CN109034530A - A kind of piping lane planing method based on neural network algorithm - Google Patents
A kind of piping lane planing method based on neural network algorithm Download PDFInfo
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Abstract
The piping lane planing method based on neural network algorithm that the invention discloses a kind of: pipe gallery appropriate building evaluation points rank scores list is formulated；A certain piping lane planning fairway is selected, to the six evaluation points marking of every road, is given a mark by expert decision-making method to every road piping lane appropriate building total score；Establish neural network structure, six evaluation points scorings are used as input quantity, and piping lane appropriate building total score inputs neural network structure training as output quantity, the piping lane appropriate building total score and six evaluation points relationships of every road are obtained, trained neural network structure is piping lane appropriate building appraisement system；Select it is a certain plan new area to piping lane, by six evaluation points scoring input piping lane appropriate building appraisement systems of every road, obtain every road piping lane appropriate building total score, divide piping lane appropriate building grade.Piping lane appropriate building is carried out present invention introduces neural network algorithm and analyzes work, can reduce the subjectivity in decision, accelerates the speed and reasonability of piping lane planning.
Description
Technical field
The invention belongs to piping lane construction plan fields, and more specifically, it relates to a kind of pipes based on neural network algorithm
Corridor planing method.
Background technique
Domestic piping lane construction will trace back to 1958 earliest and build first in Beijing；Country's publication in 2013 to 2016
The policy of series comprehensive piping lane construction, from planning, construction, management etc. proposes concrete measure, by " first planning, after build
If " principle, it is desirable that the completion Urban Underground pipe gallery construction plan authorized strength work in 2017 of each county (city), especially current
Jing-jin-ji region integration accelerates under conditions of promoting, and urban infrastructure construction and management level needs further increase, whole at present
It is in the rise stage on body, underground pipe gallery policy is intensively put into effect and from the continuous specification of technology, construction, financing etc. and complete
Kind, policy is constantly raised the price, is refined, and is expected to after the completion of the construction of experimental city, and subsequent duplicate expansion is expected to quickly open.
The reason of construction of domestic pipe gallery quickly propels mainly by overall situation and investment pull economic development etc. it is multiple because
The influence of element, often leader-will is stronger for construction area selection, to cause do not have clear appropriate building to build in many piping lanes
If the reason of, lack enough scientific basis.It is more PPP mode and research in policy, and comprehensive in terms of academic research
The research of piping lane technical specification or regulation etc. " hard science "；In piping lane regional planning, study on construction, Industry Innovation developmental research etc.
Shortage is compared in the research of " soft science ", and therefore, have greatly improved space in piping lane planning appropriate building analysis.
Summary of the invention
Purpose of the invention is to overcome the shortcomings in the prior art, provides a kind of piping lane based on neural network algorithm
Planing method introduces neural network algorithm and carries out piping lane appropriate building analysis work, and neural network algorithm passes through mind in simulation human brain
Transmittance process through member realizes the work for the nonlinearity relationship between data that solves, and can reduce in decision through the invention
Subjectivity, and the speed and reasonability of piping lane planning can be greatly speeded up.
The purpose of the present invention is what is be achieved through the following technical solutions.
Piping lane planing method based on neural network algorithm of the invention, comprising the following steps:
Step 1 formulates pipe gallery appropriate building evaluation points rank scores list
Related six evaluation points, including functional region of city and land used are planned with piping lane according to the setting of expert decision-making method
Intensity, municipal pipeline and trunk pipeline corridor situation, road construction situation, underground space utilization power, road traffic flow feelings
Condition, main landscape road；Six evaluation points and six quantifiable considerations are linked up with, the two corresponds, including road
Road two sides land used plot ratio, number of lines and trunk pipeline corridor (non-status) situation, road build up situation, underground space development
Whether intensity road saturation degree, is used as landscape road；According to the quantized result of considerations, each evaluation points is carried out etc.
Grade divides, according to the different corresponding scorings of grade setting；
Step 2 selects a certain piping lane planning fairway, the pipe gallery appropriate building evaluation formulated according to step 1 because
Sub- rank scores list gives a mark to six evaluation points of every road in this area, and passes through expert decision-making method pair
The piping lane appropriate building total score of every road is given a mark in this area；
Step 3 establishes a neural network structure, by commenting for six evaluation points of every road that step 2 obtains
It is allocated as input quantity, the piping lane appropriate building total score of every road is as output quantity, using these input, output quantities as training data
It inputs in the neural network structure and is trained, the piping lane appropriate building total score and six of every road is obtained by neural network algorithm
The relationship of item evaluation points, trained neural network structure is piping lane appropriate building appraisement system；
Step 4 selectes a certain new area to piping lane planning, is evaluated according to the pipe gallery appropriate building that step 1 is formulated
Factor rating scoring list obtains the scoring of six evaluation points of every road in the new area, by these evaluation points
Scoring be input in piping lane appropriate building appraisement system, obtain this it is new area in every road piping lane appropriate building total score, according to
The piping lane appropriate building total score score value of every road divides its piping lane appropriate building grade, in this, as the foundation of piping lane planning.
The grade of piping lane appropriate building described in step 4 builds including should not build, can build, conveniently, is suitable for construction, root
The piping lane appropriate building grade of every road is evaluated according to the score value of the piping lane appropriate building total score of every road.
Compared with prior art, the beneficial effects brought by the technical solution of the present invention are as follows:
The present invention is big for ingredient shared by subjectivity in current piping lane planning process, it is relevant to piping lane appropriate building it is every because
Element cannot very intuitive for policymaker's comprehensive analysis the shortcomings that, propose and piping lane appropriate building total score obtained by multi-factor analysis approach
Method, using neural network algorithm as kernel complete piping lane planning forecast work, both solved piping lane planning in subjectivity
Problem, increases the objectivity of piping lane programmed decision-making, and improves the efficiency and accuracy rate of piping lane planning.
Detailed description of the invention
Fig. 1 is the flow chart of the piping lane planing method the present invention is based on neural network algorithm.
Fig. 2 is the Artificial Neural Network Structures established in embodiment.
Specific embodiment
The invention will be further described with reference to the accompanying drawing.
As shown in Figure 1, the piping lane planing method of the invention based on neural network algorithm, comprising the following steps:
Step 1 formulates pipe gallery appropriate building evaluation points rank scores list:
Related six evaluation points, including functional region of city and land used are planned with piping lane according to the setting of expert decision-making method
Intensity, municipal pipeline and trunk pipeline corridor situation, road construction situation, underground space utilization power, road traffic flow feelings
Condition, main landscape road.Six evaluation points and six quantifiable considerations are linked up with, the two corresponds, consider because
Element successively include both sides of the road land used plot ratio, number of lines and trunk pipeline corridor (non-status) situation, road build up situation,
Whether underground space development intensity road saturation degree, is used as landscape road.According to the quantized result of considerations, each is commented
The valence factor carries out grade classification, according to the different corresponding scorings of grade setting.Such as example shown in the following table 1, wherein full marks be
10 points.
1 pipe gallery appropriate building evaluation points rank scores list of table
Step 2 selects a certain piping lane planning fairway, the pipe gallery appropriate building evaluation formulated according to step 1 because
Sub- rank scores list gives a mark to six evaluation points of every road in this area, and passes through expert decision-making method pair
The piping lane appropriate building total score of every road is given a mark in this area.
Step 3 establishes a neural network structure, by commenting for six evaluation points of every road that step 2 obtains
It is allocated as input quantity, the piping lane appropriate building total score of every road is as output quantity, using these input, output quantities as training data
It inputs in the neural network structure and is trained, the piping lane appropriate building total score and six of every road is obtained by neural network algorithm
The relationship of item evaluation points, trained neural network structure is piping lane appropriate building appraisement system.
Step 4 selectes a certain new area to piping lane planning, is evaluated according to the pipe gallery appropriate building that step 1 is formulated
Factor rating scoring list obtains the scoring of six evaluation points of every road in the new area, by these evaluation points
Scoring be input in piping lane appropriate building appraisement system, obtain this it is new area in every road piping lane appropriate building total score S, according to
Its piping lane appropriate building grade is respectively divided in the piping lane appropriate building total score score value of every road, in this, as the foundation of piping lane planning.
Wherein, piping lane appropriate building grade builds including should not build, can build, conveniently, is suitable for construction, according to every road
The score value of the piping lane appropriate building total score on road evaluates the piping lane appropriate building grade of every road.As shown in table 2 below, as 0 < S≤2,
Piping lane appropriate building grade is " should not build "；As 2 < S≤4, piping lane appropriate building grade is " can build "；As 4 < S≤7,
Piping lane appropriate building grade is " conveniently construction "；As 7 < S≤10, piping lane appropriate building grade is " being suitable for construction ".
2 piping lane appropriate building grade classification of table is according to table
| Piping lane appropriate building total score | Piping lane appropriate building grade |
| 0 S≤2 < | It should not build |
| 2 S≤4 < | It can build |
| 4 S≤7 < | Conveniently construction |
| 7 S≤10 < | It is suitable for construction |
Embodiment
200, the existing city A region road completes the training of neural network model as sample, and with trained nerve
Network model completes the piping lane planning of 290 sections of the city B region road, specific operation process are as follows:
Firstly, establishing Artificial Neural Network Structures using programs such as matlab, as shown in Figure 2.
Then, the six evaluation points scores and piping lane appropriate building total score for obtaining 200 roads in the city A region are as original
Data (such as table 3) are trained neural network model.
The trained initial data of table 3
Secondly, being given a mark to six evaluation points of 290 roads in the city B region, marking the results are shown in Table according to table 1
4。
290, the city table 4B region, six, road evaluation points marking table
Note: it is more to be related to region since length is longer for part of road, therefore these roads are divided into several segments, respectively
Number.
Then, trained neural network model is introduced, the evaluation points score in table 4 is imported into neural network mould
Type obtains the constructive total score S of piping lane of every road, is shown in Table 5.
The constructive total score Score Lists of piping lane of 290 roads in the city table 5B region
| Road name | Number | The constructive total score S of piping lane |
| The road Lv Cheng | 3 | 7.3 |
| The road Lv Cheng | 1 | 7.1 |
| The road Lv Cheng | 2 | 7.1 |
| Gaoyang main road | 18 | 6.85 |
| Gaoyang main road | 19 | 6.85 |
| Gaoyang main road | 20 | 6.85 |
| The upper North Road Pu | 1 | 6.8 |
| The upper North Road Pu | 2 | 6.8 |
| The upper North Road Pu | 3 | 6.8 |
| The road Lv Cheng | 4 | 6.8 |
| The road Kai Zhou | 4 | 6.7 |
| The main road Jing Kai | 5 | 6.7 |
| The main road Jing Kai | 6 | 6.7 |
| Gaoyang main road | 7 | 6.65 |
| The main road Wei Dou | 2 | 6.6 |
| The main road Wei Dou | 3 | 6.6 |
| The main road Jing Kai | 2 | 6.4 |
| Gaoyang main road | 2 | 6.35 |
| Gaoyang main road | 3 | 6.35 |
| Gaoyang main road | 4 | 6.35 |
| Gaoyang main road | 5 | 6.35 |
| Gaoyang main road | 6 | 6.35 |
| Gaoyang main road | 22 | 6.35 |
| : | : | : |
| Aimin Street | 1 | 3.1 |
| Aimin Street | 5 | 3.1 |
| Aimin Street | 6 | 3.1 |
| Aimin Street | 7 | 3.1 |
| The road Xiang Yuan | 2 | 2.95 |
| Long Menlu | 3 | 2.75 |
| Long Menlu | 4 | 2.75 |
| The road Xiang Yuan | 1 | 2.75 |
| The road Xiang Yuan | 3 | 2.75 |
| Black Warrior road | 14 | 2.7 |
Finally, dividing the piping lane appropriate building grade of every road according to table 2, it the results are shown in Table 6.
6 piping lane appropriate building grade classification of table
| Road name | Number | The constructive total score S of piping lane | Piping lane appropriate building grade |
| The road Lv Cheng | 3 | 7.3 | It is suitable for construction |
| The road Lv Cheng | 1 | 7.1 | It is suitable for construction |
| The road Lv Cheng | 2 | 7.1 | It is suitable for construction |
| Gaoyang main road | 18 | 6.85 | Conveniently construction |
| Gaoyang main road | 19 | 6.85 | Conveniently construction |
| Gaoyang main road | 20 | 6.85 | Conveniently construction |
| The upper North Road Pu | 1 | 6.8 | Conveniently construction |
| The upper North Road Pu | 2 | 6.8 | Conveniently construction |
| The upper North Road Pu | 3 | 6.8 | Conveniently construction |
| The road Lv Cheng | 4 | 6.8 | Conveniently construction |
| The road Kai Zhou | 4 | 6.7 | Conveniently construction |
| The main road Jing Kai | 5 | 6.7 | Conveniently construction |
| The main road Jing Kai | 6 | 6.7 | Conveniently construction |
| Gaoyang main road | 7 | 6.65 | Conveniently construction |
| The main road Wei Dou | 2 | 6.6 | Conveniently construction |
| The main road Wei Dou | 3 | 6.6 | Conveniently construction |
| The main road Jing Kai | 2 | 6.4 | Conveniently construction |
| Gaoyang main road | 2 | 6.35 | Conveniently construction |
| Gaoyang main road | 3 | 6.35 | Conveniently construction |
| Gaoyang main road | 4 | 6.35 | Conveniently construction |
| Gaoyang main road | 5 | 6.35 | Conveniently construction |
| Gaoyang main road | 6 | 6.35 | Conveniently construction |
| Gaoyang main road | 22 | 6.35 | Conveniently construction |
| : | : | : | : |
| Aimin Street | 1 | 3.1 | It can build |
| Aimin Street | 5 | 3.1 | It can build |
| Aimin Street | 6 | 3.1 | It can build |
| Aimin Street | 7 | 3.1 | It can build |
| The road Xiang Yuan | 2 | 2.95 | It can build |
| Long Menlu | 3 | 2.75 | It can build |
| Long Menlu | 4 | 2.75 | It can build |
| The road Xiang Yuan | 1 | 2.75 | It can build |
| The road Xiang Yuan | 3 | 2.75 | It can build |
| Black Warrior road | 14 | 2.7 | It can build |
Although above in conjunction with attached drawing, invention has been described, and the invention is not limited to above-mentioned, above-mentioned is specific
Embodiment is only schematical, rather than restrictive, and those skilled in the art under the inspiration of the present invention, go back
Many forms can be made, all of these belong to the protection of the present invention.
Claims (2)
1. a kind of piping lane planing method based on neural network algorithm, which comprises the following steps:
Step 1 formulates pipe gallery appropriate building evaluation points rank scores list
Plan related six evaluation points according to the setting of expert decision-making method and piping lane, including functional region of city and land used it is strong
Degree, municipal pipeline and trunk pipeline corridor situation, road construction situation, underground space utilization power, road traffic flow situation,
Main landscape road；Six evaluation points and six quantifiable considerations are linked up with, the two corresponds, including road two
Side land used plot ratio, number of lines and trunk pipeline corridor (non-status) situation, that road builds up situation, underground space development is strong
Whether degree road saturation degree, is used as landscape road；According to the quantized result of considerations, grade is carried out to each evaluation points
It divides, according to the different corresponding scorings of grade setting；
Step 2 selects a certain piping lane planning fairway, the pipe gallery appropriate building evaluation points point formulated according to step 1
Grade scoring list, gives a mark to six evaluation points of every road in this area, and by expert decision-making method to the ground
The piping lane appropriate building total score of every road is given a mark in area；
Step 3 establishes a neural network structure, and the scoring of six evaluation points of every road that step 2 is obtained is made
For input quantity, the piping lane appropriate building total score of every road is inputted as output quantity using these inputs, output quantity as training data
It is trained in the neural network structure, the piping lane appropriate building total score of every road is obtained by neural network algorithm and six are commented
The relationship of the valence factor, trained neural network structure are piping lane appropriate building appraisement system；
Step 4 selectes a certain new area to piping lane planning, the pipe gallery appropriate building evaluation points formulated according to step 1
Rank scores list obtains the scoring of six evaluation points of every road in the new area, by commenting for these evaluation points
Divide and be input in piping lane appropriate building appraisement system, the piping lane appropriate building total score of every road in the new area is obtained, according to every
The piping lane appropriate building total score score value of road divides its piping lane appropriate building grade, in this, as the foundation of piping lane planning.
2. the piping lane planing method according to claim 1 based on neural network algorithm, which is characterized in that institute in step 4
Piping lane appropriate building grade is stated to build including should not build, can build, conveniently, be suitable for construction, it is suitable according to the piping lane of every road
The score value of building property total score evaluates the piping lane appropriate building grade of every road.
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Cited By (1)
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Application publication date: 20181218 |