CN109854299B - Method for rapidly determining friction resistance coefficient of ventilation roadway based on big data - Google Patents
Method for rapidly determining friction resistance coefficient of ventilation roadway based on big data Download PDFInfo
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Abstract
The invention provides a method for quickly determining the friction resistance coefficient of an air tunnel based on big data, which comprises the following steps: s1: acquiring roadway ventilation parameter data and roadway friction resistance coefficients corresponding to the roadway ventilation parameter data; s2: establishing a classification data mother set; establishing classification data subsets, and establishing a three-dimensional analysis model of the influence factors of each seed set; s3: acquiring ventilation parameters of a roadway of which the friction resistance coefficient of the roadway is to be determined, finding a classification data mother set and a classification data child set matched with the ventilation parameters of the roadway, and giving a suggested value of the friction resistance coefficient of the roadway; the invention forms a comprehensive, dynamically updated and accurate big data set of a friction resistance coefficient system by establishing a classified data mother set and a classified data child set of the ventilation parameter data of the roadway and the friction resistance coefficient of the roadway, searches the data set of similar roadway in the data set of the similar roadway according to the attributes of the roadway, then searches similar influence factors in the data set of the similar roadway and rapidly determines the friction resistance coefficient.
Description
Technical Field
The invention relates to the technical field of prediction of mine roadway friction resistance coefficients, in particular to a method for quickly determining ventilation roadway friction resistance coefficients based on big data.
Background
China is a big coal country, the underground mining is still the main mining mode in China, and the prevention and the control of mine disasters are the important factor in coal mining; mine ventilation is an important guarantee means for preventing and treating mine disasters, and the ventilation system meets the breathing requirements of underground workers by conveying fresh air to various wind utilization places in a mine, and simultaneously achieves the purposes of diluting harmful gas, adjusting the temperature of the mine and the like. When mine disasters occur, the ventilation system implementation equipment is adjusted, so that the accident expansion can be effectively prevented, the occurrence of chain accidents is blocked, and the life and property safety is guaranteed to the maximum extent, so that the ventilation system plays an important role in mine safety production.
In order to effectively guarantee the reliability and stability of a ventilation system, understand the running condition of the mine ventilation system and master the ventilation resistance distribution of the whole mine, but the problem of inaccurate measurement commonly exists when the ventilation resistance is measured in the whole mine at present. Data measured by various measuring methods also need manual processing and error adjustment, and can be brought into a mine ventilation system to carry out ventilation network calculation until the ventilation conditions (air volume, air speed and resistance distribution) of the whole mine are simulated. In ventilation simulation, when an untested roadway is met, only one friction resistance coefficient can be set according to experience. Therefore, it is necessary to establish a comprehensive, dynamically updated and accurate frictional resistance coefficient system database so as to accurately determine the roadway resistance according to the determined frictional resistance coefficient during ventilation simulation.
Disclosure of Invention
In view of the above, the present invention provides a method for rapidly determining a friction resistance coefficient of an air tunnel based on big data, which forms a comprehensive, dynamically updated and accurate big data set of a friction resistance coefficient system by establishing a classified data parent set and a classified data child set of tunnel ventilation parameter data and a tunnel friction resistance coefficient, searches data sets of similar tunnels in the big data set through tunnel attributes, and then searches similar influence factors in the data sets of the similar tunnels to rapidly determine the friction resistance coefficient, thereby rapidly calculating the resistance of the tunnel.
The invention provides a method for quickly determining the friction resistance coefficient of an air tunnel based on big data, which comprises the following steps:
s1: acquiring roadway ventilation parameter data and roadway friction resistance coefficients corresponding to the roadway ventilation parameter data;
s2: classifying the tunnel ventilation parameter data layer by layer according to the tunnel attributes, and establishing a classification data mother set; classifying the parent set according to the category of the influence factors, establishing a classification data subset, and establishing a three-dimensional analysis model of the influence factors of each seed set; the influence factors are parameter data influencing the value of the roadway friction resistance coefficient in the roadway ventilation parameter data;
s3: acquiring ventilation parameters of a roadway of which the friction resistance coefficient of the roadway is to be determined, comparing the ventilation parameters of the roadway with each classification data mother set, finding a classification data mother set matched with the ventilation parameters of the roadway, then finding a subset corresponding to the classification data mother set according to the acquired influence factors, comparing the influence factors with a three-dimensional analysis model of the influence factors of the corresponding subset, and giving a suggested value of the friction resistance coefficient of the roadway.
Further, the method also includes step S4: increasing the occurrence times of the friction resistance coefficient value corresponding to the influence factor in the three-dimensional analysis model of the influence factor corresponding to the suggested value of the friction resistance coefficient of the roadway, which is given in the step S3, by 1 to obtain an updated three-dimensional analysis model of the influence factor; wherein, the three-dimensional analysis model of the influence factor comprises an influence factor value, a friction resistance coefficient and the number of times of occurrence of the friction resistance coefficient value.
Further, the roadway attributes comprise three roadway attributes, namely roadway support forms, roadway types and roadway wall surface characteristics.
Further, the influence factors comprise the size of the section of the roadway, the air density of the roadway, the water retention of the roadway and the concavity and convexity of the wall surface of the roadway.
Further, in step S2, classifying the tunnel ventilation parameter data layer by layer according to the tunnel attributes, and establishing a classification data parent set specifically includes:
s21: classifying the tunnel ventilation parameter data according to one of the tunnel attribute categories, and establishing a mapping relation between the tunnel ventilation parameters of the tunnel attributes of the various categories and the numerical range of the tunnel friction resistance coefficient;
s22: classifying the tunnel ventilation parameters classified in the step S21 according to one of the remaining two tunnel attribute categories, and establishing a mapping relation between the tunnel ventilation parameters classified in the step S21 of each tunnel attribute category and the numerical range of the tunnel friction resistance coefficient;
s23: classifying the tunnel ventilation parameters classified in the step S22 according to one of the rest tunnel attribute categories, establishing a mapping relation between the tunnel ventilation parameters classified in the step S22 of each tunnel attribute and a tunnel friction resistance coefficient numerical range, forming a mapping relation between each tunnel friction resistance coefficient numerical range and three tunnel attributes, and establishing a classification data master set; and each element in the classification data parent set comprises three roadway attributes, a friction resistance coefficient value range mapped by the three roadway attributes and the number of times of occurrence of the friction resistance coefficient value.
Further, in the step S2, the parent set is classified according to the category of the influence factor, a classification data subset is established, and the establishing of the three-dimensional analysis model of the influence factor of each subset specifically includes:
s24: classifying the tunnel ventilation parameters classified in the step S23 according to the type of the influence factors, establishing a mapping relation between the influence factors and the tunnel friction resistance coefficient numerical range after the classification in the step S23, forming a mapping relation between the tunnel friction resistance coefficient numerical range and the influence factors and between the tunnel friction resistance coefficient numerical range and the three tunnel attributes, and establishing classification data subsets, wherein each classification data subset comprises the three tunnel attributes, the influence factors, the three tunnel attributes, the friction resistance coefficient numerical range mapped by the influence factors and the number of times of occurrence of the friction resistance coefficient;
s25: and establishing a three-dimensional coordinate system, namely establishing a three-dimensional analysis model of the influence factors by taking the influence factor value in each classified data subset as an X axis, the friction resistance coefficient as a Y axis and the friction resistance coefficient value as a Z axis.
Further, the step S3 specifically includes:
s31: collecting ventilation parameters of the roadway of which the frictional resistance coefficient of the roadway is to be determined,
s32: comparing the acquired ventilation parameters of the laneway with the friction resistance coefficient of the laneway to be determined with the attributes of the three laneways in each classification data mother set according to the attributes of the laneway, and finding out the classification data mother set of the attributes of the three laneways corresponding to the ventilation parameters;
s33: then comparing various influence factors in the ventilation parameters with influence factors in the subsets belonging to the classification data master set, and finding out all subsets corresponding to various influence factors in the ventilation parameters;
s34: solving the intersection of the numerical ranges of the frictional resistance coefficients in the sub-sets to obtain the initial suggested value range of the roadway frictional resistance coefficient;
s35: and observing the three-dimensional analysis model of each influence factor corresponding to each friction resistance coefficient in the suggested value range, and taking the friction resistance coefficient value with the maximum total occurrence frequency of the three-dimensional analysis model of each influence factor in each friction resistance coefficient in the suggested value range as the suggested value of the roadway friction resistance coefficient.
The invention has the beneficial effects that: the invention forms a comprehensive, dynamically updated and accurate big data set of a friction resistance coefficient system by establishing a classified data mother set and a classified data child set of the ventilation parameter data of the roadway and the friction resistance coefficient of the roadway, searches the data set of the similar roadway in the data set of the similar roadway according to the attributes of the roadway, then searches the similar influence factors in the data set of the similar roadway, and rapidly determines the friction resistance coefficient, thereby rapidly calculating the resistance of the roadway.
Drawings
The invention is further described below with reference to the following figures and examples:
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is an exemplary diagram of a mapping relationship of a sorted data mother set;
FIG. 3 is an exemplary diagram of a three-dimensional analytical model of an impact factor;
fig. 4 is a schematic diagram of data distributed storage.
Detailed Description
As shown in fig. 1, the method for rapidly determining the friction resistance coefficient of the ventilation roadway based on big data provided by the invention comprises the following steps:
s1: acquiring roadway ventilation parameter data and roadway friction resistance coefficients corresponding to the roadway ventilation parameter data; and the roadway ventilation parameter data and the roadway friction resistance coefficient corresponding to the roadway ventilation parameter data are derived from data actually measured and calculated in all coal mines all over the country.
S2: classifying the tunnel ventilation parameter data layer by layer according to the tunnel attributes, and establishing a classification data mother set; classifying the parent set according to the category of the influence factors, establishing a classification data subset, and establishing a three-dimensional analysis model of the influence factors of each seed set; the influence factors are parameter data influencing the value of the roadway friction resistance coefficient in the roadway ventilation parameter data;
s3: acquiring ventilation parameters of a roadway of which the friction resistance coefficient of the roadway is to be determined, comparing the ventilation parameters of the roadway with each classification data mother set, finding a classification data mother set matched with the ventilation parameters of the roadway, then finding a subset corresponding to the classification data mother set according to the acquired influence factors, comparing the influence factors with a three-dimensional analysis model of the influence factors of the corresponding subset, and giving a suggested value of the friction resistance coefficient of the roadway. By the method, a classified data master set and a classified data sub-set of the roadway ventilation parameter data and the roadway friction resistance coefficient are established to form a comprehensive, dynamically updated and accurate large data set of the friction resistance coefficient system, the data sets of similar roadways are searched in the large data set according to the roadway attributes, then similar influence factors are searched in the data sets of the similar roadways, the friction resistance coefficient is rapidly determined, and therefore the resistance of the roadway is rapidly calculated. The calculation formula of the tunnel ventilation resistance is as follows:
wherein the content of the first and second substances,
is the coefficient of friction resistance of the roadway (constant, unit: N.s)2/m4) And L is a roadway length (unit: m), U is the roadway perimeter (unit: m), S is the cross-sectional area (unit: m is2)。
Further comprising step S4: increasing the occurrence times of the friction resistance coefficient value corresponding to the influence factor in the three-dimensional analysis model of the influence factor corresponding to the suggested value of the friction resistance coefficient of the roadway, which is given in the step S3, by 1 to obtain an updated three-dimensional analysis model of the influence factor; the three-dimensional analysis model of the influence factor is dynamically updated by the method, so that the three-dimensional analysis model of the influence factor is trained while the three-dimensional analysis model of the influence factor is called, and the precision of the three-dimensional analysis model of the influence factor is continuously improved.
The roadway attributes comprise three roadway attributes, namely roadway support forms, roadway categories and roadway wall surface characteristics. The roadway support forms comprise anchor spraying support, sand slurry spraying support, anchor rod support, stone masonry arch support, rubble masonry arch support, concrete shed support, "U" section steel support, I-steel and steel rail support, shield type support, support type support, single hydraulic prop, metal friction prop, hinged top beam and wood prop. The roadway categories include a rail roadway, a rail inclined roadway, a rail conveyor roadway, a ventilation pedestrian roadway (with steps) and a ventilation pedestrian roadway (without steps). The roadway wall surface characteristics comprise smooth blasting, convex-concave degree <150, common blasting and convex-concave degree >150, anchor rods expose 100-200 anchor intervals of 600-1000, anchor rods expose 150-200 anchor intervals of 600-800, the wall surface is rough and smooth, the section is 5-9, the longitudinal diameter is 4-5, the section is 5-8, the longitudinal diameter is 4-8, the section is 9-10, the longitudinal diameter is 4-8, the section is 4-6, the longitudinal diameter is 7-9, the section is 9-10 and the longitudinal diameter is 4-8.
The influence factors comprise the size of a roadway section, the air density of the roadway, the water accumulation of the roadway and the concavity and convexity of the wall surface of the roadway.
As shown in fig. 2, the step S2 of classifying the roadway ventilation parameter data layer by layer according to the roadway attributes, and the establishing a classification data parent set specifically includes:
s21: classifying the tunnel ventilation parameter data according to one of the tunnel attribute categories, and establishing a mapping relation between the tunnel ventilation parameters of the tunnel attributes of the various categories and the numerical range of the tunnel friction resistance coefficient; in this embodiment, step S21 is to classify the tunnel attributes according to the tunnel support form.
S22: classifying the tunnel ventilation parameters classified in the step S21 according to one of the remaining two tunnel attribute categories, and establishing a mapping relation between the tunnel ventilation parameters classified in the step S21 of each tunnel attribute category and the numerical range of the tunnel friction resistance coefficient; in this embodiment, step S22 is to classify the lane type according to the lane support form.
S23: classifying the tunnel ventilation parameters classified in the step S22 according to one of the rest tunnel attribute categories, establishing a mapping relation between the tunnel ventilation parameters classified in the step S22 of each tunnel attribute and a tunnel friction resistance coefficient numerical range, forming a mapping relation between each tunnel friction resistance coefficient numerical range and three tunnel attributes, and establishing a classification data master set; and each element in the classification data parent set comprises three roadway attributes, a friction resistance coefficient value range mapped by the three roadway attributes and the number of times of occurrence of the friction resistance coefficient value. In this embodiment, step S23 is to classify the tunnel wall surface according to the tunnel wall surface characteristics in the form of a tunnel support. The mapping relationship between the numerical range of the friction resistance coefficient of each roadway and the three roadway attributes can be shown in table 1.
TABLE 1
Wherein, each group of the numerical range of the friction resistance coefficient corresponds to the roadway attribute of a roadway support form, the roadway attribute of a roadway category and the roadway attribute of a roadway wall surface characteristic. The roadway ventilation parameter data are subjected to preliminary contrast mapping through the classified data master set, and the friction resistance coefficient numerical range corresponding to the roadway ventilation parameter data is limited and reduced, so that the cost for finding the corresponding friction resistance coefficient numerical range through the classified data subset is reduced. Specifically, for example, the data base set S is classified1Including elements-rail drift, anchor-shotcrete support, smooth blasting, convex-concave degree<150; classification data mother set S2Including elements-inclined track lane, anchor-shotcrete support, common blasting, convex-concave degree>150
As shown in fig. 3, in step S2, classifying the parent set according to the category of the impact factor, establishing a classification data subset, and establishing a three-dimensional analysis model of the impact factor of each subset, specifically:
s24: classifying the tunnel ventilation parameters classified in the step S23 according to the type of the influence factors, establishing a mapping relation between the influence factors and the tunnel friction resistance coefficient numerical range after the classification in the step S23, forming a mapping relation between the tunnel friction resistance coefficient numerical range and the influence factors and between the tunnel friction resistance coefficient numerical range and the three tunnel attributes, and establishing classification data subsets, wherein each classification data subset comprises the three tunnel attributes, the influence factors, the three tunnel attributes, the friction resistance coefficient numerical range mapped by the influence factors and the number of times of occurrence of the friction resistance coefficient;
s25: and establishing a three-dimensional coordinate system, namely establishing a three-dimensional analysis model of the influence factors by taking the influence factor value in each classified data subset as an X axis, the friction resistance coefficient as a Y axis and the occurrence frequency of the friction resistance coefficient value as a Z axis. The three-dimensional analysis model can be visually observed by establishing the three-dimensional analysis model, the occurrence frequency of the friction resistance coefficient corresponding to the influence factor is convenient for a user to quickly determine the suggested value of the roadway friction resistance coefficient.
As shown in fig. 4, in the actual application process, since the classified data parent set and the classified data subset set established in step S2 have large data sizes, when the three-dimensional analysis model of the impact factors corresponding to the classified data parent set, the classified data subset data and the classified data subset data is stored, mass data storage is realized by using a big data storage technology, and a fast data storage and reading mechanism is established, wherein the storage mode adopts distributed storage. The large data collection adopts a distributed database system, and is characterized in that data are physically distributed on different nodes of a computer network and logically belong to the same system. The system adopts a management system with centralized global control, namely, the global control components are centralized on a certain node, and the node completes all control functions of coordination of global transactions, local database conversion and the like; when the system responds to the external request, the following steps are adopted: query decomposition, dividing the global query into a plurality of subqueries, wherein each query only relates to a certain node data and can be completed by a local database management system (local DBMS); selecting an operation execution sequence, and determining the sequence of connection and parallel operation; selecting an execution operation method, improving execution efficiency and establishing a data quick response mechanism.
The step S3 specifically includes:
s31: collecting ventilation parameters of the roadway of which the frictional resistance coefficient of the roadway is to be determined,
s32: comparing the acquired ventilation parameters of the laneway with the friction resistance coefficient of the laneway to be determined with the attributes of the three laneways in each classification data mother set according to the attributes of the laneway, and finding out the classification data mother set of the attributes of the three laneways corresponding to the ventilation parameters;
s33: then comparing various influence factors in the ventilation parameters with influence factors in the subsets belonging to the classification data master set, and finding out all subsets corresponding to various influence factors in the ventilation parameters;
s34: solving the intersection of the numerical ranges of the frictional resistance coefficients in the sub-sets to obtain the initial suggested value range of the roadway frictional resistance coefficient;
s35: and observing the three-dimensional analysis model of each influence factor corresponding to each friction resistance coefficient in the suggested value range, and taking the friction resistance coefficient value with the maximum total occurrence frequency of the three-dimensional analysis model of each influence factor in each friction resistance coefficient in the suggested value range as the suggested value of the roadway friction resistance coefficient.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (5)
1. A method for rapidly determining the friction resistance coefficient of an air tunnel based on big data is characterized by comprising the following steps: the method comprises the following steps:
s1: acquiring roadway ventilation parameter data and roadway friction resistance coefficients corresponding to the roadway ventilation parameter data;
s2: classifying the tunnel ventilation parameter data layer by layer according to the tunnel attributes, and establishing a classification data mother set; classifying the parent set according to the category of the influence factors, establishing a classification data subset, and establishing a three-dimensional analysis model of the influence factors of each seed set; the influence factors are parameter data influencing the value of the roadway friction resistance coefficient in the roadway ventilation parameter data;
s3: acquiring ventilation parameters of a roadway of which the friction resistance coefficient of the roadway is to be determined, comparing the ventilation parameters of the roadway with each classification data mother set, finding a classification data mother set matched with the ventilation parameters of the roadway, then finding a subset corresponding to the classification data mother set according to the acquired influence factors, comparing the influence factors with a three-dimensional analysis model of the influence factors of the corresponding subset, and giving a suggested value of the friction resistance coefficient of the roadway;
in the step S2, the tunnel ventilation parameter data is classified layer by layer according to the tunnel attributes, and establishing a classification data parent set specifically includes:
s21: classifying the tunnel ventilation parameter data according to one of the tunnel attribute categories, and establishing a mapping relation between the tunnel ventilation parameters of the tunnel attributes of the various categories and the numerical range of the tunnel friction resistance coefficient;
s22: classifying the tunnel ventilation parameters classified in the step S21 according to one of the remaining two tunnel attribute categories, and establishing a mapping relation between the tunnel ventilation parameters classified in the step S21 of each tunnel attribute category and the numerical range of the tunnel friction resistance coefficient;
s23: classifying the tunnel ventilation parameters classified in the step S22 according to one of the rest tunnel attribute categories, establishing a mapping relation between the tunnel ventilation parameters classified in the step S22 of each tunnel attribute and a tunnel friction resistance coefficient numerical range, forming a mapping relation between each tunnel friction resistance coefficient numerical range and three tunnel attributes, and establishing a classification data master set; each element in the classification data parent set comprises three roadway attributes, a friction resistance coefficient value range mapped by the three roadway attributes and the number of times of occurrence of the friction resistance coefficient value;
in step S2, classifying the parent set according to the category of the influence factor, establishing a classification data subset, and establishing a three-dimensional analysis model of the influence factor of each subset, specifically:
s24: classifying the tunnel ventilation parameters classified in the step S23 according to the type of the influence factors, establishing a mapping relation between the influence factors and the tunnel friction resistance coefficient numerical range after the classification in the step S23, forming a mapping relation between the tunnel friction resistance coefficient numerical range and the influence factors and between the tunnel friction resistance coefficient numerical range and the three tunnel attributes, and establishing classification data subsets, wherein each classification data subset comprises the three tunnel attributes, the influence factors, the three tunnel attributes, the friction resistance coefficient numerical range mapped by the influence factors and the number of times of occurrence of the friction resistance coefficient;
s25: and establishing a three-dimensional coordinate system, namely establishing a three-dimensional analysis model of the influence factors by taking the influence factor value in each classified data subset as an X axis, the friction resistance coefficient as a Y axis and the occurrence frequency of the friction resistance coefficient value as a Z axis.
2. The big-data-based rapid determination method of the friction resistance coefficient of the ventilation roadway according to claim 1, characterized in that: further comprising step S4: increasing the occurrence times of the friction resistance coefficient value corresponding to the influence factor in the three-dimensional analysis model of the influence factor corresponding to the suggested value of the friction resistance coefficient of the roadway, which is given in the step S3, by 1 to obtain an updated three-dimensional analysis model of the influence factor; wherein, the three-dimensional analysis model of the influence factor comprises an influence factor value, a friction resistance coefficient and the number of times of occurrence of the friction resistance coefficient value.
3. The big-data-based rapid determination method for the friction resistance coefficient of the ventilation roadway according to claim 2, wherein: the roadway attributes comprise three roadway attributes, namely roadway support forms, roadway categories and roadway wall surface characteristics.
4. The big-data-based rapid determination method of the friction resistance coefficient of the ventilation roadway according to claim 3, wherein: the influence factors comprise the size of a roadway section, the air density of the roadway, the water accumulation of the roadway and the concavity and convexity of the wall surface of the roadway.
5. The big-data-based rapid determination method of the friction resistance coefficient of the ventilation roadway according to claim 1, characterized in that: the step S3 specifically includes:
s31: collecting ventilation parameters of the roadway of which the frictional resistance coefficient of the roadway is to be determined,
s32: comparing the acquired ventilation parameters of the laneway with the friction resistance coefficient of the laneway to be determined with the attributes of the three laneways in each classification data mother set according to the attributes of the laneway, and finding out the classification data mother set of the attributes of the three laneways corresponding to the ventilation parameters;
s33: then comparing various influence factors in the ventilation parameters with influence factors in the subsets belonging to the classification data master set, and finding out all subsets corresponding to various influence factors in the ventilation parameters;
s34: solving the intersection of the numerical ranges of the frictional resistance coefficients in the sub-sets to obtain the initial suggested value range of the roadway frictional resistance coefficient;
s35: and observing the three-dimensional analysis model of each influence factor corresponding to each friction resistance coefficient in the suggested value range, and taking the friction resistance coefficient value with the maximum total occurrence frequency of the three-dimensional analysis model of each influence factor in each friction resistance coefficient in the suggested value range as the suggested value of the roadway friction resistance coefficient.
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