TWI512269B - Best-fit traffic path and path data selecting system and method thereof - Google Patents

Description

Most suitable path and path data selection system and method thereof

A selection system and method thereof, in particular, a selection system and method for the most suitable path and path data.

With the advancement of technology, more and more driving devices have been developed, such as driving recorders, driving navigation devices, etc., which can effectively provide users with a more convenient driving process.

The existing navigation device is provided for user path navigation, and the route navigation provided by the general navigation device is mostly provided by the shortest path method, and then navigated according to the shortest path.

However, the driving navigation device does not meet the needs of the user according to the shortest path method, for example, the user needs to reach the destination in the shortest time, the user needs the minimum fuel consumption, the user needs to turn to fewer paths, etc., and the above requirements are based on the existing The shortest path method is not available, and the shortest path does not have to be the shortest time to reach the destination. The shortest path may be uncomfortable, unsafe, not fuel-efficient, or encounter too many traffic lights, resulting in time to reach the destination. Too long.

In summary, it has been known in the prior art that the existing navigation device has long been provided with the shortest path to provide a problem that cannot meet the different needs of the user. Therefore, it is necessary to propose improved technical means to solve this problem.

In view of the prior art, existing navigation navigation devices provide the shortest path to provide a problem that cannot meet the different needs of users. The present invention discloses a system and method for selecting the most suitable path and path data, wherein: the most suitable path disclosed by the present invention And a path data selection system, comprising: a driving record database, a receiving module, a query module, an analysis module, and a path selection module.

The driving record database is configured to store at least one path of each path node to the remaining path nodes and a plurality of driving history data corresponding to the path; the receiving module is configured to receive the starting path node and the ending path node; the query module The method is used for querying the corresponding path and the driving history data corresponding to the path according to the starting path node and the ending path node bicycle record database; the analysis module is configured to separately analyze the driving history data corresponding to the path to analyze At least one average value of each path and a variation value corresponding to the average value; and the path selection module is configured to correspondingly display the queried path, the driving data or the indicator average value corresponding to the path, and the average value The variation value is used to provide the user with a choice of path.

The method for selecting the most suitable path and path data disclosed by the present invention includes the following steps: First, providing at least one path for storing each path node to the remaining path nodes and driving record data of multiple driving history data corresponding to the path a library; then, receiving the start path node and the end path node; and then, according to the start path node and the end path node bicycle record database, querying the corresponding path and the travel history data corresponding to the path; and then, respectively, the path and the path Corresponding driving history data is analyzed to analyze at least one average value of each path and a variation value corresponding to the average value to correspondingly generate at least one selection result; finally, the correspondingly displayed the path and the path are queried Corresponding to the selection result, to provide the user with a choice of path.

The system and method disclosed by the present invention are as above, and the difference from the prior art is that the present invention analyzes the driving history data corresponding to the path between the path nodes from the pre-established driving record database, and the driving history data includes driving time and driving. The fuel consumption and the calculated driving index, etc., to analyze at least one average value of each path and the variation value corresponding to the average value, and correspondingly generate at least one selection result, correspondingly displaying the queried path and corresponding to the path The result of the selection is to provide the user with a choice of path, thereby providing a path for different needs of the user according to different driving history data.

Through the above technical means, the present invention can achieve technical effects according to different driving history data to provide path selection.

11‧‧‧ Driving Record Database

12‧‧‧ receiving module

13‧‧‧Query Module

14‧‧‧Analysis module

15‧‧‧Path Selection Module

16‧‧‧ Data Prediction Module

21‧‧‧ Path

211‧‧‧ Path

212‧‧‧ Path

213‧‧‧ Path

22‧‧‧ Driving history data

221‧‧‧ Driving history data

222‧‧‧ Driving history data

223‧‧‧ Driving history data

A‧‧‧Path node

B‧‧‧Path node

C‧‧‧Path node

D‧‧‧Path node

E‧‧‧Path node

F‧‧‧Path node

G‧‧‧Path node

H‧‧‧Path node

I‧‧‧Path node

Step 101‧‧‧ provides at least one path and path for storing each path node to the remaining path nodes Driving record database of multiple driving history data corresponding to the path

Step 102‧‧‧ Receive start path node and end path node

Step 103‧‧‧Query the corresponding path and the driving history data corresponding to the path according to the starting path node and the ending path node bicycle record database

Step 104‧‧‧ analyzes the driving history data corresponding to the path to analyze at least one average value of each path and the variation value corresponding to the average value to generate at least one selection result correspondingly

Step 105‧‧‧ corresponds to displaying the queried path and the selection result corresponding to the path to provide a user to select a path

FIG. 1 is a block diagram showing a selection system of the most suitable path and path data of the present invention.

FIG. 2 is a flow chart showing a method for selecting the most suitable path and path data according to the present invention.

FIG. 3 is a schematic diagram showing the paths and path nodes of the most suitable path and path data selection according to the present invention.

FIG. 4A is a schematic diagram showing a travel record database of the most suitable path and path data selection according to the present invention.

FIG. 4B is a schematic diagram showing the driving history data of the most suitable path and path data selection according to the present invention.

5A to 5C are diagrams showing an analysis process for providing path and path data selection according to the present invention.

FIG. 6A is a schematic diagram showing the driving history data of the most suitable path and path data selection according to the present invention.

FIG. 6B is a schematic diagram showing the driving history data of the most suitable path and path data selection according to the present invention.

FIG. 6C is a schematic diagram showing the predicted driving history data of the most suitable path and path data selection according to the present invention.

The embodiments of the present invention will be described in detail below with reference to the drawings and embodiments, so that the application of the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented.

In the following, the selection system of the most suitable path and path data disclosed in the present invention is first described, and reference is made to "1", and "1" is a block diagram showing the most suitable path and path data selection system of the present invention. .

The most suitable path and path data selection system disclosed in the present invention comprises: a driving record database 11, a receiving module 12, an inquiry module 13, an analysis module 14, and a path selection module 15.

The driving record database 11 stores all paths of each path node to the remaining path nodes and multiple driving history data corresponding to the path, and each path node is an intersection of the actual road, and at least two intersections are connected, that is, a path, and The driving history data corresponding to the path is the driving data data actually passing the path. Each line will generate a driving history data after one route. The driving history data includes driving time, driving fuel consumption and calculated driving index...etc. This is for illustrative purposes only and is not intended to limit the scope of application of the present invention. The driving index includes comfort or safety. The vehicle index is obtained through comprehensive analysis of the vehicle speed, the acceleration before and after the driving, the braking, the engine speed, the steering wheel rotation, the driving angular velocity, the left and right acceleration of the driving, and the acceleration of the upper and lower acceleration of the vehicle. This is only an example, and is not This limits the scope of application of the invention.

For example, the paths in which the path node A to the path node I are stored in the driving record database 11 are respectively "path node A - path node B - path node C - path node F - path node I" and "path node A - The two paths of the path node D-path node G-path node H-path node I" are merely illustrative here and are not intended to limit the scope of application of the present invention.

The driving history data corresponding to the path "path node A-path node B-path node C-path node F-path node I" are respectively "300 hours in the morning at 300 o'clock" and "trailing time 305 seconds at 4 o'clock in the morning", "During the morning at 4 o'clock in the morning, 310 seconds", "March 4 o'clock driving time 305 seconds", "8 o'clock in the morning driving time 1000 seconds", "8 o'clock in the morning driving time 480 seconds", "8 o'clock in the morning driving time 490 seconds", “500 seconds driving time at 8:00 in the morning”, “500 seconds driving time at 6:00 pm”, “490 seconds driving time at 6:00 pm” and “480 seconds driving time at 6:00 pm” are for illustrative purposes only. This limits the scope of application of the invention.

The driving history data corresponding to the path "path node A-path node D-path node G-path node H-path node I" are "360 hours of driving time at 4 am" and "365 seconds of driving time at 4 am", "Training time 370 seconds at 4 o'clock in the morning", "365 hours driving time at 4 o'clock in the morning", "510 seconds driving time at 8 o'clock in the morning", "520 seconds driving time at 8 o'clock in the morning", "530 hours driving time at 8 o'clock in the morning", "5 pm driving time 530 seconds", "6 pm driving time 520 seconds" and "6 pm driving time 510 seconds" are merely illustrative and are not intended to limit the scope of application of the present invention.

Then, the receiving module 12 can receive the starting path node and the ending path node selected by the user. According to the above example, the user selects the path node A as the starting path node, and selects the path node I as the terminating path node.

Then, the query module 13 can query the corresponding path and the driving history data corresponding to the path according to the initial path node and the termination path node bicycle record database 11 received by the receiving module 12.

According to the above example, the query module 13 can query the corresponding path according to the start path node received by the receiving module 12 as the "path node A" and the end path node as the "path node I" bicycle record database 11. "Path node A-path node B-path node C-path section" Point F-path node I" and "path node A-path node D-path node G-path node H-path node I" two paths.

And querying the driving history data corresponding to the path "path node A-path node B-path node C-path node F-path node I" is respectively "4 am driving time 300 seconds" and "4 am driving time 305" "seconds", "morning time 4 o'clock driving time 310 seconds", "morning 4 o'clock driving time 305 seconds", "morning 8 o'clock driving time 1000 seconds", "morning 8 o'clock driving time 480 seconds", "morning 8 o'clock driving time 490" "seconds", "500 hours of driving time at 8 o'clock in the morning", "500 seconds of driving time at 6 o'clock in the evening", "490 seconds of driving time at 6 o'clock in the evening" and "trailing time 480 seconds at 6 o'clock in the evening".

And querying the driving history data corresponding to the path "path node A-path node D-path node G-path node H-path node I" respectively is "4 am driving time 360 seconds", "4 am driving time 365" Seconds, "Miding time 370 seconds in the morning", "365 hours driving time 4:00 seconds", "8:00 am driving time 510 seconds", "8 am driving time 520 seconds", "8 am driving time 530" "Second", "6 o'clock driving time 530 seconds", "6 o'clock driving time 520 seconds" and "6 o'clock driving time 510 seconds".

Then, the analysis module 14 can analyze the driving history data corresponding to the path to analyze at least one average value of each path and the variation value corresponding to the average value to generate at least one selection result.

According to the above example, the analysis module 14 according to the path "path node A - path node B - path node C - path node F - path node I" corresponds to the driving history data respectively, "4 am driving time 300 seconds", "morning morning 4 o'clock driving time 305 seconds", "4 o'clock morning driving time 310 seconds", "4 o'clock morning driving time 305 seconds", "8 o'clock morning driving time 1000 seconds", "8 o'clock morning driving time 480 seconds", "morning 8 o'clock driving time 490 seconds", "8 o'clock driving time 500 seconds", "6 o'clock driving time 500 seconds", "6 o'clock driving time 490 seconds" and "6 o'clock driving time 480 seconds" are listed. Histogram.

The analysis module 14 will first delete the discrete driving history data as "the driving time of 1000 in the morning is 1000 seconds". This driving history data is generally too special driving conditions, such as: car accident, road construction, etc., here is only an example. It should be noted that the scope of application of the present invention is not limited thereto, so it needs to be deleted to avoid the problem of inaccurate analysis results.

Then, the analysis module 14 can divide the driving history data into "early morning" according to the histogram. The first group of driving history data of "4 o'clock driving time 300 seconds", "4 o'clock morning driving time 305 seconds", "4 o'clock morning driving time 310 seconds" and "4 o'clock morning driving time 305 seconds", "8 o'clock in the morning" Time 480 seconds", "March time 490 seconds" and "8 hours driving time 500 seconds", "6 pm driving time 500 seconds", "6 pm driving time 490 seconds" and "6 pm driving" The second group of driving history data of time 480 seconds.

And the analysis module 14 analyzes that the average travel time of the first group of driving history data is “305 seconds” and the variation value is “5 seconds” and the corresponding first selection result is “305±5 seconds”, and The analysis module 14 analyzes that the average travel time of the second group of driving history data is "490 seconds" and the variation value is "10 seconds" and the corresponding second selection result is "490±10 seconds".

Next, the analysis module 14 according to the path "path node A - path node D - path node G - path node H - path node I" corresponding to the driving history data are "4 o'clock morning driving time 360 seconds", "4 am Driving time 365 seconds", "4 am driving time 370 seconds", "4 am driving time 365 seconds", "8 am driving time 510 seconds", "8 am driving time 520 seconds", "8 am" The corresponding histogram is listed in the 530 seconds of driving time, 530 seconds of driving time at 6 o'clock in the evening, 520 seconds of driving time at 6 o'clock in the evening, and 510 seconds of driving time at 6 o'clock in the evening.

The analysis module 14 can divide the driving history data into "360 seconds driving time at 4 am, 365 seconds driving time at 4 am", "trailing time 370 seconds at 4 am" and "4 o'clock in the morning" according to the histogram. The third group of driving history data of time 365 seconds, "510 hours of driving time at 8:00 in the morning", "520 seconds of driving time at 8:00 in the morning" and "530 seconds of driving time at 8:00 in the morning", "30 minutes driving time at 6 o'clock in the evening" The fourth group of driving history data of ", 520 seconds of driving time at 6 o'clock in the evening" and "510 seconds of driving time at 6 o'clock in the evening".

And the analysis module 14 analyzes that the average travel time of the third group of driving history data is “365 seconds” and the variation value is “5 seconds” and the corresponding third selection result is “365±5 seconds”, and The analysis module 14 analyzes that the average travel time of the fourth group of driving history data is “520 seconds” and the variation value is “10 seconds” and the corresponding fourth selection result is “520±10 seconds”.

Finally, the path selection module 15 can display the queried path and the selection result corresponding to the path to provide the user with the path selection.

According to the above example, the first selection result of the path "path node A - path node B - path node C - path node F - path node I" analyzed by the analysis module 14 is "305 ± 5 seconds" and the second selection result "490±10 seconds", and the third selection result of the path "path node A-path node D-path node G-path node H-path node I" is "365±5 seconds" and the fourth selection result It is "520 ± 10 seconds".

Then, the first selection result corresponding to the display path "Path Node A - Path Node B - Path Node C - Path Node F - Path Node I" by the path selection module 15 is "305 ± 5 seconds" and the second selection result It is "490±10 seconds", and the third selection result corresponding to the display path "path node A-path node D-path node G-path node H-path node I" is "365±5 seconds" and the fourth selection result It is "520 ± 10 seconds" to provide the user with a choice of path.

The receiving module 12 can further receive the query condition, such as time, fuel consumption, etc., and the time can be a specific time, for example, 8:00 in the morning, 6 in the evening, etc., and the time can also be a range of time, for example, : One week, one month, etc., is merely illustrative here, and is not limited to the application scope of the present invention, and the analysis module 14 can perform driving history data screening according to the query condition and then perform driving corresponding to the path. Analysis of historical data.

According to the above example, when the receiving module 12 receives the query condition as “6 pm”, the analysis module 14 will follow the path “path node A-path node B-path node C-path node F-path node I”. Take out the driving history data of "the driving time of 500 seconds at 6 o'clock in the evening", "the driving time of 490 seconds at 6 o'clock in the evening" and "the driving time of 480 seconds in the evening", and then analyze the driving history data corresponding to the route. Referring to the above description, it will not be repeated here.

And the analysis module 14 will take out "6 o'clock driving time 530 seconds" and "6 o'clock driving time 520 seconds" according to the path "path node A-path node D-path node G-path node H-path node I". And the driving history data of "the driving time of 510 seconds at 6:00 pm" is further analyzed by the driving history data corresponding to the path. For the analysis result, please refer to the above description, and no further description is made here.

In addition, when the query module 13 cannot query the corresponding path and the driving history data corresponding to the path according to the starting path node and the ending path node bicycle record database 11, the data prediction module 16 can be used according to the data prediction module 16 The existing data performs an estimation of the path corresponding to the initial path node and the termination path node and the driving history data corresponding to the path.

For example, assume that only the path node A is stored in the driving record database 11 to The path of the path node B is "path node A - path node B" and the path of the path node B to the path node C is "path node B - path node C".

The driving history data corresponding to the path "path node A-path node B" are "driving time 300 seconds", "travel time 310 seconds", and "travel time 320 seconds", respectively, and path "path node B-path node C" The corresponding driving history data are “driving time 100 seconds”, “driving time 110 seconds” and “driving time 120 seconds”.

The receiving module 12 receives the path node A selected by the user as the starting path node, and selects the path node C as the ending path node, and the query module 13 cannot query the corresponding path in the bicycle record database 11 and corresponds to the path. The driving history data, at this time, the path of the path node A to the path node B according to the path of the path node A to the path node B is "path node A - path node B" and the path of the path node B to the path node C is "path node B". The path node C" estimates that the path from the path node A to the path node B is "path node A - path node B - path node C".

According to the driving history data corresponding to the path "path node A-path node B", the average value of "driving time 300 seconds", "driving time 310 seconds", and "driving time 320 seconds" is "driving time 310 seconds". And the driving history data corresponding to the path "path node B-path node C" is the average of "driving time 100 seconds", "driving time 110 seconds", and "driving time 120 seconds" is "driving time 110 seconds" The driving history data corresponding to the path "path node A-path node B-path node C" is "driving time 440 seconds".

After the data prediction module 16 estimates the path corresponding to the initial path node and the termination path node and the driving history data corresponding to the path, the analysis module 14 and the path selection module 15 can be displayed correspondingly. The queried path and the selection result corresponding to the path are provided to provide a user with a path selection.

When the query module 13 cannot query the corresponding path and the driving history data corresponding to the path according to the starting path node and the ending path node bicycle record database 11, the user may be provided to filter and select suitable driving history data.

Next, the operation mode and flow of the present invention will be explained below by way of a practical example. The following embodiments will be described with reference to "first figure" and "second figure", and "second figure" is shown. It is a flow chart of the most suitable path and path data and path data selection method for the present invention.

Please refer to "Fig. 3", which shows the path and path node of the most suitable path and path data and path data selection for the present invention.

As shown in FIG. 3, the path nodes are path node A, path node B, path node C, path node D, path node E, path node F, path node G, path node H, and path node I, respectively. The path node A, the path node B, the path node C, the path node D, the path node E, the path node F, the path node G, the path node H, and the path node I are connected in a shape of a field word.

From the path node A to the path node I may include "path node A - path node B - path node C - path node F - path node I" ("Figure 3 only shows the schematic of this path, the rest The path is not shown, and is merely illustrative here, and is not limited to the application scope of the present invention. "Path node A-path node B-path node E-path node F-path node I", "path" Node A - Path Node B - Path Node E - Path Node H - Path Node I", "Path Node A - Path Node D - Path Node E - Path Node F - Path Node I", "Path Node A - Path Node D" - Path node E-path node H-path node I" and "path node A - path node D - path node G - path node H - path node I" six paths.

Next, please refer to "4A" and "4B", and "4A" is a schematic diagram of the driving record database for selecting the most suitable path and path data and path data for the present invention; "Block 4B" The schematic diagram of the driving history data for selecting the most suitable path and path data and path data for the present invention is shown.

The path node A to the path node B, the path node A to the path node C, the path node A to the path node D, ..., the path node A to the path node I, and the path node B to the path node A are respectively stored in the driving record database 11. Path Node B to Path Node C, Path Node B to Path Node D, ..., Path Node B to Path Node I, Path Node C to Path Node A, Path Node C to Path Node B, Path Node C to Path Node D , ..., path node C to path node I, ..., path node I to path node A, path node I to path node B, path node I to path node C, ..., path node I to path node H path and The driving history data corresponding to the path is merely exemplified herein, and is not intended to limit the application scope of the present invention.

In the embodiment, the path 21 of the route record database 11 to the path node I and the route history data 22 corresponding to the path 21 are respectively "path node A - path node B - path node C - path Node F-Path Node I", "Path Node A - Path Node B - Path Node E - Path Node H - Path Node I" and "Path Node A - Path Node D - Path Node G - Path Node H - Path Node I "Three paths (step 101).

For the driving history data 22 corresponding to the path 21 "path node A - path node B - path node C - path node F - path node I", please refer to "4B" (step 101), in "4B" The driving history data 22 is merely illustrative and is not intended to limit the scope of application of the present invention.

For the driving history data 22 corresponding to the path 21 "path node A - path node B - path node E - path node H - path node I", please refer to "Fig. 4B" (step 101), in "Fig. 4B" The driving history data 22 is merely illustrative and is not intended to limit the scope of application of the present invention.

For the driving history data 22 corresponding to the path 21 "path node A - path node D - path node G - path node H - path node I", please refer to "Fig. 4B" (step 101), in "Fig. 4B" The driving history data 22 is merely illustrative and is not intended to limit the scope of application of the present invention.

The receiving module 12 receives the path node A selected by the user as the starting path node and selects the path node I as the ending path node (step 102), and the query module 13 can receive the starting path according to the receiving module 12. The node 21 is the "path node A" and the termination path node is the "path node I". The corresponding path 21 is the path node A-path node B-path node C-path node F-path node I", "path node A - path node B - path node E - path node H - path node I" and "path node A - path node D - path node G - path node H - path node I" three paths 21 .

And querying the driving history data 22 corresponding to the path 21 "path node A - path node B - path node C - path node F - path node I" respectively is "the morning fuel consumption of 0.5 liters at 4 o'clock" and "four o'clock in the morning" Fuel consumption of 0.5 liters, "Millennium fuel consumption of 0.45 liters at 4 o'clock in the morning", "0.55 liters of fuel consumption at 4 o'clock in the morning", "2 liters of fuel consumption at 8 o'clock in the morning", "0.7 liters of fuel consumption at 8 o'clock in the morning", "8 o'clock in the morning" Fuel consumption is 0.75 liters, "0.8 liters of fuel consumption at 6 o'clock in the evening" and "0.75 liters of fuel consumption at 6 o'clock in the evening" (step 103).

The driving history data 22 corresponding to the path 21 "path node A - path node B - path node E - path node H - path node I" is queried as "1 liter of fuel consumption at 4 o'clock in the morning" and "fuel consumption at 4 o'clock in the morning" 1 liter, "11 liters of fuel consumption of 1.1 liters in the morning", "0.9 liters of fuel consumption at 4 o'clock in the morning", "1.5 liters of fuel consumption at 8 o'clock in the morning", "1.4 liters of fuel consumption at 8 o'clock in the morning", "fuel consumption at 6 o'clock in the evening" 1.5 liters and "6 pm driving fuel consumption 1.6 Lit (step 103).

And querying the driving history data 22 corresponding to the path 21 "path node A - path node D - path node G - path node H - path node I" is respectively "4 am driving fuel consumption 0.4 liters", "4 am driving" Fuel consumption 0.4 liters, "4 hrs driving fuel consumption 0.45 liters", "4:00 am fuel consumption 0.35 liters", "8 o'clock morning fuel consumption 0.5 liters", "8 o'clock morning fuel consumption 0.55 liters", "6 o'clock at night" The fuel consumption is 0.6 liters and the fuel consumption at 0.5 pm is 0.55 liters (step 103).

Please refer to "4A", "4B" and "5A" to "5C" at the same time. "5A" and "5C" are shown as paths for the present invention. The histogram of the analysis process for path data selection.

The analysis module 14 according to the path 21 "path node A - path node B - path node C - path node F - path node I" corresponding to the driving history data 22 respectively, "4 am driving fuel consumption 0.5 liters", "4 am The fuel consumption of the train is 0.5 liters, "the fuel consumption is 0.45 liters at 4 am, the fuel consumption is 0.55 liters at 4 am, the fuel consumption is 2 liters at 8 o'clock in the morning", the fuel consumption is 0.7 liters at 8 o'clock in the morning, and 8 o'clock in the morning. The fuel consumption of the train was 0.75 liters, "the fuel consumption of the vehicle at 8 o'clock in the evening was 0.8 liters" and the "fuel consumption of 0.75 liters at 6 o'clock in the evening" was analyzed.

The analysis module 14 first deletes the discrete driving history data 22 as "the fuel consumption of 2 liters at 8 o'clock in the morning". This driving history data 22 is generally too special driving conditions, such as: car accident, road construction, etc., only For the sake of illustration, the scope of application of the present invention is not limited thereto, and therefore needs to be deleted to avoid the problem of inaccurate analysis results.

Then, the analysis module 14 lists the corresponding histograms according to the driving history data 22, as shown in "5A", and divides the driving history data 22 into "driving fuel consumption of 0.5 liters" and "driving fuel consumption 0.45 according to the histogram. The first group of driving history data of “liters” and “0.55 liters of fuel consumption”, “0.7 liters of fuel consumption”, “0.75 liters of fuel consumption”, “0.8 liters of fuel consumption”, “0.8 liters of fuel consumption”, “0.75 liters of fuel consumption” And the second group of driving history data of "0.7 liters of fuel consumption".

The analysis module 14 can analyze the average fuel consumption of the vehicle according to the first group of driving history data 22 as "0.5 liter" and the variation value is "0.05 liter" and the corresponding first selection result is "0.5 ± 0.05 liter". (Step 104), the analysis module 14 can analyze the average fuel consumption of the vehicle according to the second group driving history data 22 as "0.75 liter" and the variation value is "0.05 liter" and corresponding The second selection result generated is "0.5 ± 0.05 liters" (step 104).

Next, the analysis module 14 according to the path 21 "path node A - path node B - path node E - path node H - path node I" corresponding to the driving history data 22 respectively, "4 o'clock in the morning driving fuel consumption 1 liter", "morning morning 4 o'clock driving fuel consumption 1 liter", "4 o'clock morning fuel consumption 1.1 liters", "4 o'clock morning fuel consumption 0.9 liters", "8 o'clock morning fuel consumption 1.5 liters", "8 o'clock morning fuel consumption 1.4 liters", "evening Analysis of the fuel consumption of 1.5 liters at 6 o'clock and 1.6 liters of fuel consumption at 6 o'clock in the evening.

The analysis module 14 lists the corresponding histograms according to the driving history data 22, as shown in "5B", and divides the driving history data 22 into "driving fuel consumption of 1 liter" and "driving fuel consumption of 1.1 liters" according to the histogram. And the third group of driving history data of "fighting fuel consumption of 0.9 liters", "1.5 liters of fuel consumption", "1.4 liters of fuel consumption", "1.6 liters of fuel consumption", "1.5 liters of fuel consumption", "1.4 liters of fuel consumption" and The fourth group of driving history data of “1.6 liters of fuel consumption”.

And the analysis module 14 can analyze the average fuel consumption of the vehicle according to the third group driving history data 22 as "1 liter" and the variation value is "0.1 liter" and the corresponding third selection result is "1 ± 0.1 liter". (Step 104), the analysis module 14 can analyze the average fuel consumption of the vehicle according to the fourth group driving history data 22 as "1.5 liters" and the variation value is "0.1 liters" and the corresponding fourth selection result is "1.5 ± 0.1 liters" (step 104).

Next, the analysis module 14 according to the path 21 "path node A - path node D - path node G - path node H - path node I" corresponding to the driving history data 22 respectively, "4 am driving fuel consumption 0.4 liters", "morning morning 4 o'clock driving fuel consumption 0.4 liters, "4 o'clock morning fuel consumption 0.45 liters", "4 o'clock morning fuel consumption 0.35 liters", "8 o'clock morning fuel consumption 0.5 liters", "8 o'clock morning fuel consumption 0.55 liters", "evening The 6-point driving consumes 0.6 liters and the "6 pm driving consumes 0.55 liters" for analysis.

The analysis module 14 lists the corresponding histograms according to the driving history data 22, as shown in "5C", and divides the driving history data 22 into "driving fuel consumption of 0.4 liters" and "driving fuel consumption of 0.45 liters" according to the histogram. And the fifth group of driving history data of “0.35 liters of fuel consumption”, “0.5 liters of fuel consumption”, “0.55 liters of fuel consumption”, “0.6 liters of fuel consumption”, “0.6 liters of fuel consumption”, “0.55 liters of fuel consumption” and The sixth group of driving history data of “0.5 liters of fuel consumption”.

The analysis module 14 can analyze the average fuel consumption of the vehicle according to the fifth group driving history data 22 as "0.45 liter" and the variation value is "0.05 liter" and the corresponding fifth selection result is "0.45 ± 0.05 liter". (Step 104), the analysis module 14 can analyze the average fuel consumption of the vehicle according to the sixth group driving history data 22 as "0.55 liter" and the variation value is "0.05 liter" and the corresponding second selection result is " 0.55 ± 0.05 liters" (step 104).

Finally, next, the first selection result of the path selection module 15 corresponding to the display path 21 "path node A - path node B - path node C - path node F - path node I" is "0.5 ± 0.05 liters" and The second selection result is "0.5±0.05 liters", and the third selection result corresponding to the display path 21 "path node A-path node B-path node E-path node H-path node I" is "1±0.1 liter" and the The result of the four selection is "1.5 ± 0.1 liter", and the fifth selection result corresponding to the display path 21 "path node A - path node D - path node G - path node H - path node" is "0.45 ± 0.05 liter" and the The result of the six selection is "0.55 liters" to provide the user with a choice of path (step 105).

In addition, when the query module 13 cannot query the corresponding path and the driving history data corresponding to the path according to the starting path node and the ending path node bicycle record database 11, the data prediction module 16 can be used according to the data prediction module 16 The existing data presumes that the path corresponding to the start path node and the end path node and the travel history data corresponding to the path are estimated.

That is, the path 211 in which only the path node A to the path node B are stored in the driving record database 11 is "path node A - path node B" and the path 212 of the path node B to the path node C is "path node B - path node" C".

The driving history data 221 corresponding to the path 211 "path node A - path node B" is "the fuel consumption of the vehicle is 0.9 liters", the "fuel consumption of the vehicle is 1 liter", and the "fuel consumption of the vehicle is 1.1 liters", please refer to the "figure 6A". "Fig. 6A" is a schematic diagram showing the driving history data of the most suitable path and path data and path data selection according to the present invention, and the driving history data 222 corresponding to the path 212 "path node B-path node C" is respectively "the driving fuel consumption". 0.4 liters, "0.5 liters of fuel consumption" and "0.6 liters of fuel consumption", please refer to "Figure 6B", "Figure 6B" shows the most suitable path and path data and route data selection for the present invention. In the historical data schema, the path of the path node A to the path node C and the corresponding driving history data are not stored in the driving record database 11.

The receiving module 12 receives the path node A selected by the user as the starting path node, and selects the path node C as the ending path node, and the query module 13 cannot access the bicycle record database 11 The corresponding path and the driving history data corresponding to the path are queried, and the path 211 of the path node A to the path node B can be referred to as “path node A-path node B” and path node B through the data prediction module 16 at this time. The path 212 to the path node C is "path node B - path node C". It is estimated that the path 213 of the path node A to the path node B is "path node A - path node B - path node C", please refer to "6C" As shown in the figure, "6C" is a schematic diagram showing the predicted driving history data of the most suitable path and path data and path data selection according to the present invention.

According to the driving history data 221 corresponding to the path 211 "path node A - path node B", the average value of "fuel consumption of 0.9 liters", "fuel consumption of 1 liter" and "fuel consumption of 1.1 liters" is "the fuel consumption of the vehicle 1". The liters and the driving history data 222 corresponding to the path 212 "path node B-path node C" are the average of "driving fuel consumption 0.4 liters", "driving fuel consumption 0.5 liters", and "driving fuel consumption 0.6 liters" respectively. The 0.5 liter "estimated driving history data 223 corresponding to the path 213 "path node A - path node B - path node C" is "the fuel consumption of the vehicle is 1.5 liters", please refer to the "6C figure".

After the data prediction module 16 estimates the path corresponding to the initial path node and the termination path node and the driving history data corresponding to the path, the analysis module 14 and the path selection module 15 can be displayed correspondingly. The queried path and the selection result corresponding to the path are provided to provide a user with a path selection.

When the query module 13 cannot query the corresponding path and the driving history data corresponding to the path according to the starting path node and the ending path node bicycle record database 11, the user may be provided to filter and select suitable driving history data.

In summary, it can be seen that the difference between the present invention and the prior art is that the driving history data corresponding to the path between the path nodes is analyzed from the pre-established driving record database, and the driving history data includes driving time, driving fuel consumption, and calculation. And generating the driving index, etc., to analyze at least one average value of each path and the variation value corresponding to the average value, and correspondingly generate at least one selection result, correspondingly displaying the queried path and the selection result corresponding to the path, In order to provide the user with a choice of path, the path of the different needs of the user can be provided according to different driving history data.

By means of this technical means, the existing driving navigation device of the prior art can provide the shortest path to provide the problem that cannot meet the different needs of the user, thereby achieving the technical effect of providing the most suitable path and path data selection according to different driving history data. .

Although the disclosed embodiment of the present invention is as above, the content is not intended to be straight The scope of patent protection of the present invention is limited. Any changes in the form and details of the embodiments may be made without departing from the spirit and scope of the invention. The scope of the invention is to be determined by the scope of the appended claims.

11‧‧‧ Driving Record Database

12‧‧‧ receiving module

13‧‧‧Query Module

14‧‧‧Analysis module

15‧‧‧Path Selection Module

16‧‧‧ Data Prediction Module

Claims (10)

A selection system of most suitable path and path data, comprising: a row of vehicle record database for storing at least one path of each path node to the remaining path nodes and a plurality of driving history data corresponding to the path; a module, configured to receive a start path node and a stop path node; a query module, configured to query, according to the start path node and the termination path node, a corresponding path from the driving record database and And the driving history data corresponding to the path, when the corresponding path and the driving history data corresponding to the path cannot be queried from the driving record database according to the starting path node and the ending path node, Providing a user to set a suitable driving history data; an analysis module for respectively analyzing the driving history data corresponding to the path to analyze at least one average value of each path and corresponding to the average value a variability value correspondingly generates at least one selection result; and a path selection module for corresponding display being queried The path and the path corresponding to the selection result, to provide a user to select the path. The selection system of the most suitable path and path data as described in claim 1, wherein the driving history data includes driving time, driving fuel consumption, and calculated driving indicators. The selection system of the most suitable path and path data according to the first aspect of the patent application, wherein the analysis module further comprises deleting the discrete driving history data and then performing analysis of the driving history data corresponding to the path. . The selection system of the most suitable path and path data as described in claim 1, wherein the receiving module further comprises receiving a query condition, and the analyzing module further comprises performing the driving history according to the query condition. After the data is filtered, the analysis of the driving history data corresponding to the path is performed, and the query condition is one of time or fuel consumption. The system for providing path and path data according to claim 1, wherein the query module cannot query the source record database according to the start path node and the termination path node. Corresponding path and the driving history data corresponding to the path, the path corresponding to the starting path node and the ending path node and the driving history data corresponding to the path are performed by a data prediction module according to the existing data. Estimate. A method for selecting a most suitable path and path data, comprising the steps of: providing a row of vehicle record database storing at least one path of each path node to the remaining path nodes and a plurality of driving history data corresponding to the path; a starting path node and a terminating path node; querying, according to the starting path node and the terminating path node, a corresponding path from the driving record database and the driving history data corresponding to the path; When the starting path node and the terminating path node query the corresponding path and the driving history data corresponding to the path from the driving record database, provide the user to set and filter the suitable driving history data; The driving history data corresponding to the path is analyzed to analyze at least one average value of each path and a variation value corresponding to the average value to generate at least one selection result correspondingly; and the corresponding display is queried a path and the selection result corresponding to the path to provide a user Select path. The method for selecting the most suitable path and path data as described in claim 6 of the patent application, wherein the driving record database storing a plurality of driving history data corresponding to all paths and paths of each path node to the remaining path nodes is provided. In the step, the driving history data includes driving time, driving fuel consumption, and calculated driving indicators. For example, the method for selecting the most suitable path and path data as described in item 6 of the patent application scope, The step of analyzing the driving history data corresponding to the path to analyze the average value and the variation value of each path further includes deleting the discrete driving history data and then performing the driving history data corresponding to the path. analysis. The method for selecting the most suitable path and path data, as described in claim 6, wherein the method for selecting the path and the path data further comprises receiving a query condition; and performing the driving history data screening according to the query condition. Then, the analysis of the driving history data corresponding to the path is performed. The method for selecting the most suitable path and path data according to item 6 of the patent application scope, wherein the corresponding path and path are queried from the driving record database according to the starting path node and the ending path node. The step of the corresponding driving history data further includes: when the corresponding path and the driving history data corresponding to the path cannot be queried from the driving record database according to the starting path node and the ending path node; And performing, according to the existing data, a step of the path corresponding to the start path node and the end path node, and an estimation of the driving history data corresponding to the path.