CN109854299A - Airway coefficient of frictional resistance fast determination method based on big data - Google Patents
Airway coefficient of frictional resistance fast determination method based on big data Download PDFInfo
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Abstract
A kind of airway coefficient of frictional resistance fast determination method based on big data provided by the invention, comprising steps of S1: acquisition dash supplemental characteristic and the corresponding tunnel coefficient of frictional resistance of dash supplemental characteristic；S2: classification data superclass is established；Classification data subclass is established, and establishes the three dimensional analysis model of the impact factor of every seed set；S3: acquisition the tunnel of being determined coefficient of frictional resistance tunnel ventilation parameter, find with the matched classification data superclass of the dash parameter and subclass, provide the recommended value of the coefficient of frictional resistance in the tunnel；The present invention is by establishing the classification data superclass and subclass of dash supplemental characteristic Yu tunnel coefficient of frictional resistance, form a synthesis, dynamic updates, accurate coefficient of frictional resistance system large data sets, pass through tunnel attribute, in the data set for wherein searching for similar tunnel, then similar impact factor is searched in the data set in similar tunnel, quickly determines coefficient of frictional resistance.
Description
Technical field
The present invention relates to mine laneway coefficient of frictional resistance electric powder predictions, and in particular to a kind of logical based on big data
Air way road coefficient of frictional resistance fast determination method.
Background technique
China is coal big country, and pit mining is still the main mining method in China, and prevention and treatment mine disaster is coal mining
In the most important thing；Mine ventilation is to prevent and treat the important leverage means of mine disaster, and ventilating system is by by fresh air conveying
It is each under to mine to use wind place, meet underground work personnel and breathe needs, while reaching dilution pernicious gas, adjusts mine temperature
Degree etc..When mine disaster occurs, by adjusting ventilating system facilities and equipments, it can be effectively prevent fault spread, block chain thing
Therefore occur, utmostly ensure the security of the lives and property, therefore ventilating system occupies an important position in mine safety production.
For the reliable of effective guarantee ventilating system and stablize, understand mine ventilation system operating condition, it is logical to grasp full mine
Wind resistance distribution is most important, but it is existing measure of resistance is aerated to full mine at present, the problem of generally existing " indeterminacy ".
Data measured by various measurement methods, it is also necessary to which artificial treatment, alignment error can be brought into mine ventilation system and carry out
Ventilation Network Solution, until simulating the ventilation situation (air quantity, wind speed, resistance distribution) of entire mine.It is being aerated emulation
In simulation, encounters when not surveying tunnel, can only rule of thumb set a coefficient of frictional resistance.Thus, it is quite necessary to establish one
A synthesis, dynamic update, accurate coefficient of frictional resistance system database, to there is one when being aerated analogue simulation
According to a coefficient of frictional resistance is determined, roadway resistance force is precisely determined.
Summary of the invention
In view of this, the object of the present invention is to provide a kind of, the airway coefficient of frictional resistance based on big data is quickly true
Method is determined, by establishing the classification data superclass and subclass of dash supplemental characteristic Yu tunnel coefficient of frictional resistance, shape
Phase is searched for by tunnel attribute wherein at a synthesis, dynamic update, accurate coefficient of frictional resistance system large data sets
Like the data set in tunnel, similar impact factor is then searched in the data set in similar tunnel, quickly determines frictional resistance system
Number, to quickly calculate the resistance in the tunnel.
The present invention provides a kind of airway coefficient of frictional resistance fast determination method based on big data, comprising steps of
S1: acquisition dash supplemental characteristic and the corresponding tunnel coefficient of frictional resistance of dash supplemental characteristic；
S2: dash supplemental characteristic is subjected to successively secondary classification according to tunnel attribute, establishes classification data superclass；
Superclass is classified according to the classification of impact factor, establishes classification data subclass, and establish the influence of every seed set
The three dimensional analysis model of the factor；Wherein, the impact factor is that tunnel coefficient of frictional resistance is influenced in dash supplemental characteristic
It is worth the supplemental characteristic of size；
S3: the ventilation parameter in the tunnel of the acquisition tunnel of being determined coefficient of frictional resistance, and by the dash parameter and
Each classification data superclass is compared, find with the matched classification data superclass of the dash parameter, then according to adopting
The impact factor collected finds the corresponding subclass of classification data superclass, and will affect influence that the factor and corresponding subset are closed because
The three dimensional analysis model of son compares, and provides the recommended value of the coefficient of frictional resistance in the tunnel.
It further, further include step S4: the recommended value of the coefficient of frictional resistance in the tunnel provided step S3 is corresponding
Corresponding coefficient of frictional resistance value frequency of occurrence increases by 1 in the three dimensional analysis model of impact factor, obtain updated influence because
The three dimensional analysis model of son；Wherein, the three dimensional analysis model of the impact factor include influence factor value, coefficient of frictional resistance and
Coefficient of frictional resistance numerical value frequency of occurrence.
Further, the tunnel attribute includes three kinds of tunnel attributes, i.e. roadway support form, tunnel classification and tunnel wall surface
Feature.
Further, the impact factor includes that drift section size, tunnel atmospheric density, tunnel ponding and tunnel wall surface are recessed
Convexity.
Further, dash supplemental characteristic is subjected to successively secondary classification according to tunnel attribute in the step S2, built
Vertical classification data superclass specifically includes:
S21: dash supplemental characteristic is classified according to one of tunnel attribute classification, establishes the tunnel attribute
The mapping relations of dash parameter and tunnel coefficient of frictional resistance numberical range of all categories；
S22: by the sorted dash parameter of step S21 according to the other one of class of remaining two kinds of tunnel Attribute class
Do not classify, establishes the sorted dash parameter of tunnel attribute step S21 of all categories and tunnel frictional resistance system
The mapping relations of number numberical range；
S23: by the sorted dash parameter of step S22 according to a kind of other one of class of remaining tunnel Attribute class
Do not classify, establishes the sorted dash parameter of tunnel attribute step S22 of all categories and tunnel frictional resistance system
The mapping relations of number numberical range, form the mapping corresponding with three kinds of tunnel attributes of each tunnel coefficient of frictional resistance numberical range
Relationship, and establish classification data superclass；Each element in the classification data superclass is three kinds of tunnel attributes, three kinds of lanes
The coefficient of frictional resistance numberical range and the coefficient of frictional resistance numerical value frequency of occurrence of road attribute mapping.
Further, superclass is classified in the step S2 according to the classification of impact factor, establishes classification data
Set, and establish the three dimensional analysis model of the impact factor of every seed set specifically:
S24: the dash parameter that step S23 classifies is classified according to the classification of impact factor, establishment step S23
The mapping relations of impact factor and tunnel coefficient of frictional resistance numberical range after classification form tunnel coefficient of frictional resistance numerical value model
Enclose with impact factor and the corresponding mapping relations of three kinds of tunnel attributes, and establish classification data subclass, each classification
The frictional resistance mapped comprising three kinds of tunnel attributes, impact factor, three kinds of tunnel attributes and impact factor in data subset conjunction
Factor v range and the coefficient of frictional resistance numerical value frequency of occurrence；
S25: using the influence factor value in each classification data subclass as X-axis, using coefficient of frictional resistance as Y-axis, to rub
Wiping resistance coefficient numerical value is Z axis, establishes three-dimensional system of coordinate, that is, establishes the three dimensional analysis model of impact factor.
Further, in the step S3 specifically:
S31: the ventilation parameter in the tunnel of the acquisition tunnel of being determined coefficient of frictional resistance,
S32: will acquire the tunnel of being determined coefficient of frictional resistance tunnel ventilation parameter according to tunnel attribute with it is each
Three kinds of tunnel attributes in classification data superclass are compared, and look for described in three kinds of tunnel attributes corresponding with the ventilation parameter
Classification data superclass；
S33: and then by all kinds of impact factors in the ventilation parameter and in the subclass for belonging to the classification data superclass
In impact factor be compared, find all subclass corresponding with all kinds of impact factors in the ventilation parameter；
S34: asking the intersection of coefficient of frictional resistance numberical range in each subclass, obtains initial tunnel coefficient of frictional resistance
Recommended value range；
S35: the three dimensional analysis model of the corresponding each impact factor of each coefficient of frictional resistance within the scope of observation recommended value, it will
There is the most friction of total degree in three dimensional analysis model in each coefficient of frictional resistance within the scope of recommended value in each impact factor
Recommended value of the resistance coefficient numerical value as tunnel coefficient of frictional resistance.
Beneficial effects of the present invention: the present invention is by establishing point of the dash supplemental characteristic with tunnel coefficient of frictional resistance
Class data superclass and subclass form a synthesis, dynamic update, accurate coefficient of frictional resistance system large data sets, lead to
Tunnel attribute is crossed, in the data set for wherein searching for similar tunnel, similar influence is then searched in the data set in similar tunnel
The factor quickly determines coefficient of frictional resistance, to quickly calculate the resistance in the tunnel.
Detailed description of the invention
The invention will be further described with reference to the accompanying drawings and examples:
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is classification data superclass mapping relations exemplary diagram；
Fig. 3 is the three dimensional analysis model example figure of impact factor；
Fig. 4 is that data distribution formula stores schematic diagram.
Specific embodiment
As shown in Figure 1, a kind of quick determination side of airway coefficient of frictional resistance based on big data provided by the invention
Method, comprising steps of
S1: acquisition dash supplemental characteristic and the corresponding tunnel coefficient of frictional resistance of dash supplemental characteristic；It is described
Dash supplemental characteristic and the corresponding tunnel coefficient of frictional resistance of dash supplemental characteristic derive from coal mine in all parts of the country and go through
Secondary true measurement and the data calculated.
S2: dash supplemental characteristic is subjected to successively secondary classification according to tunnel attribute, establishes classification data superclass；
Superclass is classified according to the classification of impact factor, establishes classification data subclass, and establish the influence of every seed set
The three dimensional analysis model of the factor；Wherein, the impact factor is that tunnel coefficient of frictional resistance is influenced in dash supplemental characteristic
It is worth the supplemental characteristic of size；
S3: the ventilation parameter in the tunnel of the acquisition tunnel of being determined coefficient of frictional resistance, and by the dash parameter and
Each classification data superclass is compared, find with the matched classification data superclass of the dash parameter, then according to adopting
The impact factor collected finds the corresponding subclass of classification data superclass, and will affect influence that the factor and corresponding subset are closed because
The three dimensional analysis model of son compares, and provides the recommended value of the coefficient of frictional resistance in the tunnel.By the above method, lane is established
The classification data superclass and subclass of road ventilation parameter data and tunnel coefficient of frictional resistance form a synthesis, dynamic more
Newly, accurate coefficient of frictional resistance system large data sets, by tunnel attribute, in the data set for wherein searching for similar tunnel, so
Similar impact factor is searched in the data set in similar tunnel afterwards, coefficient of frictional resistance is quickly determined, to quickly calculate
The resistance in the tunnel.The Air Resistance in Roadway Ventilation calculation formula are as follows:
Wherein,For tunnel coefficient of frictional resistance (constant, unit: Ns2/m4), L is tunnel length (unit: m), and U is
Tunnel perimeter (unit: m), S are cross-sectional area (unit: m2)。
It further include step S4: by the corresponding impact factor of recommended value of the coefficient of frictional resistance in the tunnel provided step S3
Three dimensional analysis model in corresponding coefficient of frictional resistance value frequency of occurrence increase by 1, obtain the three-dimensional of updated impact factor
Analysis model；Wherein, the three dimensional analysis model of the impact factor includes influence factor value, coefficient of frictional resistance and frictional resistance
Factor v frequency of occurrence carries out dynamic update by three dimensional analysis model of the above method to impact factor, to call shadow
While ringing the three dimensional analysis model of the factor, the three-dimensional of impact factor is continuously improved in the three dimensional analysis model of training impact factor
The precision of analysis model.
The tunnel attribute includes three kinds of tunnel attributes, i.e. roadway support form, tunnel classification and wall region feature.Institute
Stating roadway support form includes bolt-spary supports, and supporting, suspension roof support, stone cornering arch supporting, rubble block arch supporting, coagulation are starched in sandblasting
Native canopy supporting, U-typed steel supporting, I-steel, rail supporting, shield, chock shield, standing support are single
Body hydraulic prop, metal friction pillar, articulated roof beam, wooden prop.The tunnel classification includes track gallery, track inclined gallery, rail
Road transporter lane is divulged information pedestrian lane (having step), is divulged information pedestrian lane (no step).The wall region feature includes that smooth surface is quick-fried
Broken, convex-concave degree<150, General Explosive, convex-concave degree>150, exposed 100~200 anchor spacing 600~1000 of anchor pole, anchor pole exposed 150
~200 anchor spacing 600~800, wall surface is coarse, and wall surface is smooth, section 5~9, vertical bore 4~5, and section 5~8, vertical bore 4~
8, section 9~10, vertical bore 4~8, section 4~6, vertical bore 7~9, section 9~10, vertical bore 4~8.
The impact factor includes drift section size, tunnel atmospheric density, tunnel ponding and tunnel wall surface camber.
As shown in Fig. 2, dash supplemental characteristic is subjected to successively secondary classification according to tunnel attribute in the step S2,
Classification data superclass is established to specifically include:
S21: dash supplemental characteristic is classified according to one of tunnel attribute classification, establishes the tunnel attribute
The mapping relations of dash parameter and tunnel coefficient of frictional resistance numberical range of all categories；In the present embodiment, step S21 is pressed
Classify according to the tunnel attribute classification of roadway support form.
S22: by the sorted dash parameter of step S21 according to the other one of class of remaining two kinds of tunnel Attribute class
Do not classify, establishes the sorted dash parameter of tunnel attribute step S21 of all categories and tunnel frictional resistance system
The mapping relations of number numberical range；In the present embodiment, step S22 classifies according to the tunnel classification of roadway support form.
S23: by the sorted dash parameter of step S22 according to a kind of other one of class of remaining tunnel Attribute class
Do not classify, establishes the sorted dash parameter of tunnel attribute step S22 of all categories and tunnel frictional resistance system
The mapping relations of number numberical range, form the mapping corresponding with three kinds of tunnel attributes of each tunnel coefficient of frictional resistance numberical range
Relationship, and establish classification data superclass；Each element in the classification data superclass is three kinds of tunnel attributes, three kinds of lanes
The coefficient of frictional resistance numberical range and the coefficient of frictional resistance numerical value frequency of occurrence of road attribute mapping.In the present embodiment, step
Rapid S23 classifies according to the wall region feature of roadway support form.Each tunnel coefficient of frictional resistance numberical range with three kinds
The corresponding mapping relations of tunnel attribute can be as shown in table 1.104
Table 1
Wherein, every group of coefficient of frictional resistance numberical range corresponds to a kind of tunnel attribute of roadway support form, a kind of tunnel
The tunnel attribute of classification and a kind of tunnel attribute of wall region feature.By classification data superclass by dash parameter number
According to preliminary comparison mapping is first carried out, the corresponding coefficient of frictional resistance numberical range of dash supplemental characteristic is first defined
It reduces, to reduce the expense for finding corresponding coefficient of frictional resistance numberical range subsequently through classification data subclass.Specifically,
For example, classification data superclass S1Include element --- track gallery, bolt-spary supports, photoface exploision, convex-concave degree < 150；Classification number
According to superclass S2Include element --- track inclined gallery, bolt-spary supports, General Explosive, convex-concave degree > 150
As shown in figure 3, superclass is classified according to the classification of impact factor in the step S2, classification data is established
Subclass, and establish the three dimensional analysis model of the impact factor of every seed set specifically:
S24: the dash parameter that step S23 classifies is classified according to the classification of impact factor, establishment step S23
The mapping relations of impact factor and tunnel coefficient of frictional resistance numberical range after classification form tunnel coefficient of frictional resistance numerical value model
Enclose with impact factor and the corresponding mapping relations of three kinds of tunnel attributes, and establish classification data subclass, each classification
The frictional resistance mapped comprising three kinds of tunnel attributes, impact factor, three kinds of tunnel attributes and impact factor in data subset conjunction
Factor v range and the coefficient of frictional resistance numerical value frequency of occurrence；
S25: using the influence factor value in each classification data subclass as X-axis, using coefficient of frictional resistance as Y-axis, to rub
Wiping resistance coefficient numerical value frequency of occurrence is Z axis, establishes three-dimensional system of coordinate, that is, establishes the three dimensional analysis model of impact factor.Pass through
Establishing three dimensional analysis model can intuitively observe, the corresponding coefficient of frictional resistance frequency of occurrences of impact factor, be easy to use personnel
Quickly determine the recommended value of tunnel coefficient of frictional resistance.
As shown in figure 4, in actual application, due to the step S2 classification data superclass established and classification data
Collective data is larger, to classification data superclass and classification data subclass data and classification data subclass data
When the three dimensional analysis model of corresponding impact factor is stored, using big data memory technology, realizes mass data storage, build
Vertical data quick storage and reading mechanism, storage mode use distributed storage.Large data sets use distributed data base system,
It is characterized in that being distributed in the different node of computer network on Data Physical, same system is logically belonged to.System uses
Management system in global domination set concentrates on global control composition on a certain node, complete global transaction by the node
Coordination and local data bank conversion etc. all control functions；System response external uses when requesting: 1. query decomposition, will be global
Inquiry is divided into several subqueries, and each inquiry relates only to a certain node data, can be by local data base management system (local DBMS) (part
DBMS it) completes；2. selection operation execution order, determine connection and and operate order；3. selection executes operating method, raising is held
Line efficiency establishes data rapid responding mechanis.
In the step S3 specifically:
S31: the ventilation parameter in the tunnel of the acquisition tunnel of being determined coefficient of frictional resistance,
S32: will acquire the tunnel of being determined coefficient of frictional resistance tunnel ventilation parameter according to tunnel attribute with it is each
Three kinds of tunnel attributes in classification data superclass are compared, and look for described in three kinds of tunnel attributes corresponding with the ventilation parameter
Classification data superclass；
S33: and then by all kinds of impact factors in the ventilation parameter and in the subclass for belonging to the classification data superclass
In impact factor be compared, find all subclass corresponding with all kinds of impact factors in the ventilation parameter；
S34: asking the intersection of coefficient of frictional resistance numberical range in each subclass, obtains initial tunnel coefficient of frictional resistance
Recommended value range；
S35: the three dimensional analysis model of the corresponding each impact factor of each coefficient of frictional resistance within the scope of observation recommended value, it will
There is the most friction of total degree in three dimensional analysis model in each coefficient of frictional resistance within the scope of recommended value in each impact factor
Recommended value of the resistance coefficient numerical value as tunnel coefficient of frictional resistance.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although referring to compared with
Good embodiment describes the invention in detail, those skilled in the art should understand that, it can be to skill of the invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of technical solution of the present invention, should all be covered at this
In the scope of the claims of invention.
Claims (7)
1. a kind of airway coefficient of frictional resistance fast determination method based on big data, it is characterised in that: comprising steps of
S1: acquisition dash supplemental characteristic and the corresponding tunnel coefficient of frictional resistance of dash supplemental characteristic；
S2: dash supplemental characteristic is subjected to successively secondary classification according to tunnel attribute, establishes classification data superclass；It will be female
Set is classified according to the classification of impact factor, establishes classification data subclass, and establish the impact factor of every seed set
Three dimensional analysis model；Wherein, the impact factor is that influence tunnel coefficient of frictional resistance value is big in dash supplemental characteristic
Small supplemental characteristic；
S3: the ventilation parameter in the tunnel of the acquisition tunnel of being determined coefficient of frictional resistance, and by the dash parameter and each point
Class data superclass is compared, find with the matched classification data superclass of the dash parameter, then according to collecting
Impact factor find the corresponding subclass of classification data superclass, and will affect the impact factor that the factor and corresponding subset are closed
Three dimensional analysis model compares, and provides the recommended value of the coefficient of frictional resistance in the tunnel.
2. the airway coefficient of frictional resistance fast determination method according to claim 1 based on big data, feature
It is: further includes step S4: by the corresponding impact factor of recommended value of the coefficient of frictional resistance in the tunnel provided step S3
Corresponding coefficient of frictional resistance value frequency of occurrence increases by 1 in three dimensional analysis model, obtains the three-dimensional point of updated impact factor
Analyse model；Wherein, the three dimensional analysis model of the impact factor includes influence factor value, coefficient of frictional resistance and frictional resistance system
Number numerical value frequency of occurrence.
3. the airway coefficient of frictional resistance fast determination method according to claim 2 based on big data, feature
Be: the tunnel attribute includes three kinds of tunnel attributes, i.e. roadway support form, tunnel classification and wall region feature.
4. the airway coefficient of frictional resistance fast determination method according to claim 3 based on big data, feature
Be: the impact factor includes drift section size, tunnel atmospheric density, tunnel ponding and tunnel wall surface camber.
5. the airway coefficient of frictional resistance fast determination method according to claim 4 based on big data, feature
It is: dash supplemental characteristic is subjected to successively secondary classification according to tunnel attribute in the step S2, establishes classification data
Superclass specifically includes:
S21: dash supplemental characteristic is classified according to one of tunnel attribute classification, it is all kinds of to establish the tunnel attribute
The mapping relations of other dash parameter and tunnel coefficient of frictional resistance numberical range；
S22: by the sorted dash parameter of step S21 according to the other one of classification of remaining two kinds of tunnel Attribute class into
Row classification, establishes the sorted dash parameter of tunnel attribute step S21 of all categories and tunnel coefficient of frictional resistance number
It is worth the mapping relations of range；
S23: by the sorted dash parameter of step S22 according to a kind of other one of classification of remaining tunnel Attribute class into
Row classification, establishes the sorted dash parameter of tunnel attribute step S22 of all categories and tunnel coefficient of frictional resistance number
It is worth the mapping relations of range, forms the mapping corresponding with three kinds of tunnel attributes of each tunnel coefficient of frictional resistance numberical range and close
System, and establish classification data superclass；Each element in the classification data superclass is three kinds of tunnel attributes, three kinds of tunnels
The coefficient of frictional resistance numberical range and the coefficient of frictional resistance numerical value frequency of occurrence of attribute mapping.
6. the airway coefficient of frictional resistance fast determination method according to claim 5 based on big data, feature
It is: superclass is classified according to the classification of impact factor in the step S2, establishes classification data subclass, and establish
The three dimensional analysis model of the impact factor of every seed set specifically:
S24: the dash parameter that step S23 classifies is classified according to the classification of impact factor, establishment step S23 classification
The mapping relations of impact factor and tunnel coefficient of frictional resistance numberical range afterwards, formed tunnel coefficient of frictional resistance numberical range with
Impact factor and the corresponding mapping relations of three kinds of tunnel attributes, and classification data subclass is established, each classification data
The coefficient of frictional resistance mapped comprising three kinds of tunnel attributes, impact factor, three kinds of tunnel attributes and impact factor in subclass
Numberical range and the coefficient of frictional resistance numerical value frequency of occurrence；
S25: using the influence factor value in each classification data subclass as X-axis, using coefficient of frictional resistance as Y-axis, with the resistance that rubs
Force coefficient numerical value frequency of occurrence is Z axis, establishes three-dimensional system of coordinate, that is, establishes the three dimensional analysis model of impact factor.
7. the airway coefficient of frictional resistance fast determination method according to claim 6 based on big data, feature
It is: in the step S3 specifically:
S31: the ventilation parameter in the tunnel of the acquisition tunnel of being determined coefficient of frictional resistance,
S32: will acquire the tunnel of being determined coefficient of frictional resistance tunnel ventilation parameter according to tunnel attribute with each classification
Three kinds of tunnel attributes in data superclass are compared, and look for and dividing described in three kinds of tunnel attributes corresponding with the ventilation parameter
Class data superclass；
S33: and then by all kinds of impact factors in the ventilation parameter and in the subclass for belonging to the classification data superclass
Impact factor is compared, and finds all subclass corresponding with all kinds of impact factors in the ventilation parameter；
S34: asking the intersection of coefficient of frictional resistance numberical range in each subclass, obtains building for initial tunnel coefficient of frictional resistance
View value range；
S35: the three dimensional analysis model of the corresponding each impact factor of each coefficient of frictional resistance within the scope of observation recommended value will suggest
There is the most frictional resistance of total degree in three dimensional analysis model in each coefficient of frictional resistance within the scope of value in each impact factor
Recommended value of the factor v as tunnel coefficient of frictional resistance.
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| CN109854299B (en) | 2020-10-23 |
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