issuing judgment questionnaires to 5 experts on the aspects of management, maintenance, scientific research and the like of relevant departments of the railway to obtain an original importance judgment basis, and explaining by taking the evaluation index weight calculation of the expert A as an example. Experts used a 1-9 scale method, as shown in table 2, to compare the importance of each pair of indicators.
S4-1-1: the comparison and judgment matrix of the first-level index layer is as follows:
Figure 966107DEST_PATH_IMAGE059
primary index layer: maximum eigenvalue 6.228421,
Figure 517174DEST_PATH_IMAGE060
=0.033591, test pass; first level index layer weights
Figure 785344DEST_PATH_IMAGE061
=[0.13615003, 0.26159678, 0.05785475, 0.43580124, 0.06331962, 0.04527758]。
S4-1-2: is characterized by (a)
Figure 43150DEST_PATH_IMAGE062
) The following comparison and judgment matrix of the second-level index layer is as follows:
Figure 4153DEST_PATH_IMAGE063
is uniqueCharacterization (
Figure 776937DEST_PATH_IMAGE064
) The following secondary index layers: the maximum eigenvalue 11.854818,
Figure 52060DEST_PATH_IMAGE060
=0.055508, check pass;
is characterized by (a)
Figure 226690DEST_PATH_IMAGE064
) Lower second level index layer weights
Figure 358594DEST_PATH_IMAGE065
=[0.0541804, 0.11641961, 0.01788082, 0.02048179, 0.02098414, 0.18077475, 0.2625123, 0.08967559, 0.0819787, 0.13316328 0.02194863]。
S4-1-3: spatial relationship (
Figure 821936DEST_PATH_IMAGE066
) The following comparison and judgment matrix of the second-level index layer is as follows:
Figure 431909DEST_PATH_IMAGE067
spatial relationship (
Figure 726624DEST_PATH_IMAGE068
) The following secondary index layers: the maximum eigenvalue 3.032367,
Figure 763850DEST_PATH_IMAGE060
=0.018183, check pass; spatial relationship (
Figure 511226DEST_PATH_IMAGE068
) Lower second level index layer weight
Figure 128152DEST_PATH_IMAGE069
=[0.07861686, 0.26275317, 0.65862997]。
S4-1-4: wind power factor (
Figure 277374DEST_PATH_IMAGE070
) The importance comparison matrix of the following secondary index layers is as follows:
Figure 751081DEST_PATH_IMAGE071
wind power factor (
Figure 923436DEST_PATH_IMAGE072
) The following secondary index layers: the maximum eigenvalue 3.038511,
Figure 875211DEST_PATH_IMAGE060
=0.021635, test pass; wind power factor (
Figure 144519DEST_PATH_IMAGE072
) Lower second level index layer weight
Figure 523548DEST_PATH_IMAGE073
=[0.25828499, 0.63698557, 0.10472943]。
S4-1-5: topographic factors (
Figure 183199DEST_PATH_IMAGE074
) The importance comparison matrix of the following secondary index layers is as follows:
Figure 938665DEST_PATH_IMAGE075
topographic factors (
Figure 62479DEST_PATH_IMAGE076
) The following secondary index layers: the maximum eigenvalue 3.029064,
Figure 81251DEST_PATH_IMAGE060
=0.016328, test pass; topographic factors (
Figure 24936DEST_PATH_IMAGE076
) Lower second level index layer weights
Figure 584093DEST_PATH_IMAGE077
=[0.10203403, 0.72584831, 0.17211767]。
Existing statistics (
Figure 296834DEST_PATH_IMAGE054
) And surface factors (
Figure 486507DEST_PATH_IMAGE057
) The lower unique secondary index weight values are inherited from the respective primary index values.
S4-2: and calculating the weight coefficient of each index layer, and aggregating and solving the index weight aiming at the evaluation results of 5 experts in order to eliminate the error of the experts caused by different cognitive degrees and obtain more reliable evaluation results.
S4-2-1: the sum of the ratio weights of the experts in the first-level index layer
Figure 183068DEST_PATH_IMAGE078
：
Figure 280337DEST_PATH_IMAGE079
And (3) normalizing the results of the proportion weight of each expert in the first-level index layer:
Figure 50847DEST_PATH_IMAGE080
first-level index layer clustering weight W:
Figure 208159DEST_PATH_IMAGE081
Figure 392015DEST_PATH_IMAGE082
Figure 27396DEST_PATH_IMAGE083
s4-2-2: is characterized by (a)
Figure 917992DEST_PATH_IMAGE064
) The sum of the proportion weights of the experts in the lower two-level index layer
Figure 511784DEST_PATH_IMAGE078
：
Figure 651778DEST_PATH_IMAGE084
Is characterized by (a)
Figure 559691DEST_PATH_IMAGE064
) The normalization result of the proportion weight of each expert in the following two-level index layer is as follows:
Figure 367110DEST_PATH_IMAGE085
is characterized by (a)
Figure 866225DEST_PATH_IMAGE064
) The following two levels of index layer clustering weight W:
Figure 493515DEST_PATH_IMAGE086
Figure 673961DEST_PATH_IMAGE087
s4-2-3: spatial relationship (
Figure 601466DEST_PATH_IMAGE068
) The ratio weight sum of each expert in the lower two-level index layer
Figure 271481DEST_PATH_IMAGE078
：
Figure 589330DEST_PATH_IMAGE089
Spatial relationship (
Figure 370204DEST_PATH_IMAGE068
) The second-level index layer comprises the following normalized results of the proportion weight of each expert:
Figure 152216DEST_PATH_IMAGE090
spatial relationship (
Figure 993133DEST_PATH_IMAGE068
) The following two levels of index layer clustering weight W:
Figure 798278DEST_PATH_IMAGE091
Figure 648422DEST_PATH_IMAGE092
s4-2-4: wind power factor (
Figure 19360DEST_PATH_IMAGE072
) The sum of the proportion weights of the experts in the lower two-level index layer
Figure 234441DEST_PATH_IMAGE078
：
Figure 323620DEST_PATH_IMAGE094
Wind power factor (
Figure 711876DEST_PATH_IMAGE072
) The normalization result of the proportion weight of each expert in the following two-level index layer is as follows:
Figure 140583DEST_PATH_IMAGE095
wind power factor (
Figure 323303DEST_PATH_IMAGE072
) The following second level index layer clustering weight W:
Figure 165357DEST_PATH_IMAGE086
Figure 294987DEST_PATH_IMAGE096
s4-2-5: topographic factors (
Figure 374938DEST_PATH_IMAGE076
) The sum of the proportion weights of the experts in the lower two-level index layer
Figure 259718DEST_PATH_IMAGE078
：
Figure 261172DEST_PATH_IMAGE098
Topographic factors (
Figure 725651DEST_PATH_IMAGE076
) The normalization result of the proportion weight of each expert in the following two-level index layer is as follows:
Figure 191268DEST_PATH_IMAGE099
topographic factors (
Figure 981369DEST_PATH_IMAGE076
) The following two levels of index layer clustering weight W:
Figure 204540DEST_PATH_IMAGE100
Figure 738290DEST_PATH_IMAGE102
s4-3: according to the hierarchical relation of the indexes, the product of the aggregated secondary index layer weight and the attributed primary index weight is calculated, and the self characteristics are used for calculating
Figure 58412DEST_PATH_IMAGE103
The second-level index weight of (2) is calculated as an example:
Figure 753836DEST_PATH_IMAGE104
finally, the weight coefficient of the graded evaluation index of the potential safety hazard proneness of the single color steel house is obtained, and is shown in table 5.
TABLE 5 color steel house monomer safety hidden danger each-stage index weight coefficient table
Figure 729882DEST_PATH_IMAGE106
S5: the method comprises the following steps of (1) calculating the easiness of safety accidents of the railway external environment color steel house:
and (4) carrying out the numerical calculation of the easiness of occurrence on the potential safety hazard of the color steel room monomer at the 6 positions in the step S3 according to the following equation system, wherein the calculation result is shown in a table 6.
Figure 801744DEST_PATH_IMAGE108
Figure 710794DEST_PATH_IMAGE109
Table 6 result of potential safety hazard susceptibility values of color steel house monomers
Figure 108277DEST_PATH_IMAGE110
S6: potential safety hazard proneness grading of the color steel house: and calculating the value range of the numerical value of the easy-to-send property according to the numerical maximum value and the numerical minimum value of each evaluation index.
Figure 306040DEST_PATH_IMAGE111
The susceptibility values of 6 steel houses were normalized and classified according to five-level classification standards of low [0,0.2 ], medium [0.2,0.4 ], medium [0.4,0.6 ], medium [0.6,0.8 ] and high [0.8,1], as shown in table 7.
TABLE 7 result of grading of potential safety hazard of color steel house monomer
Figure 181592DEST_PATH_IMAGE112
While the preferred embodiments of the present invention have been illustrated and described, it will be appreciated by those skilled in the art that the foregoing embodiments are illustrative and not limiting, and that many changes may be made in the form and details of the embodiments of the invention without departing from the spirit and scope of the invention as defined in the appended claims. All falling within the scope of protection of the present invention.

Claims (10)

1. A potential safety hazard susceptibility evaluation method for a railway external environment color steel house is characterized by comprising the following steps:
s1, acquiring geographic information data of a color steel house in a research area;
s2, constructing an evaluation index set of potential safety hazard proneness of the color steel house;
s3, carrying out numerical processing on the evaluation indexes;
s4, calculating evaluation index weight by using a group decision analytic hierarchy process;
s5, calculating the potential safety hazard easiness of the single color steel house;
and S6, grading the potential safety hazards of the color steel house easily.
2. The method for evaluating the potential safety hazard proneness of the railway external environment color steel house according to claim 1, which is characterized by comprising the following steps of: in the S1, QGIS software is used for completing vectorization collection of color steel houses within 500 meters on two sides of a railway, and a color steel house vector attribute table is filled in by means of aerial photography remote sensing judgment, field investigation, data query and geographic information system space analysis, so that data entry of potential safety hazard susceptibility evaluation indexes is completed.
3. The method for evaluating the potential safety hazard of the railway external environment color steel house according to claim 1, is characterized by comprising the following steps of: the S2 comprises two levels of index layers in total, wherein the index layer at one level is expressed as
Figure 190505DEST_PATH_IMAGE001
Numbering the first-level indexes, wherein T is the number of indexes contained in the first-level index layer; the second level index layer is subordinate to the first level index layer and is expressed as
Figure 749663DEST_PATH_IMAGE002
The numbers of the second-level indexes are numbered, and J is the number of the second-level indexes contained under the corresponding first-level index layer.
4. The method for evaluating the potential safety hazard proneness of the railway external environment color steel house according to claim 1, which is characterized by comprising the following steps of: and S3, numerically quantifying the secondary index attribute of the color steel house related to S1 according to the closeness degree of the hidden danger susceptibility, wherein the value range of the quantified value A of the susceptibility is more than or equal to 0 and less than or equal to 1.
5. The method for evaluating the potential safety hazard proneness of the railway external environment color steel house according to claim 4, is characterized in that: the S3 comprises the following specific steps: s3-1: classifying or grading the secondary indexes, and classifying each secondary index into x classes or x grades, wherein x is more than or equal to 2; s3-2: the classification or classification of each secondary index is subjected to forward ranking of potential safety hazard proneness according to historical data of potential safety hazard accidents of the color steel houses; s3-3: calculating a degree of vulnerability quantization value a = (x + 1-i)/(x + 1) for each class or class, i =1,2,
Figure 836305DEST_PATH_IMAGE003
x and i are the ranking of the hidden danger and the easiness of the ranking of the class or the class; s3-4: and manually adjusting the variable degree quantitative value A according to historical data of the potential safety hazard accidents of the color steel room to form an evaluation index numerical table.
6. The method for evaluating the potential safety hazard of the railway external environment color steel house according to claim 1, is characterized by comprising the following steps of: the S4 comprises the following specific steps: s4-1: constructing an expert judgment matrix set; s4-2: calculating the weight coefficient of each index layer; s4-3: using maximum feature root
Figure 557136DEST_PATH_IMAGE004
Calculating a consistency index
Figure 863484DEST_PATH_IMAGE005
：
Figure 695174DEST_PATH_IMAGE006
And further calculates the consistency ratio
Figure 668946DEST_PATH_IMAGE007
(ii) a S4-4: aggregating and solving the index weight; the index weight is calculated by the formula
Figure 295099DEST_PATH_IMAGE008
；
In the formula (I), the compound is shown in the specification,
Figure 354322DEST_PATH_IMAGE009
the weight of the index is represented by,
Figure 989703DEST_PATH_IMAGE010
、
Figure 411457DEST_PATH_IMAGE011
the weighting coefficient and the index weight of the evaluation result of the kth expert are respectively, and u is the total number of the experts involved in the evaluation.
7. The method for evaluating the potential safety hazard proneness of the railway external environment color steel house according to claim 6, characterized by comprising the following steps: the experts in S4-1 adopt a 1-9 scale method, importance comparison is carried out on every two indexes, and a judgment matrix is given
Figure 113572DEST_PATH_IMAGE012
，
Figure 253566DEST_PATH_IMAGE013
，
k = 1,2,
Figure 833583DEST_PATH_IMAGE014
K in the formulamIn order to evaluate the number of factors,kthe number of experts is evaluated.
8. The method for evaluating the potential safety hazard proneness of the railway external environment color steel house according to claim 6, characterized by comprising the following steps: the S4-2 specifically comprises:
(1) calculating the product of row elements of the decision matrix
Figure 375423DEST_PATH_IMAGE015
，
Figure 749903DEST_PATH_IMAGE016
(i,j = 1,2,
Figure 377194DEST_PATH_IMAGE014
,n)；
(2) Calculating the row product
Figure 495322DEST_PATH_IMAGE015
The root of the square of degree n of (c),
Figure 891669DEST_PATH_IMAGE018
(i = 1,2,
Figure 935586DEST_PATH_IMAGE014
,n)；
(3) the weight of each index is obtained by adopting normalization processing,
Figure 50172DEST_PATH_IMAGE019
(i = 1,2,
Figure 706413DEST_PATH_IMAGE014
,n)；
(4) the maximum feature root is calculated and,
Figure 222845DEST_PATH_IMAGE021
。
9. the method for evaluating the potential safety hazard proneness of the railway external environment color steel house according to claim 1, which is characterized by comprising the following steps of: the calculation formula in S5 is as follows,
Figure 939128DEST_PATH_IMAGE023
(ii) a In the formula, F represents the easiness of the potential safety hazard of the color steel house in the external environment of the railway, S represents the evaluation attribute data of the easiness of the potential safety hazard of the color steel house,
Figure 541011DEST_PATH_IMAGE024
and (4) representing the weight of the evaluation index of the potential safety hazard proneness of the color steel house.
10. The method for evaluating the potential safety hazard of the railway external environment color steel house according to claim 1, is characterized by comprising the following steps of: and S6, utilizing the numerical value range of the potential safety hazard proneness of the color steel house to normalize the calculated proneness numerical value, and performing five-grade classification on the calculated proneness of the color steel house by using an equidistant classification method.
CN202211276239.2A 2022-10-19 2022-10-19 Method for evaluating potential safety hazard easiness of railway external environment color steel house Pending CN115358637A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104637022A (en) * 2013-11-14 2015-05-20 辽宁工程技术大学 Method of evaluating safety status of railway level crossing
CN105023067A (en) * 2015-08-04 2015-11-04 环境保护部南京环境科学研究所 Analytic hierarchy process-fuzzy comprehensive evaluation-based chemical project environmental risk evaluation system
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