CN113850680A - Rail transit engineering full life cycle engineering investment level assessment method - Google Patents
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Abstract
The invention discloses a method for evaluating the engineering investment level of a rail transit engineering in a full life cycle, which comprises the following steps: s1, acquiring original data of the construction cost of the rail transit project based on a distributed and highly-concurrent computer technology; s2, carrying out standardized professional coding processing and feature extraction association on the data; s3, calculating the line engineering cost data indexes, and counting and collecting different cost types; and S4, displaying the data according to the dimension required to be statistically analyzed. The technology realizes data classification and collection and contrastive analysis through a computer, and has the advantages of high efficiency, high speed and high accuracy.
Description
Technical Field
The invention relates to the technical field of track traffic construction cost auditing, in particular to a method for evaluating the engineering investment level of a track traffic engineering in a full life cycle.
Background
With the increasing economic strength of China, the pace of urbanization development is also gradually accelerated, and the rail transit construction, which is an important means for solving urban traffic, is also increasingly highly regarded. The main problems existing in the construction process of the urban rail transit project at present are that rail transit project cost data are not uniform in specification, the data are messy, rapid contrastive analysis cannot be realized among different lines, and the data can only be artificially obtained from different ways and then are contrasted through long-time arrangement, so that time and labor are wasted, the problems that the data are difficult to obtain, the time is long, errors occur easily and the like exist.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method for evaluating the engineering investment level of the rail transit engineering in the whole life cycle, which can quickly realize the comparative analysis of the engineering cost data among different lines, specialties and stages of the rail transit.
The invention adopts the following scheme for solving the problems: a rail transit engineering full life cycle engineering investment level assessment method comprises the following steps:
s1, acquiring original data of the construction cost of the rail transit project based on a distributed and highly-concurrent computer technology;
s2, carrying out standardized professional coding processing and feature extraction association on the data;
s3, calculating the cost data indexes of the line engineering, and counting and collecting different cost types;
and S4, displaying the data according to the dimension required to be statistically analyzed.
Further, the step of performing standardized professional encoding processing on the data in step S2 is as follows: firstly, a code is given to the standardized professional structure name, then the data is compared with the standardized professional structure name for identification, and the same code is given to the data with the matched similarity.
And calculating the text similarity between the data and the standardized professional structure name by adopting a cosine similarity algorithm.
The cosine similarity algorithm evaluates the similarity of the data and the cosine value of an included angle between two vectors of the standardized professional structure name by calculating the cosine value of the included angle between the two vectors, and the algorithm is as follows:
vector a is (x1, y1),
vector b is (x2, y2),
similarity＝a.b/|a|*|b|a.b＝x1x2+y1y2；
wherein: vector a is data, vector b is a standardized professional structure name,
the larger the obtained similarity value is, the more similar the similarity is, and the code with the highest similarity is given to the corresponding code.
Further, the step of performing feature extraction and association on the data in step S2 is as follows: in the aspect of feature association, a feature system is established according to the inheritance of the feature of the line dimension to the professional dimension; in the aspect of feature extraction, a rule of partial feature extraction is established in a feature system, and a method of regular matching or searching is applied to obtain partial feature values of line dimensions.
The invention has the beneficial effects that: the technology realizes data classification and collection and contrastive analysis through a computer, and has the advantages of high efficiency, high speed and high accuracy.
Drawings
FIG. 1 is a block flow diagram of the steps of the present invention;
fig. 2 and 3 are data presentation diagrams of embodiments of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
As shown in fig. 1, a method for evaluating the investment level of a rail transit engineering full life cycle project, the method comprises the following steps:
s1, acquiring original rail transit engineering cost data from total or volume approximate calculation of a line project based on a distributed and highly-concurrent computer technology;
s2, carrying out standardized professional coding processing and feature extraction association on the data;
s3, calculating the cost data indexes of the line engineering, and counting and collecting different cost types; the calculation method of the line engineering cost data index comprises the following steps: the total cost of the data divided by its engineering volume. The data is not represented by an index, so that the characteristic attribute of the index needs to be known, and the characteristic extraction in step S2 is to assign the characteristic attribute to the index, so that the index has more significance. And the code is used for making a unique identifier for the general type index and is used for statistical comparison analysis of the index.
S4, the computer quickly retrieves the data of the same type for comparison and analysis through coding and characteristic association of the data in the steps S2 and S3. The data of the same code is taken for one kind of analysis, and in one kind of indexes, the data condition is analyzed through different characteristics of the indexes. The data is then presented in bar and/or line graphs according to the dimensions of the statistical analysis required.
Further, the step of performing standardized professional encoding processing on the data in step S2 is as follows: firstly, a code is given to the standardized professional structure name, then the data is compared with the standardized professional structure name for identification, and the same code is given to the data with the matched similarity. For example: the 'station' professionally gives '0301' codes, the actual data expressions can be diversified (station 1, station 2, xx station and station (xx)) and are classified by the standard of 'station' -0301, and the standardization needs to compare the data with the standardized professional structure name to identify the data as the station and give the codes.
And calculating the text similarity between the data and the standardized professional structure name by adopting a cosine similarity algorithm.
The cosine similarity algorithm evaluates the similarity of the data and the cosine value of an included angle between two vectors of the standardized professional structure name by calculating the cosine value of the included angle between the two vectors, and the algorithm is as follows:
vector a is (x1, y1),
vector b is (x2, y2),
similarity＝a.b/|a|*|b|a.b＝x1x2+y1y2；
wherein: vector a is data, vector b is a standardized professional structure name,
the larger the obtained similarity value is, the more similar the similarity is, and the code with the highest similarity is given to the corresponding code.
The cosine similarity calculation method is that a cosine value between included angles of two vectors in a vector space is used for measuring the difference between two individuals, the cosine value is close to 1, the included angle tends to 0, the more similar the two vectors are, the cosine value is close to 0, and the included angle tends to 90 degrees, so that the more dissimilar the two vectors are. The cosine similarity is used to calculate the similarity between two text segments. The idea is as follows: 1. word segmentation; 2. listing all words; 3. word segmentation coding; 4. vectorizing word frequency; 5. and (4) applying a cosine function to measure the similarity of the two sentences.
Sentence a: this leather boot has a larger number. That number is appropriate.
Sentence B: the leather boot is not small in number, and is more suitable.
1. Word segmentation:
after segmenting the two sentences by using the ending segmentation words, respectively obtaining two lists:
listA [ 'this', 'only', 'leather boot', 'number', 'big', 'that', 'only', 'number', 'proper' ]
listB [ 'this', 'only', 'leather boot', 'number', 'no small', 'that', 'only', 'more fit', 'proper' ]
2. List all words, put listA and listB in one set, resulting in:
set { 'not small', 'appropriate', 'that', 'only', 'leather boot', 'more', 'number', 'this', 'big' }
The set is converted into a form of ditt, key is the word in the set, and value is the position where the word in the set appears, i.e. 'this': 1.
dit 1 { 'not small': 0, 'has' 1, 'suitable': 2, 'that' 3, 'only': 4, 'leather boot' 5, 'more fit': 6, 'number': 7, 'this' 8, 'big': 9}, it can be seen that the word "not small" is ranked 1 in set and the subscript is 0.
3. Encoding listA and listB, converting each word to a position appearing in set, after conversion:
listAcode＝[8,4,5,7,9,1,3,4,7,2]
listBcode＝[8,4,5,7,0,3,4,6,2]
by analyzing listAcode, in combination with ditt 1, it can be seen that 8 corresponds to the word "this", 4 corresponds to the word "just", 9 corresponds to the word "big", or sentence a and sentence B are converted to be represented by numbers.
4. And performing oneHot coding on the listAcode and listBcode, namely calculating the occurrence frequency of each participle. The results obtained after oneHot numbering are as follows:
listAcodeOneHot＝[0,1,1,1,2,1,0,2,1,1]
listBcodeOneHot＝[1,0,1,1,2,1,1,1,1,0]。
further, the step of performing feature extraction and association on the data in step S2 is as follows: in the aspect of feature association, a feature system is established according to the inheritance of the features of the line dimensions to professional dimensions, so that the inheritance of the values of the features can be ensured on one hand, and the uniqueness of the feature values can be kept on the other hand. Characteristics of the a-line, for example: grouping mode: 4A then also the corresponding specialty under the line, e.g. track specialty-its marshalling data source inherits its parent, line dimension. Also 4A. In the aspect of feature extraction, a rule of partial feature extraction is established in a feature system, and a method of regular matching or searching is applied to obtain partial feature values of a line dimension; for example: the circuit is characterized in that: if the security room is included, we need to search if the security room exists in the rerouting content, and determine if the characteristic value is yes or no. Another example is: to obtain the line length, a regular expression is needed to match data with text content of "line length xx" plus line kilometers, so as to obtain the line length: xx positive linear kilometers.
The following is made by combining specific data cases:
for example, the xx route real data form is shown in the following table 1:
TABLE 1
The project and expense names can be seen:
track engineering
3.1 Positive track
Underground line
1. Track laying
(1) Track laying rail for general section
(2) GJ-III type vibration damping track laying rail
(3) Middle-grade steel spring vibration damping section track laying
。。。。。。
The historical data file exists in excel form.
The first step is reading file data and converting unstructured data into structured data.
Secondly, carrying out standardized professional coding processing and feature extraction association on the data; namely, the read data is subjected to standardized encoding processing and feature extraction association, and the results are shown in table 2:
TABLE 2
Rail engineering-track
3.1 Positive track-Positive track New construction-encoding
Underground railway-main underground rail
1. Track laying
(1) General section track laying rail-general section laying rail
(2) GJ-III type vibration damping rail-high equal section rail
(3) Middle-grade steel spring vibration damping section track laying
。。。。。。
The track characteristics are as follows:
the type of the steel rail: 60/kg/m, sleeper type: long sleeper, switch type: single turnout, special turnout, line length: 9.55/square kilometer, number of stations: 4/seat, interval number: 4/segment, average inter-station spacing: 2.38/km, programmed age: 2018-09-01, vehicle grouping: 6B, whether the underground is laid completely: the laying mode is as follows: underground laying, planning period: third stage, the number of vehicles allocated in the initial stage: 528/vehicle, parking area: 176700 per square meter, line design passenger flow: 16.6/ten thousand times/day, shield inner diameter: 5.8/m.
Thirdly, calculating the cost data index of the line project, and counting and collecting different cost types; the indices are shown in tables 3 and 4:
TABLE 3
TABLE 4
And fourthly, as shown in figures 2 and 3, performing graphical display on the data according to the dimension needing statistical analysis.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention should not be limited thereby, and all the simple equivalent changes and modifications made in the claims and the description of the present invention are within the scope of the present invention.
Claims (5)
1. A rail transit engineering full life cycle engineering investment level assessment method is characterized by comprising the following steps:
s1, acquiring original data of the construction cost of the rail transit project based on a distributed and highly-concurrent computer technology;
s2, carrying out standardized professional coding processing and feature extraction association on the data;
s3, calculating the line engineering cost data indexes, and counting and collecting different cost types;
and S4, displaying the data according to the dimension required to be statistically analyzed.
2. The method for evaluating the investment level of the rail transit engineering full life cycle engineering according to claim 1, wherein the step of performing standardized professional coding processing on the data in the step S2 is as follows: firstly, a code is given to the standardized professional structure name, then the data and the standardized professional structure name are compared and identified, and the same code is given to the data with the matched similarity.
3. The method for estimating the investment level of the rail transit engineering full life cycle engineering according to claim 2, wherein the text similarity between the data and the standardized professional structure name is calculated by a cosine similarity algorithm.
4. The method for evaluating the investment level of the rail transit engineering full life cycle engineering according to claim 3, wherein the cosine similarity algorithm evaluates the similarity of the data and the cosine value of the included angle between the two vectors of the standardized professional structure name by the following algorithm:
vector a is (x1, y1),
vector b is (x2, y2),
similarity＝a.b/|a|*|b|a.b＝x1x2+y1y2；
wherein: vector a is data, vector b is a standardized professional structure name,
the larger the obtained similarity value is, the more similar the similarity is, and the code with the maximum similarity is given to the corresponding code.
5. The method for evaluating the investment level of the rail transit engineering full life cycle engineering according to claim 1, wherein the step of performing the feature extraction and association on the data in the step S2 is as follows: in the aspect of feature association, a feature system is established according to the inheritance of the feature of the line dimension to the professional dimension; in the aspect of feature extraction, a rule of partial feature extraction is established in a feature system, and a method of regular matching or searching is applied to obtain partial feature values of line dimensions.
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