KR101932695B1 - Fuel consumption estimation system based on spatial big data analysis - Google Patents

Abstract

According to an aspect of the technical idea of the present invention, a preprocessing unit for refining operation data obtained from a vehicle, road attribute information corresponding to a vehicle's driving route based on the refined operation data and map data is obtained A second processing unit for generating statistical data on the basis of the traveling data in which the road attribute information is reflected, and at least one of the traveling data in which the road property information is reflected and the generated statistical data And a fuel consumption estimating section for estimating a fuel consumption amount of the vehicle based on the fuel consumption amount estimated by the fuel consumption amount estimating section.

Description

FIELD OF THE INVENTION [0001] The present invention relates to a fuel consumption estimation system based on spatial data analysis,

The technical idea of the present invention relates to a fuel consumption estimation system, and more particularly, to a fuel consumption estimation system based on spatial big data analysis.

Environmental pollution, and global warming, the reduction of fossil fuels and the emission reduction are emerging as problems to be solved globally. As a means of solving this problem, eco-driving technology has been attracting attention in order to reduce energy consumption by improving driver's driving habits, and studies for estimating the fuel consumption amount of eco-driving related to eco-driving have been actively conducted. However, the conventional fuel consumption estimation techniques have the following problems when applied to a commercial vehicle.

First, there is a problem that estimation accuracy is low when the existing techniques are applied to commercial vehicles. Existing techniques are based on test results that are repeatedly driving a certain section using a certain number of vehicles, or a research model for a specific environment such as a California state or a large city in the United States. do.

Second, existing techniques mainly use fuel speed information to estimate fuel consumption. However, in order to obtain the vehicle speed information, the equipments mounted on the vehicle cause a lot of outlier data due to failure or malfunction due to the characteristics of the equipment, loss in transmission of sensing data through wireless communication, Errors are generated.

Third, the existing techniques use the average speed - oriented variable, which does not reflect the various operating patterns of the driver during actual driving. In addition, existing techniques have difficulties in reflecting information such as road gradients or road types of the actual environment that affect fuel consumption.

On the other hand, Korean Patent Registration No. 10-1526431 discloses a model for estimating fuel consumption for an arbitrary vehicle by receiving information related to various operations from a plurality of actual traveling vehicles. However, the Korean Patent Registration No. 10-1526431 does not reflect spatial information such as road conditions of a plurality of vehicles, and therefore, there is a problem that accurate fuel consumption can not be estimated.

Korean Patent No. 10-1526431

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and it is an object of the present invention to provide a fuel supply control system and a fuel control method thereof, which can minimize errors while using actual vehicle driving data, Estimation system.

The technical problem to be solved by the fuel consumption estimation system according to the technical idea of the present invention is not limited to the above-mentioned problems, and other problems not mentioned can be clearly understood by those skilled in the art from the following description.

According to an aspect of the technical idea of the present invention, there is provided an information processing apparatus comprising: a preprocessing unit refining operation data obtained from a vehicle; A first processing unit for acquiring road attribute information corresponding to the travel route of the vehicle based on the refined travel data and the map data and reflecting the road attribute information on the refined travel data; A second processing unit for generating statistical data on the basis of the driving data reflecting the road attribute information; And a fuel consumption estimation unit for estimating a fuel consumption amount of the vehicle based on at least one of the running data and the statistical data in which the road attribute information is reflected.

According to the exemplary embodiment, the first processing unit may be configured to match the GPS coordinates indicating the vehicle's travel route with the map indicated by the map data in the refined travel data, And a road attribute information reflecting unit that obtains the road attribute information and reflects the road attribute information on the refined operation data.

According to an exemplary embodiment, the road attribute information reflecting unit may select a matching link for the GPS coordinates based on the position of the GPS coordinates on the map, and transmit the ID of the selected matching link to the link ID, and obtain the road attribute information from the map data based on the allocated link ID.

According to an exemplary embodiment, the road attribute information reflecting unit converts the GPS coordinates into a spatial index corresponding to the map data, and calculates m (where m is a natural number) GPS coordinates Selecting candidate links adjacent to the m-th GPS coordinate on the map using a spatial index corresponding to the m-th GPS coordinate and a distance weight for each of the candidate links based on the distance between the m-th GPS coordinate and the candidate links And assigning the ID of the matching link to the link ID of the m-th GPS coordinate, and based on the assigned link ID, the map is calculated based on the calculated distance weight, The road attribute information for the m-th GPS coordinates can be obtained from the data.

According to an exemplary embodiment, the road attribute information reflecting unit may further include a history weight for each of the candidate links based on the number of times that the candidate links are preliminarily assigned as link IDs to GPS coordinates other than the m-th GPS coordinates And may select the matching link among the candidate links based on the calculated history weight and the calculated distance weight.

According to an exemplary embodiment, the road attribute information reflector further calculates a speed weight of each of the candidate links through comparison of the speed of the vehicle at the m-th GPS coordinates and the road speed limit at the candidate links, The matching link may be selected from among the candidate links based on the calculated speed weight and the calculated distance weight.

According to an exemplary embodiment, the road attribute information reflecting unit may calculate the distance between the (m + 1) -th GPS coordinate and the m-th GPS coordinate, determine whether the calculated distance meets a preset reference, If the calculated distance does not meet the preset reference, assigns the ID of the link allocated for the m-th GPS coordinate to the link ID of the (m + 1) -th GPS coordinate, and, based on the allocated link ID, And obtain road attribute information for the (m + 1) -th GPS coordinates from the data.

According to an exemplary embodiment, the first processing unit acquires the tilt information corresponding to the travel route of the vehicle based on the refined travel data and the altitude data, reflects the tilt information to the refined travel data , The second processing unit can generate the statistical data on the basis of the running data reflecting the inclination information and the road attribute information.

According to an exemplary embodiment, the elevation data may include at least one of DEM (Digital Elevation Model) data, GPS elevation data, and road gradient data.

According to an exemplary embodiment, the first processing unit is configured to calculate, based on the GPS coordinates indicating the travel path of the vehicle in the refined operating data and the altitude data corresponding to each of the GPS coordinates in the altitude data, And a tilt information reflecting unit for obtaining the tilt information from the tilt information and reflecting the tilt information on the refined operation data.

According to an exemplary embodiment, the tilt information reflecting unit may calculate a distance between n (n is a natural number) GPS coordinates and n + 1 th GPS coordinates, and if the calculated distance satisfies a preset reference Based on the calculated distance, the altitude data corresponding to the n-th GPS coordinates, and the altitude data corresponding to the (n + 1) -th GPS coordinates, if the calculated distance satisfies the preset reference The gradient of the (n + 1) -th GPS coordinates can be calculated.

According to an exemplary embodiment, the tilt information reflecting unit may calculate a distance between n (n is a natural number) GPS coordinates and n + 1 th GPS coordinates, and if the calculated distance satisfies a preset reference And if the calculated distance does not meet the preset reference, the gradient of the (n + 1) -th GPS coordinates may be set to zero.

According to an exemplary embodiment, the second processing unit may generate the statistical data by statistically analyzing the driving data on which the road attribute information is reflected.

According to an exemplary embodiment, the driving data reflecting the road attribute information may include at least one of a driving distance, a driving time, a data acquisition period, a data acquisition date and time, a speed, an engine speed per minute, a break signal, a position, an azimuth, A record of at least one of the following fields: Absolute Pressure, Mass Air Flow (MAF), fuel injection amount, road name, road type, road type, lane number, road width, road speed limit, The statistical data includes at least one of average speed, average engine revolutions per minute (RPM), average stopping time, stopping count, speed standard deviation, RPM standard deviation, speed increasing standard deviation, speed reducing standard deviation, speed and RPM correlation coefficient, And speed of GPS conversion, speed of speed, speed of dangerous overspeed, number of rapid acceleration, number of sudden deceleration, number of sudden start, number of idle stop, number of idle, rate per speed segment, ratio of RPM interval, fuel consumption, And may include a record for the at least one field of the fuel, carbon dioxide emissions, and the driving mode field of the predetermined time unit.

According to an exemplary embodiment, the fuel consumption estimating unit estimates a fuel consumption amount by using a supervised learning analysis technique that uses at least one of fields of the driving data and the statistical data reflecting the road attribute information as a variable, And a fuel consumption amount of the vehicle can be estimated by applying a record of at least one of fields of the driving data and the statistical data reflecting the road attribute information to the generated fuel consumption estimation model.

According to the embodiments of the present invention, the error can be minimized while using the actual driving vehicle data, and the fuel consumption of the vehicle can be accurately estimated by reflecting various driving pattern information and spatial information.

Although not explicitly mentioned above, the potential effects expected by the technical idea of the present invention will be apparent to those skilled in the art from the following description.

BRIEF DESCRIPTION OF THE DRAWINGS A brief description of each drawing is provided to more fully understand the drawings recited herein.
FIG. 1 is a block diagram conceptually illustrating a configuration of a fuel consumption estimation system according to an embodiment of the present invention. Referring to FIG.
FIG. 2 is a flow chart for explaining a fuel consumption amount estimation process according to an embodiment of the present invention.
3 is a diagram showing an example of step S210 of FIG.
4 is a diagram showing an example of step S230 of FIG.
FIG. 5 is a view showing an example of step S231 of FIG. 4, and FIG. 6 is a view for explaining a process of calculating an inclination.
Fig. 7 is a diagram showing an example of step S233 in Fig. 4, and Fig. 8 is a diagram for explaining a road attribute and a map matching result.
FIG. 9 is a view showing an example of step S250 of FIG. 2, and FIGS. 10 and 11 are views for explaining data related to statistical analysis.
12 is a diagram showing an example of step S270 of FIG.
FIG. 13 is a diagram schematically illustrating an environment in which a fuel consumption estimation system according to an embodiment of the present invention is used.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. However, it should be understood that the technical idea of the present invention is not limited to the specific embodiments but includes all changes, equivalents, and alternatives included in the technical idea of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS [0027] In the following description of the present invention, a detailed description of known technologies will be omitted when it is determined that the technical idea of the present invention may be unnecessarily obscured. In addition, numerals (e.g., first, second, etc.) used in the description of the present invention are merely an identifier for distinguishing one component from another.

Also, in this specification, when an element is referred to as being " connected " or " connected " with another element, the element may be directly connected or directly connected to the other element, It should be understood that, unless an opposite description is present, it may be connected or connected via another element in the middle.

The terms "to", "to", "to", "to", and "module" in the present specification mean units for processing at least one function or operation, A micro processor, a central processing unit (CPU), a graphics processing unit (GPU), an Accelerate Processor Unit (APU), a digital signal processor (DSP), an application specific integrated circuit (ASIC) (Field Programmable Gate Array), or the like, or a combination of hardware and software.

It is to be clarified that the division of constituent parts in this specification is merely a division by each main function of each constituent part. That is, two or more constituent parts to be described below may be combined into one constituent part, or one constituent part may be divided into two or more functions according to functions that are more subdivided. In addition, each of the constituent units described below may additionally perform some or all of the functions of other constituent units in addition to the main functions of the constituent units themselves, and that some of the main functions, And may be carried out in a dedicated manner.

Hereinafter, exemplary embodiments of the present invention will be described in detail.

FIG. 1 is a block diagram conceptually illustrating a configuration of a fuel consumption estimation system according to an embodiment of the present invention. Referring to FIG.

Referring to FIG. 1, the fuel consumption estimation system 100 includes a preprocessing unit 110, a first processing unit 120, a second processing unit 130, a fuel consumption amount estimating unit 140, a user interface unit 150, (Not shown).

The preprocessing unit 110 prepares data (e.g., information on the speed, acceleration, RPM, and GPS coordinates of the vehicle) related to the travel obtained from a plurality of vehicles (hereinafter referred to as travel data) refine). The driving data may be data acquired by a DTG terminal, an OBD (On-Board Diagnostic) -II terminal mounted on each of the plurality of vehicles, and the like. However, the present invention is not limited thereto, and the driving data may be data obtained from various devices for sensing information related to the moving path of the vehicles and the like. The driving data may be directly transmitted from the plurality of vehicles to the fuel consumption estimation system 100, but is not limited thereto.

The preprocessing unit 110 performs a format conversion process, a data segmentation process, and the like on the advanced data, map data, etc. input from an external device (not shown) or suitable for data processing and analysis in the first processing unit 120 Can be performed.

The first processing unit 120 can acquire information (hereinafter referred to as gradient information) about an inclination corresponding to the travel path of the vehicle based on the operation data and the altitude data refined by the preprocessing unit 110. [

The first processing unit 120 acquires information (hereinafter, referred to as road attribute information) about the road attribute corresponding to the vehicle travel route based on the travel data and the map data refined by the preprocessing unit 110 . For example, the road attribute information may include information on the road name, information on the type of road (highway, national road, etc.), information on the number of lanes, road width, information on the road speed limit, .

The first processing unit 120 may reflect the inclination information and the road attribute information in the refined operation data. For example, the first processing unit 120 may generate at least one field and a record for each of the tilt information and the road attribute information, and may add the generated field and the record to the refined operation data .

According to an embodiment, the first processing unit 120 may include an inclination information reflecting unit 121 and a road attribute information reflecting unit 123.

The inclination information reflecting unit 121 reflects the GPS coordinates representing the driving route of the vehicle in the refined operation data and the gradient information in the GPS coordinates based on the altitude related values corresponding to each of the GPS coordinates in the altitude data And reflect the inclination information to the refined operation data.

The road attribute information reflecting unit 123 matches the GPS coordinates indicating the vehicle's travel route with the map indicated by the map data in the refined operation data to obtain the road attribute information for the GPS coordinates from the map data, And the obtained road attribute information can be reflected in the refined operation data.

The second processing unit 130 generates statistical data according to a predetermined method on the basis of the running data processed by the first processing unit 120, for example, the tilt information and the running data reflecting the road property information .

The fuel consumption estimating unit 140 estimates the fuel consumption amount of an arbitrary vehicle based on at least one of the running data processed by the first processing unit 120 and the statistical data generated by the second processing unit 130 .

The user interface unit 150 may provide the estimation result of the fuel consumption estimation unit 140 to the user. According to an embodiment, the user interface unit 150 may include a visualization processing unit.

The visualization processing unit visualizes the driving data, the statistical data, the result data corresponding to the estimation result of the fuel consumption amount estimating unit 140, and allows the user such as the driver or the transportation company to directly recognize the data.

The database 160 may store data processed and / or generated in each configuration of the fuel consumption estimation system 100, data input from an external device, and the like.

According to an embodiment, the fuel consumption estimation system 100 may be implemented as a system based on a map data reduction technique using a Hadoop echo system (HDFS, HBase, etc.) and / or a big data processing technology such as apache SPARK .

Hereinafter, the overall process of estimating the fuel consumption amount by the fuel consumption estimation system 100 will be described with reference to FIG.

FIG. 2 is a flow chart for explaining a fuel consumption amount estimation process according to an embodiment of the present invention.

Referring to FIG. 2, in step S210, the fuel consumption estimation system 100 may perform a travel data preprocessing that removes an abnormal value included in the travel data acquired from the vehicle. Accordingly, the amount of data to be processed in the fuel consumption estimation system 100 can be reduced, and the accuracy of the estimated fuel consumption can be increased. Although not shown in FIG. 2, the fuel consumption estimation system 100 can perform predetermined preprocessing such as format conversion on altitude data and map data.

In step S230, the fuel consumption estimation system 100 may acquire the gradient information and the road attribute information based on the running data, the altitude data, and the map data, and reflect the obtained gradient data on the running data.

In step S250, the fuel consumption estimation system 100 may generate statistical data based on the slope information and the driving data on which the road attribute information is reflected.

In step S270, the fuel consumption estimation system 100 can estimate the fuel consumption based on at least one of the running data and the statistical data.

Hereinafter, the function and role of each component of the fuel consumption estimation system 100 according to an embodiment of the present invention, the data processing process, and the like will be described in detail with reference to FIG. 3 to FIG.

The functions and roles of the preprocessing unit 110 (Figs. 1 to 3)

1 and 2, the preprocessing unit 110 refines data on various operations, for example, DTG data, OBD-II data, and the like included in the driving data received from a plurality of arbitrary vehicles, can do. The preprocessing unit 110 can perform processing such as format conversion, data division processing, and the like so as to be suitable for the processing and analysis of the fuel consumption estimation system 110 for the altitude data, the map data, and the like.

3, the preprocessing unit 110 can detect an abnormal value in the travel data (S211), remove or correct the record detected as an abnormal value (S213), and refine the travel data.

For example, the preprocessing unit 110 may detect an outlier by determining whether a record of at least one data field included in the driving data meets a predetermined criterion (S2111) Records can be removed or corrected to meet the above criteria.

Alternatively, the preprocessing unit 110 can detect an abnormal value by comparing the successive records of at least one field with respect to the running data (S2113), and remove the detected abnormal value.

Alternatively, the preprocessing unit 110 can detect an abnormal value by comparing the records of the fields correlated with each other with respect to the running data (S2115), and can eliminate the detected abnormal value.

Alternatively, the preprocessing unit 110 can statistically analyze records of at least one field with respect to the running data to detect, as an outliers, records indicating an abnormal driving pattern (S2117), and remove records that are detected as outliers .

Since DTG data or the like is vehicle sensing information, a large amount of errors, that is, an anomaly, may be generated during the generation and / or transmission. Therefore, the preprocessing unit 110 performs an operation of filtering and purifying the operation data received from the vehicle, thereby improving the accuracy in the data processing and analysis steps described later.

The preprocessing unit 110 can refine the traveling data by the six types of methods as illustrated in Table 1 below. In connection with the refinement of the traveling data of the preprocessing unit 110 illustrated in Table 1 below, Korean Patent No. 10-1601031, 10-1601034, registered on February 2, and US Patent No. 9,600,541, filed on March 21, 2017, are incorporated herein by reference.

Way Contents Possible cause of error Out of range Remove information that exceeds the default range of the machine Sensor error Meaningless value Data is within the default range, but unimportant values are removed from statistics such as maintenance and idle vehicles Sensor error, idling, no running, short running (no statistical validity) GPS distance conversion Detection and elimination of GPS errors by converting distance between GPS coordinates into mileage GPS sensor error Filter by comparison Compares and filters the two field values included in the running data
- Comparison of speed information calculated from vehicle speed information and GPS coordinates
- Acceleration vs maximum speed
- Average speed vs. GPS distance Sensor error, idling, statistics Ineffective information Statistical technique Error detection using Pearson correlation, standard deviation, etc. During maintenance, sensor error, idling, short running (no statistical validity) Operating pattern Fault detection using driver's driving pattern Sensor error

The functions and roles of the first processing unit 120 (Figs. 1, 2 and 4 to 8)

FIG. 4 is a diagram illustrating an example of step S230 of FIG. 2, illustrating data processing performed by the first processing unit 120. FIG. Step S231 of FIG. 4 may be performed in the inclination information reflecting unit 121 of the first processing unit 120 and step S233 of FIG. 3 may be performed in the road attribute information reflecting unit 123 of the first processing unit 120 .

Referring to FIG. 5, which shows step S231 of FIG. 4 and FIG. 4 in more detail, the tilt information reflecting unit 121 obtains the tilt of the vehicle from the navigation data refined by the preprocessing unit 110, (N is a natural number) GPS coordinates and the (n + 1) th GPS coordinates (S2311).

The inclination information reflecting unit 121 can determine whether the calculated distance meets a preset reference (S2313). For example, the criterion may be set to an arbitrary value by a user to determine whether or not the inclination calculation is unnecessary according to a difference in distance between the n-th GPS coordinate and the (n + 1) -th GPS coordinate.

If the calculated distance meets a preset reference, the gradient information reflecting unit 121 may calculate the gradient based on the calculated distance, the altitude data corresponding to the n-th GPS coordinates, and the altitude data corresponding to the (n + 1) the gradient of the (n + 1) -th GPS coordinates can be calculated (S2315).

On the other hand, if the calculated distance does not meet the preset reference, the tilt information reflecting unit 121 can process the tilt of the (n + 1) th GPS coordinates as 0 (S2317).

The inclination information reflecting unit 121 may reflect the calculated inclination to the operation data refined by the preprocessing unit 110 as inclination information (S2319).

Accordingly, the driving data may include information as to whether the associated vehicle has traveled on a road having a certain degree of inclination.

Road gradients have a great impact on fuel consumption estimates. A vehicle running on a slope consumes a large amount of fuel compared to a vehicle running on a flat ground, and a vehicle running downhill consumes less fuel than a vehicle running on a flat ground.

However, since the driving data obtained by the vehicle does not include the information about the road gradient, the accuracy of the estimation of the fuel consumption of the vehicle using the vehicle driving data alone is inevitably lowered.

However, according to the technical idea of the present invention, since the inclination of the actual road is reflected in the driving data, it is possible to more accurately estimate the fuel consumption of the vehicle.

The altitude data used in calculating the slope of the slope information reflecting unit 121 may be data including various types of altitude related information.

For example, the elevation data may be Digital Elevation Model (DEM) data. It is obvious to those skilled in the art that the slope information reflecting unit 121 can be used as the digital elevation model data itself because it is the data representing the shape of the terrain by storing the altitude value of the terrain as numerical values, . The DEM data may be stored in the database 160 as a Raster image in GeoTiff format. As another example, the altitude data may be GPS altitude data. However, the present invention is not limited to this, and the altitude data may include other types of data including information related to road gradients such as road gradient data.

6 (a) is a diagram showing the converted 30-m-interval DEM data on a contour map. The method of calculating the degree of the GPS locus using the DEM data may be various, and detailed algorithms for calculating the slope can not be limited because the scope of the technical idea of the present invention can not be limited. 6 (b) shows an example in which the calculated slope results are displayed on a contour map. It can be seen that the uphill section is indicated by + inclination, and the downward section by inclination.

Table 2 below is a table for explaining an example in which the second processing unit 130, which will be described later, uses the inclination information in estimating the fuel consumption amount. Referring to Table 2, the slope information may be reflected in the operation data refined by the preprocessing unit 110. The operation data reflecting the slope information may include vehicle number information, date information, time information, mileage information, And may include downhill information. The uphill information and / or downhill information illustrated in Table 2 below may be information on the sum of the inclination of the uphill road and / or the inclination of the downhill road for a unit time (for example, 5 minutes). The driving data in which the inclination information is reflected can be used when estimating the fuel consumption of the fuel consumption estimating unit 140 when generating the statistical data of the second processing unit 130. [

the car's number date time distance driven ascent Downhill A 140405 521 0 25.16 0 A 140405 530 4 130.53 88.57 A 140405 531 8 152.47 165.13 A 140405 540 9 36.01 82.46 A 140405 541 10 0 1.86 A 140405 550 10 1.90 0 A 140405 551 10 0 1.89 A 140405 ... ... ... ...

Referring to FIG. 7, which shows step S233 of FIG. 4 and FIG. 4 in more detail, the road attribute information reflecting unit 123 reflects on the map of the GPS coordinates indicating the travel route of the vehicle from the travel data refined by the preprocessing unit 110 The ID of the selected matching link is assigned to the link ID of the GPS coordinates, and the road attribute information corresponding to the GPS coordinates is calculated from the map data based on the assigned link ID Can be acquired and reflected in the driving data.

Specifically, the road attribute information reflecting unit 123 may first calculate the distances to the previous GPS coordinates with respect to each of the GPS coordinates (S2330), and determine whether the calculated distance satisfies a preset reference ( S2331). The criterion is for determining whether or not a new search of the matching link is required according to the difference in distance between GPS coordinates, for example, and may be set to any value by the user.

The road attribute information reflecting section 123 reflects the GPS coordinates (hereinafter, convenience of explanation is referred to as the mapped GPS coordinates m (where m is a natural number) GPS coordinates) as a map It can be converted into information corresponding to the data (S2332). The road attribute information reflecting unit 123 may convert the m-th GPS coordinates into information (hereinafter referred to as " information ") corresponding to the map data because the value indicating the arbitrary place in the map data may differ from the format of the m- , &Quot; spatial index ").

The road attribute information reflecting unit 123 uses the spatial index corresponding to the m-th GPS coordinates to calculate a candidate link adjacent to the m-th GPS coordinate (i.e., a candidate road on which the m- (S2333).

The road attribute information reflecting unit 123 may calculate at least one of the distance weight, the history weight, and the speed weight for the selected candidate links (S2334).

For example, the road attribute information reflecting unit 123 may calculate a distance weight for each of the candidate links based on the distance between the m-th GPS coordinates and the candidate links. The distance weight may be information corresponding to the distance between the m-th GPS coordinate and the candidate links. According to an embodiment, the distance weight may have the smallest value of the candidate links having the smallest distance from the m-th GPS coordinates among the candidate links, but is not limited thereto.

For example, the road attribute information reflecting unit 123 may reflect the history weights for each of the candidate links based on the number of times that the candidate links are preliminarily assigned as link IDs to GPS coordinates other than the m-th GPS coordinates Can be calculated. The history weight may be information corresponding to the number of times assigned to the link ID with respect to the other GPS coordinates. According to an embodiment, the history weight may have the smallest value of the candidate links most allocated to the link IDs of the other GPS coordinates among the candidate links, but the present invention is not limited thereto.

For example, the road attribute information reflecting unit 123 may calculate a speed weight of each of the candidate links by comparing the vehicle speed at the m-th GPS coordinates with the road speed limit at the candidate links Can be calculated. Here, the speed weight may be information on a speed difference between the limit speed of the candidate links and the vehicle speed at the m-th GPS coordinates. According to an embodiment, the velocity weight may have the smallest value among the candidate links, but the present invention is not limited thereto.

Meanwhile, the road attribute information reflecting unit 123 may calculate various weight values in addition to the distance weight, the history weight, and the speed weight, which are exemplified above, for use in selecting a matching link to be described later. Hereinafter, It is noted that the section 123 uses the distance weight, the history weight, and the speed weight as examples.

The road attribute information reflecting unit 123 may select a matching link among the candidate links based on the calculated weight (S2335). In detail, the road attribute information reflecting unit 123 reflects a matching link matching the m-th GPS coordinates among the candidate links based on any one of a distance weight, a history weight, a speed weight or a combination thereof Can be selected.

For example, the road attribute information reflecting unit 123 may select a candidate link having the smallest distance weight among the candidate links as the matching link. The candidate link closest to the m-th GPS coordinates among the candidate links can be selected as the matching link.

In another example, the road attribute information reflecting unit 123 may select a candidate link having the smallest sum of the distance weight and the history weight among the candidate links as the matching link. If any one of the candidate links is a link that is not allocated to other GPS coordinates even though it is the mth GPS coordinate and the closest neighbor link, it may be difficult to regard the candidate weight as a position on the actual travel route. It is possible to select a candidate link corresponding to the actual travel route as the matching link.

As another example, the road attribute information reflecting unit 123 may select a candidate link having the smallest sum of distance weight and speed weight among the candidate links as the matching link. It is assumed that the speed information corresponding to the m-th GPS coordinates is 110 km / h, the speed limit of the K provincial road is 80 km / h, and the speed limit of the U highway is 110 km / h , It is difficult to travel at a speed of 110 [km / h] on the K-provincial road even though the m-th GPS coordinate is closer to the K-provincial road than the U-highway. Therefore, in addition to the distance weight, The candidate link can be selected as the matching link.

The road attribute information reflecting unit 123 can assign the ID of the selected matching link to the link ID of the m-th GPS coordinates (S2336), and based on the assigned link ID, It is possible to obtain the road attribute information for the road (S2337).

On the other hand, if the calculated distance does not meet the preset reference, the road attribute information reflecting unit 123 may assign the ID of the link allocated to the previous GPS coordinates to the link ID of the corresponding GPS coordinates (S2338).

For example, if the distance between the (m + 1) -th GPS coordinate and the m-th GPS coordinate does not match the predetermined reference, the road attribute information reflecting unit 123 may determine the ID of the link allocated to the m- May be assigned to the link ID of the (m + 1) -th GPS coordinates. Thus, the road attribute information reflecting unit 123 can improve the data matching speed. Then, the road attribute information reflecting unit 123 can obtain the road attribute information for the (m + 1) th GPS coordinates from the map data based on the assigned link ID (S2337).

The road attribute information reflecting section 123 can reflect the obtained road attribute information on the running data refined by the preprocessing section 110 (S2339).

8 (a) is a diagram of road attribute information for each link. Referring to FIG. 8A, the road attribute information may include information on the type of road, the type of road, the width of the road, the number of persons, the speed limit, and the like.

8 (b) is a diagram for explaining a case where the road attribute information is reflected in the travel data refined by the preprocessing unit 110. In FIG. Accordingly, the driving data reflecting the road attribute information may include information on the vehicle number, date, time, GPS coordinates, link ID, road name, road type,

The functions and roles of the second processing unit 130 (Figs. 1, 2 and 9 to 11)

Referring to FIG. 9, the second processing unit 130 may generate statistical data by statistically analyzing operation data reflecting inclination information and / or road attribute information (S251).

For example, the second processing unit 130 may calculate an average value of records of at least one field of the running data, calculate an accumulated value of the calculated average values, perform data mining from a short-term / long- Through the same statistical calculation and analysis, it is possible to generate statistical data including information related to various driving patterns of the driver.

Regarding the generation of statistical data through the statistical analysis of the second processing unit 130, Korean Registered Patent No. 10-1601031, 10-1601034 registered on March 2, 2016, and United States Registered Registered on March 21, 2017 Exemplary examples disclosed in patent 9,600,541 are incorporated herein by reference.

The operation data includes at least one of a travel distance, a travel time, a data acquisition cycle, a data acquisition date and time, a speed, a RPM, a brake signal, a position, an azimuth, The air flow rate, the amount of fuel spray, the road name, the type of road, the type of road facility, the number of lanes, the road width, the road speed limit, the toll road field, and the road gradient.

The statistical data includes at least one of average speed, average engine revolutions per minute (RPM), average stopping time, stopping count, speed standard deviation, RPM standard deviation, speed increasing standard deviation, speed reducing standard deviation, speed and RPM correlation coefficient, The number of idle times, the number of idling times, the number of idling times, the rate of each idle speed, the rate of each RPM section, the amount of fuel consumed, the amount of fuel, the predetermined time unit A record of at least one of a fuel consumption, an amount of generated carbon dioxide, and a driving mode field.

FIG. 10 is a view for explaining a field example of operation data and statistical data, and FIG. 11 is a diagram showing an example of statistical data. The second processing unit 130 can generate the statistical data exemplified in Figs. 10 and 11 by using the driving data reflecting the road attribute information and / or the tilt information shown in Fig.

The statistical data generated by the second processing unit 130 and the analysis data generated during the statistical analysis may be stored in the database 160 and may be provided to the user through the user interface unit 150, Or may be transmitted to an external system.

The functions and roles of the fuel consumption estimating unit 140 (FIGS. 1, 2, and 12)

Referring to FIG. 12, the fuel consumption estimating unit 140 may be a data analysis technique that uses at least one of fields of running data and statistical data in which inclination information and / or road attribute information is reflected as variables, for example, supervised learning analysis method to generate a fuel consumption estimation model (S271).

For example, the fuel consumption estimation unit 140 may perform a regression analysis using at least one of the driving data and the statistical data as independent variables, and using the fuel consumption related field as a dependent variable among the fields of the driving data The fuel consumption estimation model may be generated. However, the present invention is not limited to this, and the fuel consumption amount estimating unit 140 may use various map learning analysis techniques such as a non-linear regression, a support vector machine, and a neural network A fuel consumption estimation model can be generated.

Meanwhile, the fuel consumption estimating unit 140 may use complex driving data and statistical data related to various drivers, various types of vehicles, and the like when generating the fuel consumption estimation model through the map learning analysis technique, but the present invention is not limited thereto. According to the embodiment, the fuel consumption amount estimating unit 140 may divide the driving data and the statistical data into predetermined units such as a specific driver, a specific vehicle type, and the like, and then generate a fuel consumption amount estimation model for each predetermined unit.

The fuel consumption amount estimating unit 140 may estimate the fuel consumption amount of the vehicle by a predetermined time unit using the fuel consumption amount estimating model (S273).

Specifically, the fuel consumption amount estimating unit 140 applies at least one of the field records of the running data and the statistical data of the fuel consumption amount estimation object (hereinafter, referred to as the target vehicle) to the fuel consumption amount estimation model, The fuel consumption amount of the target vehicle can be estimated in units of units, and the estimation result can be output as the result data.

Here, the record of the field applied to the fuel consumption estimation model is not limited to the record of the field directly / indirectly related to the fuel such as the fuel discharge amount of the driving data, the fuel consumption amount of the statistical data, the fuel remaining amount, The fuel consumption amount estimating unit 140 can estimate the fuel consumption amount of the target vehicle by only recording the field that is not directly or indirectly related to the fuel.

The fuel consumption amount estimating unit 140 can estimate the fuel consumption amount in various units of time, such as seconds and minutes. Results data generated by estimation of fuel consumption per second can be used for vehicle / driver economic indexes, and the result data generated by estimating fuel consumption per 10 seconds / 1 minute / The result data generated by estimating the fuel consumption per day / month may be used for the purpose of preventing illegal use of driver 's amount of fuel by transportation companies.

FIG. 13 is a diagram schematically illustrating an environment in which a fuel consumption estimation system according to an embodiment of the present invention is used. The fuel consumption estimation system 1300 shown in FIG. 13 is substantially the same as the fuel consumption estimation system 100 shown in FIG. 1, and therefore, a duplicated description will be omitted for the sake of convenience.

The fuel consumption amount estimation result data for the specific driver, the type of vehicle, and the traveling route obtained by the fuel consumption estimation system 1300 is at least one of the eco driving system 1310, the eco routing system 1330, and the fuel ratio evaluation system 1350 Can be delivered as one.

The eco-driving system 1310 can improve the driving habits of the driver based on the resultant data on the fuel consumption estimated based on the running data, the statistical data or the like, or the mining result data for the analysis of the driving pattern. The eco-driving system 1310 can be implemented in a mobile terminal of a vehicle driver, a navigation system installed in a vehicle, a transportation company management system, and the like.

The eco-routing system 1330 can contribute to the fuel economy of the driver by providing the driver with a driving route that minimizes fuel consumption based on the resultant data on the estimated fuel consumption. The eco-routing system 1330 may be embodied in a system such as a traffic safety management entity as well as a mobile terminal of a vehicle driver, a navigation system installed in a vehicle, and the like.

The fuel cost evaluation system 1330 can be implemented in a control system of a government agency, a transportation company, and the like, and can use the result data on the estimated fuel consumption to evaluate whether or not the fuel price subsidies supported by commercial vehicles are illegitimately supplied or not.

Although not shown in FIG. 13, the fuel consumption estimation result obtained by the fuel consumption estimation system 1300 may be transmitted to, for example, an environmental management system for regulation and management of CO2 emissions.

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, Various modifications and variations are possible.

100: Fuel consumption estimation system
110:
120: first processing section
121: inclination information reflecting unit
123: Road attribute information reflecting unit
130:
140: fuel consumption amount estimating unit
150:
160: Database

Claims (15)

A preprocessing unit for refining operation data obtained from the vehicle;
A first processing unit for acquiring road attribute information corresponding to the travel route of the vehicle based on the refined travel data and the map data and reflecting the road attribute information on the refined travel data;
A second processing unit for generating statistical data on the basis of the driving data reflecting the road attribute information; And
And a fuel consumption estimating unit estimating a fuel consumption amount of the vehicle based on at least one of the running data and the statistical data in which the road attribute information is reflected,
Wherein the first processing unit comprises:
Selecting a plurality of candidate links corresponding to GPS coordinates indicating a travel route of the vehicle based on the refined travel data and the map data,
A history weight related to the distance between the GPS coordinates and each of the plurality of candidate links for each of the selected plurality of candidate links, a history weight associated with the number of times assigned to GPS coordinates other than the GPS coordinates, Calculating at least one of a speed weight associated with a speed difference between a speed of the vehicle and a road speed limit of each of the plurality of candidate links,
Selecting one matching link corresponding to the GPS coordinates based on at least one of the calculated distance weight, the history weight, and the speed weight,
And obtains the road attribute information based on the selected matching link. The method according to claim 1,
Wherein the first processing unit comprises:
A road attribute information reflecting unit for calculating at least one of the distance weight, the history weight, and the speed weight for each of the selected plurality of candidate links;
And a fuel consumption estimating system. 3. The method of claim 2,
The road attribute information reflecting unit may include:
Assigning the ID of the selected matching link to the link ID of the GPS coordinates,
And obtains the road attribute information from the map data based on the allocated link ID. The method of claim 3,
The road attribute information reflecting unit may include:
Converts the GPS coordinates into a spatial index corresponding to the map data,
Selecting candidate links that are adjacent to the m-th (m is a natural number) GPS coordinate on the driving route of the vehicle on the map according to the map data using the converted spatial index,
Calculating the distance weight for each of the candidate links based on the m-th GPS coordinates and the distance between the candidate links,
Selecting the matching link among the candidate links based on the calculated distance weight,
Assigns the ID of the matching link to the link ID of the m-th GPS coordinates,
And acquires road attribute information for the m-th GPS coordinates from the map data based on the allocated link ID. 5. The method of claim 4,
The road attribute information reflecting unit may include:
Calculating the history weight for each of the candidate links based on the number of times that the candidate links are preliminarily assigned as link IDs to GPS coordinates other than the m-th GPS coordinates,
And selects the matching link among the candidate links based on the calculated history weight and the calculated distance weight. 5. The method of claim 4,
The road attribute information reflecting unit may include:
Calculating a speed weight of each of the candidate links by comparing the speed of the vehicle with the road speed limit speed at the candidate links at the m-th GPS coordinates,
And selects the matching link among the candidate links based on the calculated speed weight and the calculated distance weight. 5. The method of claim 4,
The road attribute information reflecting unit may include:
calculating a distance between the m + 1th GPS coordinate and the mth GPS coordinate,
Judges whether or not the calculated distance satisfies a preset reference,
And if the calculated distance does not match the preset reference, assigning the ID of the link allocated for the m-th GPS coordinates to the link ID of the m + 1-th GPS coordinates, And obtains road attribute information for the (m + 1) -th GPS coordinates from the map data. The method according to claim 1,
Wherein the first processing unit comprises:
Acquiring tilt information corresponding to the travel route of the vehicle based on the refined travel data and the altitude data, reflecting the tilt information on the refined travel data,
Wherein the second processing unit comprises:
And generates the statistical data on the basis of the slope information and the running data reflecting the road property information. 9. The method of claim 8,
The elevation data includes:
(DEM) data, GPS altitude data, and road gradient data. 9. The method of claim 8,
Wherein the first processing unit comprises:
Acquiring tilt information in the GPS coordinates based on GPS coordinates representing the travel path of the vehicle in the refined operating data and altitude data corresponding to each of the GPS coordinates in the altitude data, And an inclination information reflecting unit reflecting the inclination information on the data. 11. The method of claim 10,
The inclination information reflecting unit may include:
th GPS coordinate and the (n + 1) -th GPS coordinate, where n is a natural number,
Judges whether or not the calculated distance satisfies a preset reference,
If the calculated distance satisfies the preset reference, the (n + 1) -th GPS (Global Positioning System) based on the calculated distance, the altitude data corresponding to the n-th GPS coordinates and the altitude data corresponding to the And calculates an inclination in the coordinates. 11. The method of claim 10,
The inclination information reflecting unit may include:
th GPS coordinate and the (n + 1) -th GPS coordinate, where n is a natural number,
Judges whether or not the calculated distance satisfies a preset reference,
And if the calculated distance does not meet the predetermined criterion, processes the slope in the (n + 1) -th GPS coordinate to be zero. The method according to claim 1,
Wherein the second processing unit comprises:
And the statistical data is generated by statistically analyzing the running data on which the road attribute information is reflected. The method according to claim 1,
The operation data, in which the road attribute information is reflected,
(MAF), fuel injection amount, road name, and the like, as well as the number of revolutions per minute, mileage, travel time, data acquisition period, data acquisition date and time, speed, engine revolutions per minute, brake signal, position, azimuth angle, acceleration, MAP (Manifold Absolute Pressure) A record of at least one field of a road type, a type of road facility, a number of lanes, a road width, a road limiting speed, and a toll road field,
The statistical data includes,
Average speed, Mean RPM, Mean stop time, Stop count, Speed standard deviation, RPM standard deviation, Speed increase standard deviation, Speed reduction standard deviation, Speed and RPM correlation coefficient, Vehicle speed and GPS conversion speed difference The number of idle times, the number of idling cycles, the number of idling cycles, the rate of idling cycles, the ratio of RPM intervals, the fuel consumption amount, the fuel consumption amount, the fuel consumption amount per unit time, the amount of carbon dioxide generation, And a record for at least one field of the driving mode field. The method according to claim 1,
Wherein the fuel consumption estimating unit estimates,
Generating a fuel consumption estimation model through a supervised learning analysis method using at least one of fields of the driving data and the statistical data reflecting the road attribute information as variables,
Estimating a fuel consumption amount of the vehicle at a predetermined time unit by applying a record of at least one field among fields of the running data and the statistical data in which the road attribute information is reflected to the generated fuel consumption estimation model, Estimation system.

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