CN112289061A - Driving trip departure reminding system and method based on big data - Google Patents

Driving trip departure reminding system and method based on big data Download PDF

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CN112289061A
CN112289061A CN202011084380.3A CN202011084380A CN112289061A CN 112289061 A CN112289061 A CN 112289061A CN 202011084380 A CN202011084380 A CN 202011084380A CN 112289061 A CN112289061 A CN 112289061A
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祁星星
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
    • G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
    • G08G1/096877—Systems involving transmission of navigation instructions to the vehicle where the input to the navigation device is provided by a suitable I/O arrangement
    • G08G1/096883—Systems involving transmission of navigation instructions to the vehicle where the input to the navigation device is provided by a suitable I/O arrangement where input information is obtained using a mobile device, e.g. a mobile phone, a PDA

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Abstract

The invention discloses a driving trip departure reminding system and method based on big data, which comprises the following steps: the system comprises a singlechip, a database, a voice recognition module, a road condition image acquisition module, a GPS module, a controller, a WIFI module and a mobile phone terminal, wherein the road condition image acquisition module acquires the conditions of road vehicles in a driving area and divides the road conditions into three types of smooth, slow and congestion, the GPS module positions the position of a gas station when a driver searches a temporary gas station at a high speed due to insufficient oil, the data in the road condition image acquisition module, the GPS module and the voice recognition module are transmitted to the controller through the singlechip, the invention discloses a driving trip departure reminding method based on big data, which helps a driver to plan an optimal path for searching for a gas station and a destination parking space, reduces damage to an engine caused by driving with insufficient oil and reduces potential safety hazards of driving trip.

Description

Driving trip departure reminding system and method based on big data
Technical Field
The invention relates to the technical field of big data driving intellectualization, in particular to a driving trip departure reminding system and method based on big data.
Background
When a driver goes out, the driver often encounters a traffic jam condition due to wrong route selection, therefore, the selection of a correct route according to road conditions is crucial, when the driver drives on a highway, the situation that the driver cannot reach a destination due to insufficient vehicle oil quantity can occur, the driver needs to go to a service area to refuel, when the remaining vehicle oil quantity of the driver cannot support the vehicle to reach the service area, the driver needs to search a gas station at a high speed to refuel, the driver blindly searches the gas station and only wastes the vehicle oil, an optimal route with less oil consumption or less refueling needs to be planned, in addition, when the driver arrives at the destination and needs to temporarily stop the vehicle, the driver often finds an empty parking space to stop the vehicle when the vehicle stops more, a lot of time can be wasted on parking, and the route arrangement after the vehicle stops is delayed.
Therefore, a driving departure reminding system and method based on big data are needed to solve the above problems.
Disclosure of Invention
The invention aims to provide a driving trip departure reminding system and method based on big data, so as to solve the problems in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: a driving trip departure reminding system based on big data comprises: the output ends of the voice recognition module, the road condition image acquisition module and the GPS module are connected with the input end of the singlechip, the voice recognition module is used for recognizing the identity of a driver, the road condition image acquisition module is used for image acquisition and classification of real-time road conditions, the GPS module is used for positioning the position of a gas station, the output end of the singlechip is connected with the input end of the controller, the output end of the controller is connected with the input ends of the database and the WIFI module, the output end of the database is connected with the input end of the controller, the output end of the WIFI module is connected with the mobile phone terminal, and the WIFI module is used for wirelessly transmitting data in the controller to the mobile phone terminal, the mobile phone terminal is used for displaying route and road condition information.
Furthermore, the database stores parking space information of parking lots commonly used by drivers, commonly used driving routes, driving data of different drivers and identification keywords of different drivers, and the data stored in the database helps the system to judge the identities of the drivers, the driving speeds and the oil consumption degrees of the corresponding drivers and count the number of vacant parking spaces when the drivers arrive at the destination parking lots.
Furthermore, after the GPS module locates the current position of the driver, the destination to which the driver drives is determined, then whether a route from the current position to the destination exists is searched from a database, if the route corresponds to the current position, the road condition information of the corresponding route is collected through the road condition image collection module and fed back to the mobile phone terminal, if the route does not correspond to the current position, a map on the mobile phone terminal is used for searching for a feasible route, if the road condition of the corresponding route is smooth, the driver drives according to the corresponding route, if the corresponding route is congested, other suitable routes are searched, an optimal route is planned according to the real-time road condition, the driving time is saved, and the safety in the driving process is improved.
Further, the process of acquiring the road condition by the road condition image acquisition module is as follows: firstly, extracting a road area in an image, then counting a gray histogram, wherein the gray histogram refers to a description graph for counting the occurrence frequency of all pixels in a digital image according to the size of a gray value, because the dimension of a feature vector of the gray histogram is too high, filtering processing needs to be carried out on the image to reduce the dimension of the feature vector of the gray histogram, dimension reduction processing is carried out on the image by using an LDA algorithm, and the road condition is divided into: the method comprises three types of smoothness, slow walking and congestion, an LDA (linear discriminant analysis) algorithm is an effective feature dimension reduction method for classification, and the filtering process smoothes the gray level abrupt change interference while retaining the gray level characteristics of the image.
A driving trip departure reminding method based on big data comprises the following steps:
s1: identifying the identity of the driver through a voice identification module;
s2: the database calls driving data which are collected in advance and correspond to the driver;
s3: calculating the percentage of oil consumption of the driver in the remaining distance according to the distance of the destination;
s4: comparing the database to judge whether the driver can reach the destination;
s5: GPS positions the position of the service area at high speed and calculates whether the service area can be reached;
s6: the lower high speed utilizes GPS to position the gasoline station and find the most suitable gasoline station;
s7: and planning an optimal path for finding the vacant temporary parking spaces of the destination.
Further, in step S1: the voice recognition module stores recognition keywords of different drivers, the drivers speak the keywords to the voice recognition module before starting, the voice recognition module recognizes the keywords and calls the keywords of the drivers from the database to match the keywords so as to confirm the identities of the drivers, the driving habits of the different drivers are different, and the identities of the drivers are recognized, so that the optimal routes can be planned for the drivers more accurately.
Further, in step S2: the driving data of the driver collected in advance by the database comprises: setting the average running speed of a corresponding driver as V and the average fuel consumption percentage of the corresponding driver as B according to the running speed set and the fuel consumption percentage set of the corresponding driver in the daily normal-open process of the driver, and setting the running speed set of the corresponding driver as { V + V }1，v2，v3，...viCalculating the average driving speed of the corresponding driver
Figure BDA0002719856880000031
i > 0 and i is an integer,
if the collected percentage set of the oil consumption of the corresponding driver driving per hundred kilometers is { b }1，b2，b3，...bjCalculating the average fuel consumption percentage of each hundred kilometers of the corresponding driver
Figure BDA0002719856880000032
j > 0 and j is an integer.
Further, in steps S3-S5: setting the distance from the driver to the destination in the middle of high speed as X, the percentage of the remaining oil quantity in the middle of high speed as B, the percentage of the oil consumption of the corresponding driver in the remaining distance as a1, and calculating the percentage of the oil consumption of the corresponding driver in the remaining distance according to the calculated average percentage of the oil consumption B of the corresponding driver in each hundred kilometers in driving
Figure BDA0002719856880000033
If the remaining oil percentage b during the high-speed midway is larger than the fuel consumption percentage a1 during the remaining distance driving, the corresponding driver can reach the destination without refueling, if the remaining oil percentage b during the high-speed midway is smaller than the fuel consumption percentage a1 during the remaining distance driving, the position of the service area at the high speed needs to be positioned through a GPS, whether the driver can reach the service area for refueling is judged, the distance from the driver to the service area during the high-speed midway is set as Y, the fuel consumption percentage from the driver to the service area during the high-speed midway is set as a2, and the fuel consumption percentage corresponding to the driver to the service area during the high-speed midway is calculated
Figure BDA0002719856880000034
If the remaining oil percentage b during high-speed midway is greater than the oil consumption percentage a2 during high-speed midway to the service area, the driver can reach the service area, if the remaining oil percentage b during high-speed midway is less than the oil consumption percentage a2 during high-speed midway to the service area, the driver cannot reach the service area and needs to locate the position of the gas station and find the most appropriate gas station by using a GPS at a high speed in the step S6, and more choices are provided for planning to find the appropriate route for filling the gas station at a low speed and filling the gas in the gas station at a low speed in consideration that the service area cannot be found depending on the remaining oil.
Further, in step S6: the method comprises the steps of enabling a driver to drive at a high speed midway to a destination at a distance of X, enabling the average fuel consumption percentage of every hundred kilometers of a driver to be B, enabling the positions of charging piles to be different, enabling the driving routes to be different, enabling the position of a lower high speed to be a point C, enabling the positions of two different charging piles to be a point D and a point E respectively, enabling the position of the destination to be a point F, enabling two different routes to be arranged, enabling the two different routes to be a route CDF and a route CEF, enabling the distance from the point C to the point D to be D1, the distance from the point D to the point F to be D2, enabling the distance from the point C to the point E to be D3, enabling the distance from the point E to the point F to be D4, enabling the remaining fuel consumption percentage of the point C at the lower high speed to be M, enabling the fuel consumption percentage of the point C to the point D to be M1, enabling the percentage of the point D toThe percentage of oil consumption from point E to point F is M4, and the percentage of oil consumption from point C to point D is calculated
Figure BDA0002719856880000041
Calculating the percentage of the remaining oil quantity at the point D to be M-M1, and calculating the percentage of the oil consumption from the point D to the point F
Figure BDA0002719856880000042
Calculating the percentage of oil consumption from point C to point E
Figure BDA0002719856880000043
Calculating the percentage of the remaining oil quantity at the point E to be M-M3, and calculating the percentage of the oil consumption from the point E to the point F
Figure BDA0002719856880000044
Calculating the percentage of oil consumption and the percentage of remaining oil at each point is beneficial to selecting a route more suitable for driving according to the oil consumption and the oil amount required to be added, if the formula is lacked, the difficulty of planning an optimal route is increased, when d1+ d2 is d3+ d4, d1+ d2 > X and d3+ d4 > X, the total oil consumption percentages of the route CDF and the route CEF are the same, if M-M1 is more than M-M3, the route CDF is preferentially selected, otherwise, the route CEF is selected; when d1+ d2 ≠ d3+ d4, d1+ d2 > X and d3+ d4 > X, if M1+ M2>M3+ M4, the route CDF is selected, otherwise the route CEF is selected, the total oil consumption and the oil amount required to be added are comprehensively considered, the most appropriate route is planned, the total oil consumption and the oil adding cost are reduced, and the cost is saved.
Further, in step S7: after the vehicle reaches the destination, the vacant temporary parking spaces need to be searched, and the optimal path needs to be planned for searching the vacant temporary parking spaces in the destination: setting n temporary parking spaces, setting a parking entrance as a point H, establishing a coordinate system by taking the point H as an original point, and marking out a coordinate set { (x) of the parking spaces1，y1)，(x2，y2)，(x3，y3)...(xn，yn) The database counts the number of empty parking spaces according to the number of the vehicles entering and leaving, and sets the time of the vehicle reaching the destination as t, wherein t is the time between t and 30 and t]Time of dayIn the section, setting the original vehicle as m1Vehicle, entering vehicle m2M, the outgoing vehicle3And calculating the number m of the remaining empty parking spaces at the moment t as n- (m)2-m3)-m1Calculating the workload of arriving the empty parking space after the residual empty parking space at the time t is reduced, calculating the distance of arriving the occupied parking space without calculating the distance of arriving the occupied parking space, and marking the residual empty parking space coordinates { (x)Hollow 1，yHollow 1)，(xHollow 2，yHollow 2)，...(xEmpty m，yEmpty m) Seeking the best empty parking space position coordinate (x) according to the distance from the vehicle to each empty parking spaceAir conditioner，yAir conditioner) And setting the distance set of the parking entrance to the empty parking space as { k }1，k2，k3...kzAnd the shortest distance from the parking entrance to the empty parking space
Figure BDA0002719856880000045
Calculating the shortest distance is helpful for judging the position of the optimal vacant parking space, the difficulty of searching the optimal vacant parking space is increased due to the lack of the formula, if the optimal vacant parking space is occupied when the optimal vacant parking space is reached, the alternative vacant parking space is searched according to the distance, and the coordinate of the alternative vacant parking space is set as (x)Prepare for，yPrepare for) The distance from the optimal empty parking space to the alternative empty parking space is
Figure BDA0002719856880000046
The empty parking space closest to the best empty parking space is the spare empty parking space, so that an effective prompt is provided for the driver to park, and the difficulty of finding the parking space for temporary parking is reduced.
Compared with the prior art, the invention has the following beneficial effects:
1. because the road conditions of different driving routes are different, the problems of congestion and the like can be encountered when a wrong route is selected, the road condition image acquisition module acquires the road condition images in real time, the road conditions are divided into three types of smooth, slow running and congestion, and the common route road condition data in the database is combined to provide help for a driver, so that the driver is helped to select a better route, and the time wasted on the road is saved;
2. when the automobile is driven on a highway, the problem that the automobile is not enough in engine oil to support the automobile to run to a destination can be met midway, and two situations are considered: (1) under the condition that the residual fuel quantity is enough to support the automobile to drive to the nearest service area gas station, the automobile can be driven to the service area to be refueled; (2) under the condition that the remaining fuel quantity is not enough to support the automobile to drive to the nearest service area gas station, the nearby gas station needs to be searched at a high speed for refueling, the position of the nearby gas station is located by using a GPS, the two aspects of the total fuel consumption for reaching a destination and the fuel quantity needed to be added for reaching the gas station are comprehensively considered, an optimal route is planned for a driver, the total fuel consumption and the refueling cost are reduced, and the cost is saved.
3. The invention is characterized in that when a driver drives to a destination, temporary parking spaces need to be searched, and the problem that too many parked cars are difficult to find empty parking spaces is likely to occur.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a driving departure reminding system based on big data according to the present invention;
FIG. 2 is a flow chart of a road condition image acquisition module according to the present invention;
FIG. 3 is a flow chart of the present invention for planning a route according to road conditions;
FIG. 4 is a step diagram of a driving departure reminding method based on big data according to the present invention;
fig. 5 is a flowchart of finding an optimal parking space according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to fig. 1-5, the present invention provides the following technical solutions: a driving trip departure reminding system based on big data comprises: the singlechip, a database, the speech recognition module, road conditions image acquisition module, the GPS module, a controller, WIFI module and mobile phone terminal, the speech recognition module, the output of road conditions image acquisition module and GPS module is connected with the input of singlechip, the speech recognition module is used for discerning driver's identity, road conditions image acquisition module is used for carrying out image acquisition and classification to real-time road conditions, the GPS module is used for fixing a position of filling station, the output of singlechip is connected with the input of controller, the output and the database of controller, the input of WIFI module is connected, the output of database is connected with the input of controller, the output and the cell-phone terminal connection of WIFI module, the WIFI module is used for the data wireless transmission in the controller to mobile phone terminal, the cell-phone terminal is used for showing route and road conditions information.
The database stores parking space information of parking lots commonly used by drivers, commonly used driving routes, driving data of different drivers and identification keywords of different drivers.
After the GPS module positions the current position of a driver, firstly determining the destination to which the driver drives, then searching whether a route from the current position to the destination exists in a database, if the route corresponds to the destination, acquiring road condition information of the corresponding route through a road condition image acquisition module and feeding the road condition information back to the mobile phone terminal, if the route does not correspond to the destination, searching for a feasible route by using a map on the mobile phone terminal, if the road condition of the corresponding route is smooth, driving the driver according to the corresponding route, and if the corresponding route is blocked, searching for other suitable routes.
The process of acquiring the road conditions by the road condition image acquisition module is as follows: firstly, extracting a road area in an image, then counting a gray level histogram, wherein the gray level histogram refers to a description chart obtained by counting the occurrence frequency of all pixels in a digital image according to the size of a gray level, because the dimension of a feature vector of the gray level histogram is too high, filtering processing needs to be carried out on the image to reduce the dimension of the feature vector of the gray level histogram, dimension reduction processing is carried out on the image by an LDA algorithm, and road conditions are divided into: the method comprises three types of smoothness, slow walking and congestion, wherein an LDA (linear discriminant analysis) algorithm is an effective characteristic dimension reduction method for classification, and filtering processing is used for smoothing gray level abrupt change interference.
A driving trip departure reminding method based on big data comprises the following steps:
s1: identifying the identity of the driver through a voice identification module;
s2: the database calls driving data which are collected in advance and correspond to the driver;
s3: calculating the percentage of oil consumption of the driver in the remaining distance according to the distance of the destination;
s4: comparing the database to judge whether the driver can reach the destination;
s5: GPS positions the position of the service area at high speed and calculates whether the service area can be reached;
s6: the lower high speed utilizes GPS to position the gasoline station and find the most suitable gasoline station;
s7: and planning an optimal path for finding the vacant temporary parking spaces of the destination.
In step S1: the voice recognition module stores recognition keywords of different drivers, the drivers speak the keywords to the voice recognition module before starting, the voice recognition module recognizes the keywords, and the keywords of the drivers are called from the database to be matched so as to confirm the identities of the drivers.
In step S2: the driving data of the driver collected in advance by the database comprises the following steps: setting the average running speed of a corresponding driver as V and the average fuel consumption percentage of the corresponding driver as B according to the running speed set and the fuel consumption percentage set of the corresponding driver in the daily normal-open process of the driver, and setting the running speed set of the corresponding driver as { V + V }1，v2，v3，...viGet out the correspondencesAverage driving speed of driver
Figure BDA0002719856880000071
i > 0 and i is an integer,
if the collected percentage set of the oil consumption of the corresponding driver driving per hundred kilometers is { b }1，b2，b3，...bjCalculating the average fuel consumption percentage of each hundred kilometers of the corresponding driver
Figure BDA0002719856880000072
j > 0 and j is an integer.
In steps S3-S5: setting the distance from the driver to the destination in the middle of high speed as X, setting the percentage of the remaining oil quantity in the middle of high speed as B, setting the percentage of the oil consumption of the corresponding driver in the remaining distance as a1, and calculating the percentage of the oil consumption of the corresponding driver in the remaining distance according to the calculated average percentage of the oil consumption B of the corresponding driver in each hundred kilometers in driving
Figure BDA0002719856880000073
If the remaining oil percentage b during the high-speed midway is larger than the fuel consumption percentage a1 during the remaining distance driving, the corresponding driver can reach the destination without refueling, if the remaining oil percentage b during the high-speed midway is smaller than the fuel consumption percentage a1 during the remaining distance driving, the position of the service area at the high speed needs to be positioned through a GPS, whether the driver can reach the service area for refueling is judged, the distance from the driver to the service area during the high-speed midway is set as Y, the fuel consumption percentage from the driver to the service area during the high-speed midway is set as a2, and the fuel consumption percentage corresponding to the driver to the service area during the high-speed midway is calculated
Figure BDA0002719856880000074
If the percentage b of the remaining fuel amount during the high speed midway is larger than the percentage a2 of the fuel consumption during the high speed midway to the service area, the driver can arrive at the service area, and if the driver drives at the high speed midwayThe remaining fuel amount percentage b is less than the fuel consumption percentage a2 when the driver is driving at high speed midway to the service area, and the driver cannot reach the service area and needs to locate the fuel station by using the GPS at high speed and find the most suitable fuel station in the step S6.
In step S6: the distance from the driver to the destination in the middle of high speed is X, the average fuel consumption per hundred kilometers of the driver is B, the charging piles are arranged in different driving routes, the lower high speed position is set as a point C, the positions of two different charging piles are respectively set as a point D and a point E, the position of the destination is set as a point F, two different routes are set as a route CDF and a route CEF, the distance from the point C to the point D is D1, the distance from the point D to the point F is D2, the distance from the point C to the point E is D3, the distance from the point E to the point F is D4, the percentage of the remaining oil quantity of the position point C at the lower high speed is set to be M, the percentage of the oil consumption from the point C to the point D is set to be M1, the percentage of the oil consumption from the point D to the point F is set to be M2, the percentage of the oil consumption from the point C to the point E is set to be M3, the percentage of the oil consumption from the point E to the point F is set to be M4, and the percentage of the oil consumption from.
Figure BDA0002719856880000081
Calculating the percentage of the remaining oil quantity at the point D to be M-M1, and calculating the percentage of the oil consumption from the point D to the point F
Figure BDA0002719856880000082
Calculate percent oil consumption from Point C to Point E
Figure BDA0002719856880000083
Calculating the percentage of the remaining oil quantity at the point E to be M-M3, and calculating the percentage of the oil consumption from the point E to the point F
Figure BDA0002719856880000084
When d1+ d2 ═ d3+ d4, d1+ d2 > X and d3+ d4 > X, the total fuel consumption percentages of route CDF and route CEF are the same, if M-M1 > M-M3, route CDF is preferentially selected, otherwise route CEF is selected; when d1+ d2 ≠ d3+ d4, d1+ d2 > X and d3+ d4 > X, if M1+ M2>M3+ M4, route CDF, and route CEF otherwise.
In step S7:after the vehicle reaches the destination, the vacant temporary parking spaces need to be searched, and the optimal path needs to be planned for searching the vacant temporary parking spaces in the destination: setting n temporary parking spaces, setting a parking entrance as a point H, establishing a coordinate system by taking the point H as an original point, and marking out a coordinate set { (x) of the parking spaces1，y1)，(x2，y2)，(x3，y3)...(xn，yn) The database counts the number of empty parking spaces according to the number of the vehicles entering and leaving, and sets the time of the vehicle reaching the destination as t, wherein t is the time between t and 30 and t]In the time period, setting the original vehicle as m1Vehicle, entering vehicle m2M, the outgoing vehicle3And calculating the number m of the remaining empty parking spaces at the moment t as n- (m)2-m3)-m1Marking the coordinates of the remaining empty parking space { (x)Hollow 1，yHollow 1)，(xHollow 2，yHollow 2)，...(xEmpty m，yEmpty m) Seeking the best empty parking space position coordinate (x) according to the distance from the vehicle to each empty parking spaceAir conditioner，yAir conditioner) And the set of the distances from the parking entrance to the empty parking space is set as { k }1，k2，k3...kzThe shortest distance from a parking entrance to an empty parking space
Figure BDA0002719856880000085
If the best empty parking space is occupied when the parking space arrives, then the spare empty parking space is searched according to the distance, and the coordinate of the spare empty parking space is set as (x)Prepare for，yPrepare for) The distance from the optimal empty parking space to the alternative empty parking space is
Figure BDA0002719856880000086
And the empty parking space closest to the best empty parking space is the spare empty parking space.
The first embodiment is as follows: setting the distance X from the high speed to the destination to be 400km, the distance Y from the high speed to the service area to be 350km, the average fuel consumption per hundred kilometers B to be 16%/hundred kilometers, the remaining fuel quantity M of the lower high speed at the position point C to be 50%, the distance D1 from the point C to the point D to be 100km, the distance D2 from the point D to the point F to be 400km, the distance D3 from the point C to the point E to be 200km, and the distance E from the point E to the point D to the point E to be 200kmThe distance d4 of the point F is 300km, because d1+ d2 is d3+ d4 according to the formula
Figure BDA0002719856880000087
The percentage of fuel consumption a1 of the driver in the remaining journey is calculated to be 64%>M, can not arrive at the destination without refueling, according to the formula
Figure BDA0002719856880000091
Calculating the fuel consumption percentage a2 of the driver driving to the service area in the middle of high speed, namely 56%, the driver can not refuel to the service area, the driver needs to refuel to a gas station at a point D or a point E, and the route CDF: according to the formula
Figure BDA0002719856880000092
Calculating the oil consumption percentage M1 from the point C to the point D to be 16 percent, calculating the residual oil quantity percentage M-M1 to be 34 percent of the point D, adding 66 percent of oil, and after the oil is filled, according to a formula
Figure BDA0002719856880000093
Calculating the fuel consumption percentage from the point D to the point F, wherein M2 is 64 percent, and M1+ M2 is 80 percent; route CEF: according to the formula
Figure BDA0002719856880000094
Calculating the percentage of oil consumption from the point C to the point E, namely M3 is 32%, calculating the percentage of the oil quantity remaining from the point E, namely M-M3 is 18%, adding 82% of oil, after the oil is fully added, according to a formula
Figure BDA0002719856880000095
And (3) calculating the fuel consumption percentage M4 to F of 48 percent and M3+ M4 to 80 percent, M1+ M2, and selecting a route CDF, wherein the amount of oil required to be added to D is less than that of the oil required to be added to E.
Example two: the number n of parking spaces for setting a destination is 300, and the arrival time of the own vehicle is 7: 30, in the range of 7: 00 to 7: within 30 half hours, the database calls the original number m of vehicles157, number of vehicles driven in m2280, number of outgoing vehicles m340, according to the formula m-n- (m)2-m3)-m1And 7 is calculated: 30 hours of left empty parking spaceThe number m is 3, the coordinates of the parking entrance H are set to (0, 0), the coordinates of the remaining empty space 1 are set to (2, 3), the coordinates of the remaining empty space 2 are set to (3, 4), the coordinates of the remaining empty space 3 are set to (4, 5), and the distance from H to the remaining empty space is calculated
Figure BDA0002719856880000096
k1<k2<k3The shortest distance from the parking entrance to the empty parking space
Figure BDA0002719856880000097
So the best empty parking space is the residual empty parking space 1, and the distance from the residual empty parking space 2 to the best empty parking space is
Figure BDA0002719856880000098
The distance from the rest empty parking space 3 to the best empty parking space is
Figure BDA0002719856880000099
The spare empty parking space is the residual empty parking space 2, and if the spare empty parking space is occupied, the residual empty parking space 3 is selected.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A driving trip departure reminding system based on big data comprises: the output ends of the voice recognition module, the road condition image acquisition module and the GPS module are connected with the input end of the singlechip, the voice recognition module is used for recognizing the identity of a driver, the road condition image acquisition module is used for image acquisition and classification of real-time road conditions, the GPS module is used for positioning the position of a gas station, the output end of the singlechip is connected with the input end of the controller, the output end of the controller is connected with the input ends of the database and the WIFI module, the output end of the database is connected with the input end of the controller, the output end of the WIFI module is connected with the mobile phone terminal, and the WIFI module is used for wirelessly transmitting data in the controller to the mobile phone terminal, the mobile phone terminal is used for displaying route and road condition information.
2. A big data based driving trip departure reminding system as claimed in claim 1, wherein: the parking space information of the parking lot commonly used by the driver, the commonly used driving route, the driving data of different drivers and the identification keywords of different drivers are stored in the database.
3. A big data based driving trip departure reminding system as claimed in claim 1, wherein: the GPS module is used for positioning the current position of a driver, determining the destination to which the driver drives, searching whether a route from the current position to the destination exists in a database, if the route corresponds to the current position, acquiring road condition information of the corresponding route through the road condition image acquisition module and feeding the road condition information back to the mobile phone terminal, if the route does not correspond to the current position, searching for a feasible route by using a map on the mobile phone terminal, if the road condition of the corresponding route is smooth, driving the driver according to the corresponding route, and if the corresponding route is blocked, searching for other suitable routes.
4. A big data based driving trip departure reminding system as claimed in claim 3, wherein: the road condition image acquisition module acquires the road condition by the following steps: firstly, extracting a road area in an image, then counting a gray histogram, because the dimension of a feature vector of the gray histogram is too high, filtering the image to reduce the dimension of the feature vector of the gray histogram, then performing dimension reduction on the image by using an LDA algorithm, and dividing road conditions into: smooth, slow moving and congestion.
5. A driving trip departure reminding method based on big data comprises the following steps:
s1: identifying the identity of the driver through a voice identification module;
s2: the database calls driving data which are collected in advance and correspond to the driver;
s3: calculating the percentage of oil consumption of the driver in the remaining distance according to the distance of the destination;
s4: comparing the database to judge whether the driver can reach the destination;
s5: GPS positions the position of the service area at high speed and calculates whether the service area can be reached;
s6: the lower high speed utilizes GPS to position the gasoline station and find the most suitable gasoline station;
s7: and planning an optimal path for finding the vacant temporary parking spaces of the destination.
6. The big-data-based driving travel departure reminding method according to claim 5, characterized in that: in step S1: the voice recognition module stores recognition keywords of different drivers, the drivers speak the keywords to the voice recognition module before starting, the voice recognition module recognizes the keywords, and the keywords of the drivers are called from the database to be matched so as to confirm the identities of the drivers.
7. The big-data-based driving travel departure reminding method according to claim 5, characterized in that: in step S2: the driving data of the driver collected in advance by the database comprises: setting the average running speed of a corresponding driver as V and the average fuel consumption percentage of the corresponding driver as B according to the running speed set and the fuel consumption percentage set of the corresponding driver in the daily normal-open process of the driver, and setting the running speed set of the corresponding driver as { V + V }1，v2，v3，...vi}，Calculating the average driving speed of the driver
Figure FDA0002719856870000021
i > 0 and i is an integer,
if the collected percentage set of the oil consumption of the corresponding driver driving per hundred kilometers is { b }1，b2，b3，...bjCalculating the average fuel consumption percentage of each hundred kilometers of the corresponding driver
Figure FDA0002719856870000022
j > 0 and j is an integer.
8. A big data-based driving trip departure reminding method according to claim 5 or 7, wherein: in steps S3-S5: setting the distance from the driver to the destination in the middle of high speed as X, the percentage of the remaining oil quantity in the middle of high speed as B, the percentage of the oil consumption of the corresponding driver in the remaining distance as a1, and calculating the percentage of the oil consumption of the corresponding driver in the remaining distance according to the calculated average percentage of the oil consumption B of the corresponding driver in each hundred kilometers in driving
Figure FDA0002719856870000023
If the remaining oil percentage b during the high-speed midway is larger than the fuel consumption percentage a1 during the remaining distance driving, the corresponding driver can reach the destination without refueling, if the remaining oil percentage b during the high-speed midway is smaller than the fuel consumption percentage a1 during the remaining distance driving, the position of the service area at the high speed needs to be positioned through a GPS, whether the driver can reach the service area for refueling is judged, the distance from the driver to the service area during the high-speed midway is set as Y, the fuel consumption percentage from the driver to the service area during the high-speed midway is set as a2, and the fuel consumption percentage corresponding to the driver to the service area during the high-speed midway is calculated
Figure FDA0002719856870000031
If the remaining oil amount percentage b during the high-speed midway is larger than the fuel consumption percentage a2 during the high-speed midway to the service area, the driver can reach the service area, and if the remaining oil amount percentage b during the high-speed midway is smaller than the fuel consumption percentage a2 during the high-speed midway to the service area, the driver cannot reach the service area and needs to locate the position of the fuel station and find the most appropriate fuel station by using the GPS at a high speed in the step S6.
9. The big-data-based driving travel departure reminding method according to claim 5, characterized in that: in step S6: the distance from the midway of high speed to the destination is X, the average fuel consumption percentage of a driver driving per hundred kilometers is B, the positions of charging piles are different, the driving routes are different, the position of the lower high speed is set as a point C, the positions of two different charging piles are respectively set as a point D and a point E, the position of the destination is set as a point F, two different routes are set, the two different routes are a route CDF and a route CEF, wherein the distance from the point C to the point D is D1, the distance from the point D to the point F is D2, the distance from the point C to the point E is D3, the distance from the point E to the point F is D4, the remaining fuel quantity percentage of the point C at the lower high speed is set as M, the fuel consumption percentage from the point C to the point D is set as M1, the fuel consumption percentage from the point D to the point F is set as M2, the fuel consumption percentage from the point C to the point E is set as M3, and the fuel consumption percentage from the point E to the, calculate percent fuel consumption from Point C to Point D
Figure FDA0002719856870000032
Calculating the percentage of the remaining oil quantity at the point D to be M-M1, and calculating the percentage of the oil consumption from the point D to the point F
Figure FDA0002719856870000033
Calculating the percentage of oil consumption from point C to point E
Figure FDA0002719856870000034
Calculating the percentage of oil remaining at point E as M-M3, and calculating the oil remaining at points E to FPercentage of oil consumption
Figure FDA0002719856870000035
When d1+ d2 ═ d3+ d4, d1+ d2 > X and d3+ d4 > X, the total fuel consumption percentages of the route CDF and route CEF are the same, if M-M1 > M-M3, the route CDF is preferentially selected, otherwise the route CEF is selected; when d1+ d2 ≠ d3+ d4, d1+ d2 > X and d3+ d4 > X, if M1+ M2>M3+ M4, the route CDF is selected, whereas the route CEF is selected.
10. The big-data-based driving travel departure reminding method according to claim 5, characterized in that: in step S7: after the vehicle reaches the destination, the vacant temporary parking spaces need to be searched, and the optimal path needs to be planned for searching the vacant temporary parking spaces in the destination: setting n temporary parking spaces, setting a parking entrance as a point H, establishing a coordinate system by taking the point H as an original point, and marking out a coordinate set { (x) of the parking spaces1，y1)，(x2，y2)，(x3，y3)...(xn，yn) The database counts the number of empty parking spaces according to the number of the vehicles entering and leaving, and sets the time of the vehicle reaching the destination as t, wherein t is the time between t and 30 and t]In the time period, setting the original vehicle as m1Vehicle, entering vehicle m2M, the outgoing vehicle3And calculating the number m of the remaining empty parking spaces at the moment t as n- (m)2-m3)-m1Marking the coordinates of the remaining empty parking space { (x)Hollow 1，yHollow 1)，(xHollow 2，yHollow 2)，...(xEmpty m，yEmpty m) Seeking the best empty parking space position coordinate (x) according to the distance from the vehicle to each empty parking spaceAir conditioner，yAir conditioner) And setting the distance set of the parking entrance to the empty parking space as { k }1，k2，k3...kzAnd the shortest distance from the parking entrance to the empty parking space
Figure FDA0002719856870000041
If the best empty parking space is occupied when the parking space arrives, searching according to the distanceFinding the spare empty parking space, and setting the coordinate of the spare empty parking space as (x)Prepare for，yPrepare for) The distance from the optimal empty parking space to the alternative empty parking space is
Figure FDA0002719856870000042
And the empty parking space closest to the optimal empty parking space is the alternative empty parking space.
CN202011084380.3A 2020-10-12 2020-10-12 Driving trip departure reminding system and method based on big data Withdrawn CN112289061A (en)

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