CN107239844B - Oil consumption prediction method for petroleum transportation vehicle based on Hadoop - Google Patents
Oil consumption prediction method for petroleum transportation vehicle based on Hadoop Download PDFInfo
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Abstract
The invention relates to a method for predicting oil consumption of a petroleum transportation vehicle based on Hadoop. The method comprises the following steps: (1) collecting data; (2) storing data; (3) calculating a correction coefficient of the oil consumption influence factor; (4) and (4) predicting and calculating the fuel consumption quota. The method of the invention takes the petroleum transportation vehicle as a research object, builds a relevant calculation model by relying on a big data analysis processing technology, analyzes and calculates the oil consumption of the vehicle, not only provides a reference basis for a transportation enterprise to set a reasonable oil consumption quota, but also can find a root cause of high oil consumption in the research process, thereby taking effective measures to reduce the oil consumption, saving the cost and saving the energy.
Description
Technical Field
The invention relates to a method for predicting oil consumption of a petroleum transportation vehicle based on Hadoop, in particular to a method for predicting oil consumption of the petroleum transportation vehicle through big data analysis and processing, relevant calculation model establishment and analysis and calculation, and belongs to the technical field of transportation vehicle management.
Background
Under the large background of the national strategy of energy conservation and emission reduction, various industries make various efforts to reduce carbon emission, and one important aspect of realizing energy efficiency improvement is to reduce gas consumption, and the transportation industry occupies most of energy consumption and carbon emission.
The petroleum transportation enterprises have many transportation vehicles, heavy transportation tasks, more points, long lines and wide areas, and the vehicle oil consumption is the main consumption of the petroleum transportation enterprises, and is also the main source for reducing the cost, saving the energy and increasing the benefit.
According to reports, 13% of the cost of petroleum transportation enterprises is fuel consumption of transportation vehicles, the whole fuel consumption cost accounts for 49% of production change cost of the enterprises, the fuel consumption greatly influences the overall benefits of the enterprises, meanwhile, the fuel management is also one of the key points of vehicle management, and accurate and reliable vehicle fuel consumption quota standards need to be made for standard management, cost control and feasible performance evaluation and assessment standards. The oil transportation enterprise vehicles have complex road conditions and severe operating environment due to specific transportation task requirements, and the vehicles of the transportation enterprises are distributed all over the country and have the characteristics of multiple points, long lines and wide area, so that the common oil consumption standard of the same vehicle type is difficult to meet the actual situation in the actual work. The basic vehicle management department cannot know the specific oil consumption condition of various vehicle types in different road sections, different environments and different hauling quantities, so that the vehicle management department of transportation enterprises needs to make a scientific oil quota standard in a working area for each vehicle type and brings the oil quota standard into the assessment, thereby avoiding the waste of fuel oil and saving the cost for enterprises. However, the traditional quantitative calculation or measurement method for fuel consumption cannot give concrete and accurate fuel consumption values of vehicles under various conditions according to the characteristics of the petroleum transportation vehicles.
The conventional research on fuel consumption management has the following problems.
(1) The related data are obtained through test tests in most of the formulation of the fuel consumption quota, and various complex environments in practice cannot be widely considered due to the limited test environment, so that the fuel consumption quota result does not meet the actual situation to a great extent.
(2) The influence factors considered by the fuel consumption model calculation method are not comprehensive enough, the correction values of the influence factors are not accurate, one part of the influence factors are related to the reference standard, the other part of the influence factors are estimated by experience, the standard reference value is only tested in the standard environment before years, and the reference significance of the standard reference value is conceivable today after years.
(3) The oil consumption analysis also needs to be targeted, and the oil consumption analysis methods cannot be the same for different enterprises, different vehicles and different transportation tasks and routes, so that it is not practical to directly apply the oil consumption research results in other fields to the oil transportation vehicles.
With the development of OBD, Beidou/GPS and big data analysis and processing technologies, the traditional technical means can not meet the precision requirement on data in a big environment.
Disclosure of Invention
The invention aims to provide a petroleum transportation vehicle oil consumption prediction method based on Hadoop aiming at the defects in the prior art, provides decision support for making a petroleum transportation vehicle oil consumption quota, and provides a reference basis for cost reduction and efficiency improvement of transportation enterprises.
The technical solution of the invention is as follows: a petroleum transportation vehicle oil consumption prediction method based on Hadoop is characterized in that a large data analysis processing technology is used for constructing an oil consumption prediction analysis model, model parameters are trained through massive historical data, so that a model result is more accurate, the oil consumption of a petroleum transportation vehicle can be accurately predicted through the method, and a more accurate reference basis is provided for a transportation enterprise to formulate an oil consumption quota; the method specifically comprises the following steps.
The first step is data acquisition.
The source data required by oil consumption prediction comprises road condition data, temperature data, altitude data, vehicle performance data and driving condition data, wherein the road condition data, the temperature data and the altitude data are acquired through access of an external system, the vehicle performance data and the driving condition data are acquired through a vehicle-mounted OBD terminal and a sensor terminal, and in addition, vehicle GPS position data are required to be acquired in real time for matching the corresponding environment condition of vehicle transportation in the calculation process.
And step two, storing data.
After the data are collected, the data need to be classified and stored according to data types, static data needed or generated in the oil consumption analysis process are stored through a relational database, and the temperature, the altitude and the driving condition are set as the static data and are stored through the relational database; and storing a large amount of real-time data such as GPS position data, road condition data, vehicle performance data and the like through an HBase distributed database.
Furthermore, the big data calculation is to collect sample data from the Hbase database and the relational database for analysis, the collection of the sample data from the Hbase database is completed through Hive SQL, and the collection of the sample data from the relational database uses a Sqoop tool.
The data storage structure after collection is shown in table 1.
TABLE 1 data storage Structure
And step three, calculating a correction coefficient of the oil consumption influence factor.
The transportation process of the petroleum transportation vehicle has particularity, and in order to calculate the oil consumption of the petroleum transportation vehicle more accurately, correction values of factors influencing the oil consumption need to be analyzed according to the transportation environment and other factors.
The oil consumption quota of the petroleum transportation vehicle is generally counted according to a freight note, the oil consumption quota is set under the conditions of a specified vehicle type and a specified line, and the calculation of the oil consumption quota is based on accurate standard oil consumption and reliable correction factors of various influence factors.
Calculating a correction coefficient of the oil consumption influence factor, wherein the calculation comprises vehicle type division, influence factor reference value determination and standard oil consumption quota determination; and calculating an influence factor correction coefficient.
And step three (1) dividing vehicle types.
According to the actual situation of the petroleum transportation vehicle, the vehicle is divided into x less than or equal to 7t, 7t < x less than or equal to 14t, 14t < x less than or equal to 21t, 21t < x less than or equal to 28t, 28t < x less than or equal to 35t and 35t < x, wherein x is the vehicle type prepared weight and the unit is ton, because the oil consumption standards of different vehicle types are different, and the oil consumption correction factors of various vehicle types are different.
And step three (2) is determination of reference values of various influence factors.
The method comprises the steps of considering influence factors including road conditions, temperature, altitude, vehicle performance, driving conditions and vehicle working conditions, wherein the road conditions are classified according to road grades, the temperature is divided according to the average monthly temperature, the altitude is divided according to the height, the vehicle performance is calibrated by the accumulated mileage of the running vehicle, the driving conditions are calibrated by the driving age of a driver, and the vehicle working conditions are calibrated by the service life of a vehicle engine.
Reference values and other standard values of factors such as road conditions, temperature, altitude and the like are clearly given in the national standard of 'the running fuel consumption of a truck', the method takes the standard as a reference, but the method is mainly considered according to the characteristics of petroleum transportation vehicles, transportation tasks, transportation lines and the like, so for the factors, the method gives a new standard as follows:
the condition that the road condition correction factor selects the 'type 1 road' is a reference value of 1.00, and the national standard is the same.
The temperature correction factor selects the condition that the average temperature in the month is 28-5 ℃ as a reference value of 1.00, because the condition of 28-5 ℃ is the most common and is the same as the national standard.
The altitude correction factor selects the case of altitude "< 500 m" as the reference value 1.00, the same as the national standard.
The vehicle performance correction factor selects the condition that the mileage of the vehicle is 0-10 km as the reference value 1.00, wherein 0-10 km is the condition that the mileage is the minimum, so the vehicle performance is the best.
The condition that the service life of the engine is less than 3 years is selected as the reference value 1.00 by the vehicle working condition correction factor, and the shorter the service life of the engine is, the better the vehicle working condition is.
And step three (3) is determination of the rated value of the standard fuel consumption.
The method comprises the steps of calculating a correction factor of each condition, wherein the correction factor is a standard fuel consumption quota, and the standard fuel consumption quota is an actual fuel consumption value calculated under the condition that the reference value of each correction factor corresponds to the standard fuel consumption quota. Therefore, the operating environment for the standard fuel consumption rating identified above is: the driving road condition is 1 type of road, the average temperature per month in a driving area is 28-5 ℃, the altitude of the driving area is less than 500m, the accumulated driving mileage of a vehicle is less than 10 ten thousands km, the driving age of a driver is more than 10 years, and the service life of an engine of the vehicle is less than 3 years, wherein the operating environment is called as a standard environment.
The system database stores a large amount of historical waybill data, and also comprises data such as weather, road conditions, vehicle working conditions, driver conditions, vehicle accumulated driving mileage and the like, the system background finds out the conditions meeting the standard environment in the historical waybill of the vehicle through big data operation, calculates the oil consumption value of one hundred kilometers of each condition according to the oil consumption data of the actual waybill, and then calculates the average value of all the oil consumption values of one hundred kilometers, wherein the average value is the standard oil consumption value Qb.
And step three (4) calculating the correction coefficient of the influence factor.
Because the calculation methods of different vehicle types are similar, the vehicle type with 7t < x < 14t is taken as an example, and the correction coefficients of various influencing factors under the vehicle type are calculated.
Under the same type of vehicle, the calculation methods of the correction coefficients of the related influencing factors are similar, and the following describes the calculation method of the correction coefficients and the general algorithm formula of the correction coefficients by taking the road condition influencing factors as an example.
The current condition is that the correction coefficient corresponding to the 1-type road is the reference value, and the fuel consumption value Q of one hundred kilometers under the reference value environment is also knownbAnd calculating the correction coefficient corresponding to the 2-type road according to the existing conditions.
According to the idea of the algorithm, an environmental condition for vehicle operation is set firstly: the driving road condition is 2 types of roads, the average temperature per month in a driving area is 28-5 ℃, the altitude of the driving area is less than 500m, the accumulated driving mileage of the vehicle is less than 10 km, the driving age of a driver is more than 10 years, and the service life of an engine of the vehicle is less than 3 years; the only difference between the operating environment and the reference value environment is that the road conditions are different.
The system background finds out the historical fuel consumption of the waybill conforming to the set environment through inquiry, calculates the fuel consumption value of one hundred kilometers of each waybill according to the kilometers of the actual waybill, and then calculates the average value of all the fuel consumption values of one hundred kilometers, wherein the average value is the fuel consumption value Q of one hundred kilometers under the preset environmentτ2。
Then the process of the first step is carried out,
here, kτ2Class 2 road correction coefficients;
therefore, the fuel consumption per hundred kilometers is calculated, and the correction coefficient of the 2 types of roads can be obtained by comparing the fuel consumption per hundred kilometers with the standard fuel consumption.
According to the formula, the method comprises the following steps of,
similarly, the correction coefficients of the remaining roads of other types can be calculated, and the correction coefficients under the conditions of the roads can be obtained by comparing the oil consumption per hundred kilometers of the same type under the set environment with the oil consumption per hundred kilometers under the reference value.
According to the calculation mode, a general calculation formula of the correction coefficients of other influencing factors can be deduced, and the formula is shown as follows.
……
Wherein the content of the first and second substances,
Q1n、...Q(i-1)n、Qin: representing the historical freight note oil consumption which can be inquired under different range values of an influence factor;
L1n、...L(i-1)n、Lin: the inquired driving mileage corresponding to the historical waybill oil consumption is expressed and used for calculating the hundred kilometers of oil consumption;
Q′1、...Q′i-1、Q′i: the hundred kilometers average fuel consumption at different range values representing one influence factor;
k1n、k2n、k3n、k4n、k5n、k6n: the correction coefficient represents other influence factors of n types of roads;
k11、k12、...、k1(i-1)、k1i: and the calculated correction coefficient values of the influencing factor under different range values are represented, wherein a certain correction coefficient is defined as a reference value and takes a value of 1.
According to the idea, all correction coefficients of a certain influencing factor can be calculated.
And step four, predicting and calculating the fuel consumption quota.
Step four (1): and (4) calculating a comprehensive model of the fuel consumption quota.
The oil consumption calculation is based on the freight note, and how to calculate the heavy-load rated oil consumption, the no-load rated oil consumption and the total oil consumption is considered, and the oil consumption rated calculation model of the petroleum transportation vehicle designed by the method is as follows.
The same-weight vehicle type means that the weight of the vehicle is in the same range, the same-weight vehicle type also comprises heavy load/no load in the same state, and the vehicle is in the heavy load/no load state when calculating the heavy load/no load standard oil consumption.
Qs＝Zsz*Lsz+Zsk*Lsk
Zsz＝kw*kh*ky*kj*kf*ki*Z0z
Zsk＝kw*kh*ky*kj*kf*ki*Z0k
Wherein Q iss-real-time waybill estimated fuel consumption;
Zsz-real-time waybill oil consumption per hundred kilometers;
Zsk-real-time waybill no-load hundred kilometers fuel consumption;
Lsz-real-time waybill heavy haul mileage;
Lsk-real-time waybill empty mileage;
Z0z-standard heavy load fuel consumption per kilometer;
Z0k-standard no-load fuel consumption per kilometre;
ki-road condition correction factor;
kw-a temperature correction factor;
kh-an altitude correction factor;
ky-a vehicle performance correction factor;
kj-a vehicle condition correction factor;
kf-a driving situation correction factor;
Qbhistory of fuel consumption [ Q ] of the vehicleb＝Qbz(heavy oil consumption) + Qbk(no-load oil consumption)]；
QtHistorical fuel consumption [ Q ] of the same-weight vehiclet＝Qtz(heavy oil consumption) + Qtk(no-load oil consumption)]；
Lb-historical driving range (L) of the vehicleb＝Lbz+Lbk)；
LtHistorical driving range (L) of the same-weight vehiclet＝Ltz+Ltk)；
n0-a vehicle historical movement number;
m0-historical number of vehicles of the same weight;
1-
-the ratio of the estimated fuel consumption per kilometer of the same-weight vehicle to the fuel consumption quota.
Step four (2): and (4) calculating the heavy load/no load standard oil consumption.
According to the fuel consumption quota calculation comprehensive model, the standard fuel consumption of the vehicle under different load conditions can be calculated, the standard fuel consumption calculation under the no-load condition and the heavy load condition is divided, and the standard fuel consumption calculation is respectively calculated according to the division of the vehicle weight range, so that the following results are obtained.
TABLE 2 Standard Fuel consumption of the same vehicle (No-load)
| Vehicle weight (t) | x≤7 | 7＜x≤14 | 14＜x≤21 | 21＜x≤28 | 28＜x≤35 | 35＜x |
| Standard oil consumption (L) | 30.05 | 34.21 | 38.51 | 42.35 | 45.89 | 49.96 |
。
TABLE 3 Standard Fuel consumption (heavy load) for the same vehicle weight
| Vehicle weight (t) | x≤7 | 7＜x≤14 | 14＜x≤21 | 21＜x≤28 | 28＜x≤35 | 35＜x |
| Standard oil consumption (L) | 30.16 | 34.33 | 38.80 | 42.86 | 46.01 | 50.10 |
。
The invention has the beneficial effects that: the method provides accurate and reliable oil consumption quota in vehicle oil consumption management for enterprises, deeply excavates and analyzes massive vehicle historical data through a Hadoop-based big data analysis and processing technology, and provides a data source and data processing support for an oil consumption analysis model; the beneficial effects of the invention on the oil consumption analysis of the petroleum transportation vehicle are as follows.
(1) By adopting a Hadoop-based big data analysis processing technology, deep mining and analysis can be performed on massive vehicle historical data, and the fuel consumption model is trained through massive historical data, so that the fuel consumption model result is more accurate and reliable, and accurate prediction of the fuel consumption quota of the vehicle under various conditions is really realized.
(2) And (3) constructing a fuel consumption calculation model, calculating the fuel consumption quota of different vehicle types running on a specific route, and providing an important reference basis for perfecting an enterprise performance evaluation system and improving the energy-saving effect.
(3) Effectively promotes the scientific development of energy conservation and emission reduction in the transportation industry, and promotes the construction of the resource-saving and environment-friendly road transportation industry.
Drawings
FIG. 1 is a flow chart of fuel consumption prediction calculation based on Hadoop in the invention.
FIG. 2 is a graph comparing fuel consumption per hundred kilometers, actual fuel consumption per hundred kilometers, and model fuel consumption per hundred kilometers.
Detailed Description
The method of the present invention is further described below with reference to the accompanying drawings.
The embodiment provides the calculation of the correction coefficient norm value of the fuel consumption influence factor by adopting the method.
According to the fuel consumption influence factors and the calculation method of the correction coefficients thereof, exemplary values of the correction coefficients of the influence factors are calculated, as shown in tables 4 to 9.
Table 4 road condition correction coefficient of oil consumption
| | Class | 1 | Class | 2 | Class | 3 | Class | 4 road | 5-type road | 6-type road |
| x≤7t | 1.00 | 1.01 | 1.05 | 1.08 | 1.11 | 1.15 | ||||
| 7t＜x≤14t | 1.00 | 1.02 | 1.05 | 1.09 | 1.13 | 1.17 | ||||
| 14t＜x≤21t | 1.00 | 1.02 | 1.06 | 1.10 | 1.14 | 1.18 | ||||
| 21t＜x≤28t | 1.00 | 1.02 | 1.07 | 1.11 | 1.15 | 1.20 | ||||
| 28t＜x≤35t | 1.00 | 1.03 | 1.08 | 1.12 | 1.17 | 1.22 | ||||
| 35t＜x | 1.00 | 1.03 | 1.08 | 1.12 | 1.18 | 1.23 |
。
TABLE 5 temperature correction factor for oil consumption (temperature:. degree. C.)
| Vehicle model | ＞28 | 28～5 | 5～-5 | -5～-15 | -15～-25 | ＜-25 |
| x≤7t | 1.01 | 1.00 | 1.02 | 1.07 | 1.12 | 1.17 |
| 7t＜x≤14t | 1.01 | 1.00 | 1.03 | 1.08 | 1.12 | 1.18 |
| 14t＜x≤21t | 1.01 | 1.00 | 1.03 | 1.09 | 1.13 | 1.19 |
| 21t＜x≤28t | 1.02 | 1.00 | 1.04 | 1.09 | 1.14 | 1.19 |
| 28t＜x≤35t | 1.02 | 1.00 | 1.04 | 1.09 | 1.14 | 1.20 |
| 35t＜x | 1.03 | 1.00 | 1.04 | 1.10 | 1.16 | 1.21 |
。
TABLE 6 altitude correction factor for oil consumption (height: m)
| Vehicle model | ＜500 | 500-1500 | 1500-2500 | 2500-3500 | ＞3500 |
| x≤7t | 1.00 | 0.99 | 0.98 | 0.97 | 0.95 |
| 7t＜x≤14t | 1.00 | 0.99 | 0.98 | 0.96 | 0.95 |
| 14t＜x≤21t | 1.00 | 0.99 | 0.97 | 0.96 | 0.94 |
| 21t＜x≤28t | 1.00 | 0.99 | 0.97 | 0.96 | 0.94 |
| 28t＜x≤35t | 1.00 | 0.99 | 0.97 | 0.95 | 0.94 |
| 35t＜x | 1.00 | 0.98 | 0.97 | 0.95 | 0.93 |
。
TABLE 7 vehicle Performance correction factor (Mileage: km)
TABLE 8 vehicle Condition correction factor (duration: year)
| Vehicle model | <3 |
3＜x＜5 | 5＜x＜8 | 8＜x＜10 | ＞10 |
| x≤7t | 1.00 | 1.02 | 1.05 | 1.08 | 1.12 |
| 7t＜x≤14t | 1.00 | 1.02 | 1.06 | 1.09 | 1.12 |
| 14t＜x≤21t | 1.00 | 1.03 | 1.06 | 1.09 | 1.13 |
| 21t＜x≤28t | 1.00 | 1.03 | 1.07 | 1.10 | 1.14 |
| 28t＜x≤35t | 1.00 | 1.04 | 1.07 | 1.10 | 1.14 |
| 35t＜x | 1.00 | 1.04 | 1.08 | 1.11 | 1.15 |
。
TABLE 9 Driving Condition correction factor (duration: year)
And (3) calculating the fuel consumption quota: in the calculation of the quota of oil consumption based on the freight note, a certain oil transportation fleet is selected for testing, the fleet has 13 oil tank trucks in total, the transportation line is basically fixed, and long distance and short distance exist. Previously, the fuel consumption rating of the fleet for each route was determined by the most conventional method, and the fuel consumption was determined according to the weight of the transported fuel, i.e. each ton of fuel corresponds to a specified fuel consumption, which is basically an empirical value, and the unit fuel consumption rating of the route was obtained by multiplying the ton of transported fuel by the fuel consumption per ton.
Table 10 shows the waybill practice of the fleet within a certain day of summer (temperature > 28℃.) of the jurisdiction.
TABLE 10 fleet one-day waybill practices
As shown in table 10, the actual waybill information includes a heavy-load mileage, an empty-load mileage, and a waybill fuel consumption quota, in a normal case, for a fixed line, the heavy-load mileage is equal to the empty-load mileage, the waybill fuel consumption quota is the total fuel consumption of the vehicle in the whole transportation process of the line, the waybill fuel consumption quota in the table is calculated according to the waybill fuel consumption quota, and the actual fuel consumption per hundred kilometer is obtained by querying data in a vehicle information table.
The basic information table of the vehicle is inquired according to the waybill number to acquire data such as information of a driver of the vehicle, the accumulated driving mileage of the vehicle, the age of an engine and the like, and for a transport fleet, the vehicle and the driver of each line are fixed, so that other information related to the waybill can be inquired through the waybill number, as shown in a table 11.
TABLE 11 waybill related information sheet
。
The driving condition correction coefficient table, the vehicle performance correction coefficient table and the vehicle working condition correction coefficient table are inquired according to the information of the driver and the vehicle in the table 11, and the corresponding fuel consumption correction coefficients are shown in the tables 12 and 13. Wherein, the temperature>The temperature correction coefficients were all the same at 28 ℃ and K is shown in Table 5t1.00, altitude within fleet transportation range<500m, K is shown in Table 6h1.00, except that the route with waybill number 01 and 02 is composed of a class 1 road and a class 2 road, all other routes of waybill belong to the class 1 road, and K is known from Table 4t1＝1.00，Kt2＝1.02。
TABLE 12 correction coefficient of fuel consumption (no load) for waybill 1
TABLE 13 manifest Fuel consumption (heavy load) correction factor TABLE 2
Tables 12 and 13 determine the correction of the influencing factors of the fuel consumption of each waybill according to the condition of each waybillThe coefficient, here, choose number 01 fortune bill to calculate demonstration, number 01 fortune bill standard heavy load oil consumption Z per kilometer0z46.12, standard no-load fuel consumption Z per kilometer0kAt 38.53, the model fuel consumption rating was calculated as follows.
Since the traffic routes are composed of class 1 roads and class 2 roads, the two classes of roads are calculated respectively.
Waybill heavy load oil consumption per kilometer (class 1 road):
Zsz1＝kτ1*kt*kh*ks*ky*kn*Z0z1＝1.04*1.07*1.07*1.00*1.00*1.00*46.12＝54.91
waybill no-load oil consumption per kilometer (type 1 road):
Zsk1＝kτ1*kt*kh*ks*ky*kn*Z0k1＝1.03*1.06*1.06*1.00*1.00*1.00*38.53＝44.59
waybill heavy load oil consumption per kilometer (type 2 roads):
Zsz2＝kτ2*kt*kh*ks*ky*kn*Z0z2＝1.04*1.07*1.07*1.03*1.00*1.00*46.12＝56.56
waybill no-load oil consumption per kilometer (2 types of roads):
Zsk2＝kτ2*kt*kh*ks*ky*kn*Z0k2＝1.03*1.06*1.06*1.02*1.00*1.00*38.53＝45.48
total oil consumption of the freight bill:
Qs＝Zsz1*Lsz1+Zsk1*Lsk1+Zsk2*Lsk2＝(54.91+44.59)*89+(56.56+45.48)*24＝11304.46
hundred kilometers fuel consumption rating:
the method is used for sequentially calculating the hundred kilometer fuel consumption quota of other freight notes.
Table 14 shows the fuel consumption per hundred kilometers for the unit of transport, the actual fuel consumption per hundred kilometers, and the fuel consumption per hundred kilometers calculated by the model.
TABLE 14 hundred kilometers fuel consumption comparison
The curve expression form of the tabular data is shown in fig. 2, the oil consumption of each hundred kilometers is almost a straight line, the result obtained according to the traditional oil consumption quota formulating method of the motorcade is the same, the difference between the oil consumption of each hundred kilometers and the actual oil consumption of each hundred kilometers is larger, the traditional method for formulating the oil consumption quota only by experience is very unscientific, the oil consumption of each hundred kilometers calculated by the oil consumption quota comprehensive model is closer to the actual oil consumption, and the oil consumption comprehensive model is based on the historical data of a large number of waybills, the conventional factors influencing the oil consumption are considered on the basis, the root cause is researched, the accurate and reliable correction factor and the reference oil consumption are actually obtained by establishing a model algorithm through large-data operation, so that the predicted value is continuously close to the actual oil consumption, and the method.
Claims (6)
1. A petroleum transportation vehicle oil consumption prediction method based on Hadoop is characterized by comprising the following steps: the method comprises the steps that firstly, data are collected, source data for predicting oil consumption comprise road condition data, temperature data, altitude data, vehicle performance data and driving condition data, the road condition data, the temperature data and the altitude data are obtained through an external system in an access mode, the vehicle performance data and the driving condition data are obtained through a vehicle-mounted OBD terminal and a sensor terminal, and the data collection further comprises the real-time obtaining of vehicle GPS position data;
step two, storing data, namely setting temperature, altitude and driving conditions as static data and storing the static data through a relational database; storing a large amount of real-time GPS position, road condition and vehicle performance data through a distributed database of HBase based on Hadoop;
calculating a correction coefficient of the fuel consumption influence factor, wherein the calculation comprises vehicle type division, influence factor reference value determination and standard fuel consumption quota determination; calculating an influence factor correction coefficient;
step four, forecasting and calculating the oil consumption quota, wherein the oil consumption quota calculation model of the petroleum transportation vehicle is as follows:
Qs＝Zsz*Lsz+Zsk*Lsk
Zsz＝kw*kh*ky*kj*kf*ki*Z0z
Zsk＝kw*kh*ky*kj*kf*ki*Z0k
wherein Q iss-real-time waybill estimated fuel consumption;
Zsz-real-time waybill oil consumption per hundred kilometers;
Zsk-real-time waybill no-load hundred kilometers fuel consumption;
Lsz-real-time waybill heavy haul mileage;
Lsk-real-time waybill empty mileage;
Z0z-standard heavy load fuel consumption per kilometer;
Z0k-standard no-load fuel consumption per kilometre;
ki-road condition correction factor;
kw-a temperature correction factor;
kh-an altitude correction factor;
ky-a vehicle performance correction factor;
kj-a vehicle condition correction factor;
kf-a driving situation correction factor;
Qbhistory of fuel consumption [ Q ] of the vehicleb＝Qbz(heavy oil consumption) + Qbk(no-load oil consumption)]；
QtHistorical fuel consumption [ Q ] of the same-weight vehiclet＝Qtz(heavy oil consumption) + Qtk(no-load oil consumption)]；
Lb-historical driving range (L) of the vehicleb＝Lbz+Lbk)；
LtHistorical driving range (L) of the same-weight vehiclet＝Ltz+Ltk)；
n0-a vehicle historical movement number;
m0-historical number of vehicles of the same weight;
2. The method for predicting oil consumption of a petroleum transportation vehicle based on Hadoop as claimed in claim 1, wherein the second step of collecting sample data from the HBase distributed database is performed by Hive SQL, and the Sqoop tool is used for collecting sample data from the relational database.
3. The method for predicting the oil consumption of the petroleum transportation vehicle based on the Hadoop as claimed in claim 1, wherein the formula for calculating the correction coefficient of the influence factor in the third step is as follows:
……
wherein the content of the first and second substances,
Q1n、...Q(i-1)n、Qin: representing the historical freight note oil consumption which can be inquired under different range values of an influence factor;
L1n、...L(i-1)n、Lin: the inquired driving mileage corresponding to the historical waybill oil consumption is expressed and used for calculating the hundred kilometers of oil consumption;
Q′1、...Q′i-1、Q′i: the hundred kilometers average fuel consumption at different range values representing one influence factor;
k1n、k2n、k3n、k4n、k5n、k6n: the correction coefficient represents other influence factors of n types of roads;
k11、k12、...、k1(i-1)、k1i: and the calculated correction coefficient values of the influencing factor under different range values are represented, wherein a certain correction coefficient is defined as a reference value and takes a value of 1.
4. The method for predicting the oil consumption of the petroleum transportation vehicle based on the Hadoop as claimed in claim 1, wherein the vehicle type in the third step is divided into x less than or equal to 7t, 7t < x less than or equal to 14t, 14t < x less than or equal to 21t, 21t < x less than or equal to 28t, 28t < x less than or equal to 35t, and 35t < x, wherein x is the vehicle type servicing weight.
5. The method for predicting the oil consumption of the petroleum transportation vehicle based on the Hadoop as claimed in claim 1, wherein the reference values of the influence factors in the third step are determined as follows:
selecting a class 1 road as a reference value 1.00 by the road condition correction factor;
the average temperature of the month is 28-5 ℃ as a reference value 1.00 by the temperature correction factor;
selecting altitude height less than 500m as a reference value 1.00 by the altitude correction factor;
selecting the number of miles traveled by the vehicle as 0-10 km as a reference value 1.00 by the vehicle performance correction factor;
the driving condition correction factor selects the driving age of the driver more than 10 years as a reference value of 1.00;
the vehicle working condition correction factor selects the engine service life less than 3 years as a reference value 1.00.
6. The method for predicting oil consumption of a petroleum transportation vehicle based on Hadoop as claimed in claim 5, wherein the standard quota value of oil consumption is determined according to the reference value of the influence factor.
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