CN104002816A - Oil saving method based on geographical environment mining and perceiving of vehicle - Google Patents

Abstract

The present invention provides a fuel-saving method for vehicle geographic environment mining perception, which comprises the following steps: Step 1, merging the position data and driving data of the starting vehicle into a data frame and transmitting it to the Internet of Vehicles center; Step 2, calling and cleaning the stored Data frame to obtain all the constant speed segments of the vehicle; step 3, perform central data fusion on the uniform speed segments, obtain the slope data of the road section and send it to the Internet of Vehicles center; step 4, the Internet of Vehicles center downloads the slope data according to the position transmitted by the driving vehicle Send to the driving vehicle; step 5, the driving vehicle uses the returned slope data to calculate the distance D between the satellite positioning coordinates of the driving vehicle and the starting position of the slope E ahead, and make a decision based on the slope E and the distance D. The invention has the advantages that not only accurate slope data can be obtained, but also there is no need to specially carry out a large number of sampling work on the road, and no need to install additional sensors, which greatly reduces the input cost.

Description

A fuel-saving method for vehicle geographic environment mining perception

technical field

The invention relates to a vehicle geographical environment mining perception fuel-saving method.

Background technique

Improving the fuel economy of automobiles is an important direction for the development of modern automobile technology. In addition to factors such as load, shape structure, and transmission efficiency, the geographical environment also has a major impact on fuel economy. When the geographical environment of the road ahead can be detected in advance, we can start to control the vehicle's proper acceleration or engine turbo pressure at an appropriate position, thereby improving the fuel consumption performance of the vehicle when going overhill. Therefore, how to perceive the geographical environment where the vehicle is driving is a very important issue.

Traditionally, sensors are mainly used for vehicle slope perception, such as inclination sensor (invention patent number: 201210076260.8), acceleration sensor (invention patent number: 200910088125.3 and 201210272414.0) or GPS height measurement. The disadvantage of the traditional method is: when the sensor measures the slope, the car is already on the slope, thus missing the opportunity to adjust the power in advance; if the slope information is measured in advance, and then the road slope information database is constructed, special measurement is required. vehicles, and each measuring vehicle needs to install additional sensor equipment, and each sensor also needs to deal with its own error, which greatly increases the input cost; if the GPS altimetry method is used, it is easy to be affected by satellite signals during the measurement process Impacts, especially in tunnel situations, tend to be completely unusable.

Contents of the invention

The technical problem to be solved by the present invention is to provide a vehicle geographic environment mining perception fuel-saving method, by collecting and fusing the location data and driving data of the starting vehicle, and transmitting the fused data frame to the Internet of Vehicles center, and then The data frames of all vehicles are cleaned and centrally fused, and the obtained slope data is stored in the Internet of Vehicles center, and the driving vehicles make vehicle decisions based on the slope data issued by the Internet of Vehicles center, so as to achieve fuel-saving effects.

The present invention relates to a vehicle geographic environment mining perception fuel-saving method, comprising the following steps:

Step 1. After the vehicle is started, it starts to collect the satellite positioning coordinates of the vehicle, and matches the satellite positioning coordinates with the road map to obtain the position data of the vehicle, and collects the driving data of the vehicle at the same time, and then compares the driving data of the vehicle with the position The data is fused into a data frame and uploaded to the car networking center;

Step 2. Call the data frames of all vehicles stored in the Internet of Vehicles Center, and perform data cleaning on the data frames of all vehicles to obtain all constant-speed segments;

Step 3. Perform central data fusion on all the above constant speed segments to obtain the slope data of the entire road section, and upload the slope data to the Internet of Vehicles center;

Step 4. The Internet of Vehicles Center sends the forward slope data to the driving vehicle according to the data frame or satellite positioning coordinates transmitted by the driving vehicle;

Step 5. The driving vehicle calculates the distance D between the satellite positioning coordinates of the driving vehicle and the starting position of the forward slope E through the slope data returned by the Internet of Vehicles Center, and makes a decision based on the slope E and the distance D.

Further, the step 1 specifically includes the following steps:

Step 11. After the vehicle is started, the satellite positioning coordinates of the vehicle are collected according to the sampling time interval ΔT; if the satellite positioning is in an effective state, record the sampling time t _s and the vehicle’s satellite positioning coordinates p _s , then go to step 12; if the satellite positioning is in failure state, go to step 13;

Step 12. Using the minimum vertical projection distance method combined with the vehicle's driving direction, map and match the vehicle's satellite positioning coordinates p _s with the road to obtain the vehicle's position data, and compare the matching position p _s ' in the vehicle position data with The sampling time t _s is recorded in the effective location cache; at the same time, the driving data of the vehicle is collected, and then the driving data and position data of the vehicle are formed into a data frame, and the data frame is sent to the Internet of Vehicles center through wireless communication, and then enters the step 14;

Step 13. When there is a record in the above-mentioned effective position buffer, take out the matching position p _s ' of the previous sampling time t _s stored by the vehicle from the effective position buffer, and start from the matching position p _s ', pass the vehicle Calculate the location data of the vehicle from the odometer data and the road map, and at the same time form a data frame with the vehicle location data and the collected driving data, and send the data frame to the Internet of Vehicles center through wireless communication, and then enter step 14; When there is no record in the above-mentioned effective location cache, just go directly to step 14;

Step 14. If the vehicle stops running, clear its effective location cache; if the vehicle continues to drive, return to step 11 for loop execution.

Further, the step 2 specifically includes the following steps:

Step 21, take out all data frames of one of the vehicles from the vehicle data storage table of the Internet of Vehicles center;

Step 22. According to the characteristics of the time interval Δt between adjacent sampling moments, all the continuous data frames of the vehicle from start to stop are separated segment by segment, and each segment of continuous data frames is arranged in the order of sampling time t _s ;

Step 23. Take out a continuous data frame of the above vehicle, convert the speed v _s of the continuous data frame at each sampling time t _s through the engine speed n and the vehicle gear d, and calculate the speed of each adjacent time Carry out derivation to obtain the acceleration a _s ; according to the selected threshold ε, judge whether a _s is smaller than the threshold ε, if so, the vehicle is driving at a constant speed at the sampling time t _s , and the data frame marking the sampling time t _s is 1; if Otherwise, the vehicle is traveling at a non-uniform speed at the sampling time t _s , and the data frame marked at the sampling time t _s is 0, and the value of a _s is continuously compared until the continuous data frame of this segment is marked;

Step 24, according to the order of the sampling time t _s , scan the marked continuous data frames, take out all the data frames marked as 1 consecutively, and store them in the constant speed segment buffer as a constant speed segment;

Step 25. If all the continuous data frames of the vehicle have not been processed, return to step 23 for loop execution; if all the continuous data frames of the vehicle have been processed, then enter step 26;

Step 26. If the data frames of all vehicles in the vehicle data storage table have been executed, proceed to step 3; if the data frames of all vehicles in the vehicle data storage table have not been executed, return to step 21 for loop execution.

Further, the step 3 specifically includes the following steps:

Step 31. Take a known constant-velocity segment a of the load-carrying vehicle A from the constant-velocity segment cache as the initial segment. In this initial segment, the position data of the vehicle is {p _a1 ,p _a2 ,...p _an ,p _d1 ,p _d2 ,…p _dk }, and calculate the power P of each point from the engine torque T and the engine speed n; suppose the power variation between the adjacent position data p _a2 and p _a1 is ΔP _a2,a1 , then the adjacent position of the constant velocity segment The power change sequence is:

$<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; an</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; an</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; ;</span>$

Step 32. Use the initial segment a as the current reference segment, and according to its position data, find out the constant-velocity segment b that has position data overlapping with the constant-velocity segment a from the constant-velocity segment cache, and set the constant-velocity segment b to belong to vehicle B, The position data is {p _d1 ,p _d2 ,...p _dk ,p _b1 ,p _b2 ,...p _bm }, and the power P of each point is calculated from the engine torque T and the engine speed n, then the power change sequence of the adjacent position of the constant velocity segment for:

$<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; bm</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; bm</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; ;</span>$

_{Step 33.} Calculate the _{normalization} coefficient {C _d1 , C _d2 ,...C _dk-1 };

Step 34, averaging the obtained normalization coefficient values C _d1 , C _d2 , ... C _dk-1 to obtain the overall normalization coefficient C;

Step 35. Using the overall normalization coefficient C, the power change sequence of car B driving on the constant speed segment b with load G _B and constant speed uB $\{{\mathrm{\&Delta;P}}_{d2,d1}^b,{\mathrm{\&Delta;P}}_{d3,d2}^b,...,{\mathrm{\&Delta;P}}_{\mathrm{dk},\mathrm{dk}-1}^b,{\mathrm{\&Delta;P}}_{b2,b1},{\mathrm{\&Delta;P}}_{b3,b2},...,{\mathrm{\&Delta;P}}_{\mathrm{bm},\mathrm{bm}-1}\}$ Normalized to the power change sequence of car A driving on the constant speed segment b with load G _A and constant speed u _A , the normalized power change sequence is:

$<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; bm</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; bm</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; ;</span>$

Step 36. Carry out splicing and fusion of the power change sequence of car A running on constant speed segment a and constant speed segment b with load G _A and constant speed u _A , and the power change sequence after splicing and fusion is obtained:

{ΔP _a2,a1 ,ΔP _a3,a2 ,…,ΔP _an,an-1 ,

$<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>$

CΔP _b2,b1 ,CΔP _b3,b2 ,…,CΔP _bm,bm-1 }

;

Step 37. If all the constant-velocity segments overlapping with the constant-velocity segment a have not been found yet, use the spliced constant-velocity segment as the current reference segment, and return to step 32 for loop execution; All the constant-velocity segments with overlapping data, and splicing all the constant-velocity segments to obtain the power change sequence of the entire road section, then enter step 38;

Step 38, using the gradient variation Transform the power change sequence of the entire road section into a slope change sequence, and transmit the obtained slope data to the Internet of Vehicles Center, where u is the speed, G is the quality, ΔP is the power change, and η _T is the mechanical transmission efficiency.

Further, the step 4 specifically includes the following steps:

Step 41, the Internet of Vehicles Center stores the transmitted slope data into the slope data storage table;

Step 42, the driving vehicle uploads the satellite positioning coordinates and driving direction to the Internet of Vehicles center;

Step 43. The Internet of Vehicles Center receives the data frame or satellite positioning coordinates and driving direction from the driving vehicle. If it receives the data frame of the driving vehicle, it takes out the matching position p _s ' and road section ID r _s in the data frame. Then enter step 44; if the satellite positioning coordinates and driving direction of the driving vehicle are received, the satellite positioning coordinates of the driving vehicle and the road are mapped and matched by using the minimum vertical projection distance method in combination with the driving direction to obtain the driving vehicle The matching position p _s " and road segment IDr _s ', then enter step 44;

Step 44. According to the matching position p _s ′ and the road segment ID _rs or the matching position p _s ” and the road segment ID _rs ′, send the forward slope data to the driving vehicle.

Further, the position data includes matching position p _s ′ and road section ID _rs ; the driving data includes engine speed n, engine torque T and vehicle gear d.

Further, the data frame is {n, T, d, p _s ', t _s , r _s }.

Further, the time interval Δt between adjacent sampling moments is characterized by: if in the same continuous data frame, the time interval Δt is equal to the sampling time interval ΔT; if in two consecutive data frames , then the time interval Δt is greater than the sampling time interval ΔT.

Further, the specific calculation process of step 33 is as follows: first calculate the engine output power P _ad2 and P _ad1 of the car A at the overlapping position data p _d2 and p _d1 respectively from the engine torque T and the engine speed n; then according to the slope i Relationship with vehicle power P Get the actual slope change encountered by car A between the position data p _d2 and p _d1 The actual slope change encountered by car B Among them, u is the speed, G is the mass, and ηT is the mechanical transmission efficiency; then according to Δi _B = Δi _A , get make Then the normalization coefficient of the product of vehicle B speed u _B and mass G _B relative to the product of A vehicle speed u _A and mass G _A Continue to select overlapping position data for calculation until all normalization coefficients C _d2 , C _d3 , . . . C _dk-1 of the overlapping position data are obtained, and then enter step 34 .

The invention has the following advantages: matching the satellite positioning coordinates and cleaning the fused data frames, so that the obtained slope data is more accurate and highly reliable; using the existing Internet of Vehicles to operate on the network, the collection of information is automatically completed Function, it does not need to invest a large number of vehicles to complete the road sampling work, and does not need to install additional sensors, so that the cost is reduced; the processed slope data is sequentially stored in a special slope data storage table, making the search convenient and fast.

Description of drawings

The present invention will be further described below in conjunction with the embodiments with reference to the accompanying drawings.

Fig. 1 is a block flow diagram of a vehicle geographical environment mining perception fuel-saving method according to the present invention.

Detailed ways

Please refer to Fig. 1, a method for mining and perceiving fuel saving in vehicle geographic environment, including the following steps:

Step 1. After the vehicle is started, it starts to collect the satellite positioning coordinates of the vehicle, and matches the satellite positioning coordinates with the road map to obtain the position data of the vehicle, and collects the driving data of the vehicle at the same time, and then compares the driving data of the vehicle with the position The data is fused into a data frame and uploaded to the Internet of Vehicles Center; the specific steps are as follows:

Step 11. After the vehicle is started, it starts to collect the satellite positioning coordinates of the vehicle according to the sampling time interval △T. Usually, the smaller the sampling time interval is, the more accurate the final slope data will be. Therefore, we generally take the sampled The time interval △T is 100ms; if the satellite positioning is in the effective state, record the sampling time t _s and the satellite positioning coordinates p _s of the vehicle, and then enter step 12; if the satellite positioning is in the invalid state (such as when the vehicle enters the tunnel) , go to step 13;

Step 12. Using the minimum vertical projection distance method and combining the vehicle's driving direction, map and match the vehicle's satellite positioning coordinates p _s with the road to obtain the vehicle's position data (including the matching position p _s ' and road section ID r _s ), And record the matching position p _s ' and the sampling time t _s into the effective position cache, which is used when the satellite positioning is in an invalid state; at the same time, the driving data of the vehicle (including engine speed n, engine torque T and vehicle gear d), and the vehicle's position data and driving data form a data frame {n,T,d,p _s ',t _s , _rs }, and send the data frame to the Internet of Vehicles through wireless communication Center, then go to step 14;

Step 13. When there is a record in the above-mentioned effective position buffer, take out the matching position p _s ' of the previous sampling time t _s stored by the vehicle from the effective position buffer, and start from the matching position p _s ', pass the vehicle Calculate the location data of the vehicle from the odometer data and the road map, and at the same time form a data frame with the vehicle location data and the collected driving data, and send the data frame to the Internet of Vehicles center through wireless communication, and then enter step 14; When there is no record in the above-mentioned effective position cache, it means that the satellite positioning has been in an invalid state, and at this time directly enter step 14;

Step 14. If the vehicle stops running, clear its effective location cache for use when the vehicle is started next time; if the vehicle continues to drive, return to step 11 for loop execution.

Step 2. Invoke the data frames of all vehicles stored in the Internet of Vehicles center, and perform data cleaning on the data frames of all vehicles to obtain all constant-velocity segments; the specific steps are as follows:

Step 21, take out all the data frames of one of the vehicles from the vehicle data storage table of the Internet of Vehicles center;

Step 22, according to the characteristics of the time interval Δt between adjacent sampling moments (if in the same continuous data frame, the time interval Δt is 100ms; if in two consecutive data frames, the time interval Δt greater than 100ms), separate all the continuous data frames of the vehicle from start to stop process segment by segment, and arrange each segment of continuous data frames in the order of sampling time t _s ;

Step 23. Take out a continuous data frame of the above vehicle, convert the speed v _s of the continuous data frame at each sampling time t _s through the engine speed n and the vehicle gear d, and calculate the speed of each adjacent time Carry out derivation to obtain the acceleration a _s ; according to the selected threshold ε (ε approaches 0), judge whether a _s is less than the threshold ε, if so, the vehicle is driving at a constant speed at the sampling time t _s , and mark the sampling time t _s The data frame of a is 1; otherwise, the vehicle is traveling at a non-uniform speed at the sampling time t _s , mark the data frame of the sampling time t _s as 0, and continue to compare the value of a _s until the continuous data frame of this segment is marked;

Step 24, according to the order of the sampling time t _s , scan the marked continuous data frames, take out all the data frames marked as 1 consecutively, and store them in the constant speed segment buffer as a constant speed segment;

Step 25. If all the continuous data frames of the vehicle have not been processed, return to step 23 for loop execution; if all the continuous data frames of the vehicle have been processed, then enter step 26;

Step 26. If the data frames of all vehicles in the vehicle data storage table have been executed, proceed to step 3; if the data frames of all vehicles in the vehicle data storage table have not been executed, return to step 21 for loop execution.

After cleaning and filtering the above data, the retained data frames are segments of vehicles traveling at approximately constant speeds. The purpose of this is to eliminate the influence of acceleration on power changes, and the speed and mass of vehicles remain unchanged on the constant speed segments. Therefore, the power consumed by road frictional resistance is equal, and the power consumed by wind resistance is also approximately equal. In this way, the influencing factors of vehicle power change mainly come from slope data.

Step 3. Perform central data fusion on all the above-mentioned constant speed segments to obtain the slope data of the entire road section, and upload the slope data to the Internet of Vehicles center; the specific steps are as follows:

Step 31. Take a known constant-velocity segment a of the load-carrying vehicle A from the constant-velocity segment cache as the initial segment. In this initial segment, the position data of the vehicle is {p _a1 ,p _a2 ,...p _an ,p _d1 ,p _d2 ,…p _dk }, and calculate the power P of each point from the engine torque T and the engine speed n; suppose the power variation between the adjacent position data p _a2 and p _a1 is ΔP _a2,a1 , then the adjacent position of the constant velocity segment The power change sequence is:

$<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; an</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; an</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; ;</span>$

Step 32. Use the initial segment a as the current reference segment, and according to its position data, find out the constant-velocity segment b that has position data overlapping with the constant-velocity segment a from the constant-velocity segment cache, and set the constant-velocity segment b to belong to vehicle B, The position data is {p _d1 ,p _d2 ,...p _dk ,p _b1 ,p _b2 ,...p _bm }, and the power P of each point is calculated from the engine torque T and the engine speed n, then the power change sequence of the adjacent position of the constant velocity segment for:

$<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; bm</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; bm</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; ;</span>$

_{Step 33.} Calculate the _{normalization} coefficient {C _d1 , C _d2 ,…C _dk-1 }; the specific calculation process is:

First calculate the engine output power P ad2 and P _ad1 of the car A at the overlapping position data p _d2 and p _d1 respectively from the engine torque T and the engine speed n; then according to _the relationship between the slope i and the car power P Get the actual slope change encountered by car A between the position data p _d2 and p _d1 The actual slope change encountered by car B Among them, u is the speed, G is the quality, and η _T is the mechanical transmission efficiency; then according to Δi _B = Δi _A , get (Here, for the convenience of expressing the formula, we assume that vehicle A and vehicle B are the same type of vehicle, then η _T in the numerator and denominator can be reduced. If in practice, the models of vehicle A and vehicle B are different, we only need to put the corresponding η _{The T} value can be substituted into the formula), so that Then the normalization coefficient of the product of vehicle B speed u _B and mass G _B relative to the product of A vehicle speed u _A and mass G _A Continue to select overlapping position data for calculation until all normalization coefficients C _d2 , C _d3 ,...C _dk-1 of the overlapping position data are obtained, and then enter step 34;

Step 34, averaging the obtained normalization coefficient values C _d1 , C _d2 , ... C _dk-1 to obtain the overall normalization coefficient C;

Step 35. Using the overall normalization coefficient C, the power change sequence of car B driving on the constant speed segment b with load G _B and constant speed u _B $\{{\mathrm{\&Delta;P}}_{d2,d1}^b,{\mathrm{\&Delta;P}}_{d3,d2}^b,...,{\mathrm{\&Delta;P}}_{\mathrm{dk},\mathrm{dk}-1}^b,{\mathrm{\&Delta;P}}_{b2,b1},{\mathrm{\&Delta;P}}_{b3,b2},...,{\mathrm{\&Delta;P}}_{\mathrm{bm},\mathrm{bm}-1}\}$ Normalized to the power change sequence of car A driving on the constant speed segment b with load G _A and constant speed u _A , the normalized power change sequence is:

$<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; bm</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; bm</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; ;</span>$

Step 36. Carry out splicing and fusion of the power change sequence of car A running on constant speed segment a and constant speed segment b with load G _A and constant speed u _A , and the power change sequence after splicing and fusion is obtained:

{ΔP _a2,a1 ,ΔP _a3,a2 ,…,ΔP _an,an-1 ,

$<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C\&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; dk</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>$

CΔP _b2,b1 ,CΔP _b3,b2 ,…,CΔP _bm,bm-1 }

;

Step 37. If all the constant-velocity segments overlapping with the constant-velocity segment a have not been found yet, use the spliced constant-velocity segment as the current reference segment, and return to step 32 for loop execution; if a position with the constant-velocity segment a has been found All the constant-velocity segments with overlapping data, and splicing all the constant-velocity segments to obtain the power change sequence of the entire road section, then enter step 38;

Step 38, using the gradient variation Transform the power change sequence of the entire road section into a slope change sequence, and transmit the obtained slope data to the Internet of Vehicles Center, where u is the speed, G is the quality, ΔP is the power change, and η _T is the mechanical transmission efficiency.

In the process of central data fusion, we only need to know the load of vehicle A as the initial reference segment, and then we can obtain the slope data of the entire road section by performing central data fusion on the uniform speed segment without knowing the load data of other vehicles. This is easy to implement in the actual car networking business.

Step 4. The IoV center sends the forward slope data to the driving vehicle according to the data frame or satellite positioning coordinates transmitted by the driving vehicle; the specific steps are as follows:

Step 41, the Internet of Vehicles Center stores the transmitted slope data into the slope data storage table;

Step 42, the driving vehicle uploads the satellite positioning coordinates and driving direction to the Internet of Vehicles center;

Step 43. The Internet of Vehicles Center receives the data frame or satellite positioning coordinates and driving direction from the driving vehicle. If it receives the data frame of the driving vehicle, it takes out the matching position p _s ' and road section ID r _s in the data frame. Then enter step 44; if the satellite positioning coordinates and driving direction of the driving vehicle are received, the satellite positioning coordinates of the driving vehicle and the road are mapped and matched by using the minimum vertical projection distance method in combination with the driving direction to obtain the driving vehicle The matching position p _s " and road segment IDr _s ', then enter step 44;

Step 44. According to the matching position p _s ′ and the road segment ID _rs or the matching position p _s ” and the road segment ID _rs ′, send the forward slope data to the driving vehicle.

Step 5. The driving vehicle calculates the distance D between the satellite positioning coordinates of the driving vehicle and the initial position of the slope E ahead through the slope data returned by the Internet of Vehicles Center, and makes a decision based on the slope E and the distance D, so that the vehicle can reach a relatively high level. Good fuel saving effect.

Although the specific embodiments of the present invention have been described above, those skilled in the art should understand that the specific embodiments we have described are only illustrative, rather than used to limit the scope of the present invention. Equivalent modifications and changes made by skilled personnel in accordance with the spirit of the present invention shall fall within the protection scope of the claims of the present invention.

Claims (9)

1. vehicle geographical environment excavates a perception fuel saving method, it is characterized in that: described method comprises the steps:

After step 1, vehicle launch, just start to gather the satellite positioning coordinate of this vehicle, and satellite positioning coordinate is mated to the position data that obtains this vehicle with road mapping, gather the running data of this vehicle simultaneously, afterwards the running data of this vehicle and position data are fused into Frame, and are uploaded to car cluster center;

Step 2, call the Frame of all vehicles that are stored in car cluster center, and the Frame of all vehicles is carried out to data cleansing, obtain all at the uniform velocity fragments;

Step 3, above-mentioned all at the uniform velocity fragments are carried out to centre data fusion, obtain the Gradient in whole section, and Gradient is uploaded to car cluster center;

Frame or satellite positioning coordinate that step 4, car cluster center transmit according to driving vehicle, be handed down to this driving vehicle by the Gradient in front;

The Gradient that step 5, driving vehicle return by car cluster center, calculates the distance B between satellite positioning coordinate and the front gradient E reference position of this driving vehicle, and carries out decision-making according to gradient E and distance B.

2. a kind of vehicle geographical environment as claimed in claim 1 excavates cognitive method, it is characterized in that: described step 1 specifically comprises the steps:

Step 11, when starting after vehicle, just, according to the time gap △ T of sampling, the satellite positioning coordinate of this vehicle is gathered; If satellite positioning, in significant condition, is write down sampling instant t _sand the satellite positioning coordinate p of this vehicle _s, enter afterwards step 12; If satellite positioning, in failure state, enters step 13;

Step 12, utilize minimum vertical projector distance method in conjunction with vehicle heading, by the satellite positioning coordinate p of this vehicle _sshine upon and mate with road, obtain the position data of this vehicle, and by the matched position p in this vehicle position data _s' and sampling instant t _sbe recorded in active position buffer memory; Gather the running data of this vehicle simultaneously, then the running data of this vehicle and position data are formed to a Frame, and by radio communication, Frame is sent to car cluster center, enter afterwards step 14;

Step 13, in the time recording in above-mentioned active position buffer memory, just from active position buffer memory, take out the last sampling instant t of this vehicle storage _smatched position p _s', and with matched position p _s' be starting point, mileage meter data by this vehicle and road-map calculate the position data of this vehicle, the running data of the position data of this vehicle and collection is formed to a Frame simultaneously, and by radio communication, Frame is sent to car cluster center, enter afterwards step 14; In the time not recording in above-mentioned active position buffer memory, just directly enter step 14;

If this vehicle stop of step 14 travels, empty its active position buffer memory; If this vehicle continues to travel, return to step 11 circulation and carry out.

3. a kind of vehicle geographical environment as claimed in claim 1 or 2 excavates perception fuel saving method, it is characterized in that: described position data comprises matched position p _s' and road section ID r _s; Described running data comprises engine speed n, engine torque T and automobile gear level d.

4. a kind of vehicle geographical environment as claimed in claim 2 excavates perception fuel saving method, it is characterized in that: described Frame is { n, T, d, p _s', t _s, r _s.

5. a kind of vehicle geographical environment as claimed in claim 1 excavates perception fuel saving method, it is characterized in that: described step 2 specifically comprises the steps:

Step 21, from the vehicle data storage list of car cluster center, take out all Frames of a car wherein;

Step 22, according to the feature of the time gap △ t between the neighbouring sample moment, separate piecemeal from the continuous data frame that starts to stopped process all this vehicle, and by each section of continuous data frame by sampling instant t _sorder is arranged;

Step 23, take out one section of continuous data frame of above-mentioned vehicle, by engine speed n and automobile gear level d, converse this section of continuous data frame at each sampling instant t _sspeed v _s, and the speed of each adjacent moment is carried out to differentiate, obtain acceleration/accel a _s; According to the threshold epsilon of choosing, judge a _swhether be less than threshold epsilon, if this vehicle is at sampling instant t _slocate as at the uniform velocity travelling this sampling instant of mark t _sframe be 1; If not this vehicle at sampling instant t _sat the uniform velocity travel for non-in place, this sampling instant of mark t _sframe be 0, continue relatively a _svalue, until complete this section of continuous data frame of mark;

Step 24, by sampling instant t _sthe order of arranging, the continuous data frame that passing marker is complete, all takes out the Frame that wherein continued labelling is 1, and is deposited at the uniform velocity in fragment buffer as fragment at the uniform velocity;

If all continuous data frames of untreated complete this vehicle of step 25, return to step 23 circulation and carry out; If all continuous data frames of processed this vehicle, enter step 26;

If the Frame of all vehicles, enters step 3 in the complete vehicle data storage list of step 26 executed; If do not execute the Frame of all vehicles in vehicle data storage list, return to step 21 circulation and carry out.

6. a kind of vehicle geographical environment as claimed in claim 5 excavates perception fuel saving method, it is characterized in that: the time gap △ t between the described neighbouring sample moment is characterized as: if in same continuous data frame, this time gap △ t equals the time gap △ T of sampling; If in two sections of continuous data frames, this time gap △ t is greater than the time gap △ T of sampling.

7. a kind of vehicle geographical environment as claimed in claim 1 excavates perception fuel saving method, it is characterized in that: described step 3 specifically comprises the steps:

Step 31, from fragment buffer at the uniform velocity, get a known load-carrying vehicle A at the uniform velocity fragment a as initial segment, establish in this initial segment, the position data of vehicle is { p _a1, p _a2... p _an, p _d1, p _d2... p _dk, and calculate each point power P by engine torque T and engine speed n; If adjacent position data p _a2with p _a1power variation be Δ P _{a2, a1}, this at the uniform velocity the power change sequence of fragment adjacent position be:

$\{{\mathrm{\&Delta;P}}_{a2,a1},{\mathrm{\&Delta;P}}_{a3,a2},...,{\mathrm{\&Delta;P}}_{\mathrm{an},\mathrm{an}-1},{\mathrm{\&Delta;P}}_{d2,d1}^a,{\mathrm{\&Delta;P}}_{d3,d2}^a,...,{\mathrm{\&Delta;P}}_{\mathrm{dk},\mathrm{dk}-1}^a\};$

Step 32, by initial segment a as current with reference to fragment, and according to its position data, from fragment buffer at the uniform velocity, find out with this at the uniform velocity fragment a have the overlapping at the uniform velocity fragment b of position data, and establish this at the uniform velocity fragment b belong to vehicle B, position data is { p _d1, p _d2... p _dk, p _b1, p _b2... p _bm, calculate each point power P by engine torque T and engine speed n, this at the uniform velocity the power change sequence of fragment adjacent position be:

$\{{\mathrm{\&Delta;P}}_{d2,d1}^b,{\mathrm{\&Delta;P}}_{d3,d2}^b,...,{\mathrm{\&Delta;P}}_{\mathrm{dk},\mathrm{dk}-1}^b,{\mathrm{\&Delta;P}}_{b2,b1},{\mathrm{\&Delta;P}}_{b3,b2},...,{\mathrm{\&Delta;P}}_{\mathrm{bm},\mathrm{bm}-1}\};$

Step 33, at overlapping position data { p _d1, p _d2... p _dk-1locate, calculate B vehicle speed and the quality product feature normalization coefficient { C with respect to A vehicle speed and quality product feature _d1, C _d2... C _dk-1;

Step 34, by the value C of the normalization coefficient obtaining _d1, C _d2... C _dk-1be averaging, obtain overall normalization coefficient C;

Step 35, utilize overall normalization coefficient C, by B car on fragment b at the uniform velocity with load-carrying G _band u at the uniform velocity _bthe power change sequence travelling $\{{\mathrm{\&Delta;P}}_{d2,d1}^b,{\mathrm{\&Delta;P}}_{d3,d2}^b,...,{\mathrm{\&Delta;P}}_{\mathrm{dk},\mathrm{dk}-1}^b,{\mathrm{\&Delta;P}}_{b2,b1},{\mathrm{\&Delta;P}}_{b3,b2},...,{\mathrm{\&Delta;P}}_{\mathrm{bm},\mathrm{bm}-1}\}$ Be normalized to A car on fragment b at the uniform velocity with load-carrying G _aand u at the uniform velocity _athe power change sequence travelling, the power change sequence after normalization method is:

$\{{\mathrm{C\&Delta;P}}_{d2,d1}^b,{\mathrm{C\&Delta;P}}_{d3,d2}^b,...,{\mathrm{C\&Delta;P}}_{\mathrm{dk},\mathrm{dk}-1}^b,{\mathrm{C\&Delta;P}}_{b2,b1},{\mathrm{C\&Delta;P}}_{b3,b2},...,{\mathrm{C\&Delta;P}}_{\mathrm{bm},\mathrm{bm}-1}\};$

Step 36, by A car on fragment a at the uniform velocity and fragment b at the uniform velocity with load-carrying G _aand u at the uniform velocity _athe power change sequence travelling splices fusion, and the power change sequence obtaining after splicing is merged is:

{ΔP _a2,a1,ΔP _a3,a2,…,ΔP _an,an-1,

$({\mathrm{\&Delta;P}}_{d2,d1}^a+{\mathrm{C\&Delta;P}}_{d2,d1}^b)/2,({\mathrm{\&Delta;P}}_{d3,d2}^a+{\mathrm{C\&Delta;P}}_{d3,d2}^b)/2,...,({\mathrm{\&Delta;P}}_{\mathrm{dk},\mathrm{dk}-1}^a+{\mathrm{C\&Delta;P}}_{\mathrm{dk},\mathrm{dk}-1}^b)/2,$

CΔP _b2,b1,CΔP _b3,b2,…,CΔP _bm,bm-1}

；

Have the overlapping all at the uniform velocity fragments of position data if step 37 is not also found out with fragment a at the uniform velocity, using the at the uniform velocity fragment of having spliced as current with reference to fragment, and return to step 32 circulation and carry out; There are the overlapping all at the uniform velocity fragments of position data if found out with fragment a at the uniform velocity, and all at the uniform velocity fragment assemblies obtained to the power change sequence in whole piece section, enter step 38;

Step 38, utilize slope change amount change the power change sequence in whole piece section into slope change sequence, and send the Gradient obtaining to car cluster center, wherein u is speed, and G is quality, and Δ P is power variation, η _tfor machinery driving efficiency.

8. a kind of vehicle geographical environment as claimed in claim 7 excavates perception fuel saving method, it is characterized in that: the concrete computation process of described step 33 is: first by engine torque T and engine speed n, calculate respectively lap position data p _d2, p _d1the engine output P of the A of place car _ad2and P _ad1; Again according to the relation of gradient i and power of vehicle P obtain at position data p _d2with p _d1between, the actual grade that A car runs into changes the actual grade that B car runs into changes wherein u is speed, and G is quality, η _tfor machinery driving efficiency; Then according to Δ i _b=Δ i _a, obtain order b vehicle speed u _bwith quality G _bproduct with respect to A vehicle speed u _awith quality G _athe normalization coefficient of product continue to choose lap position data and calculate, until obtain all normalization coefficient C of these lap position data _d2, C _d3... C _dk-1, enter afterwards step 34.

9. a kind of vehicle geographical environment as claimed in claim 1 excavates perception fuel saving method, it is characterized in that: described step 4 specifically comprises the steps:

Step 41, car cluster center deposit the Gradient transmitting in Gradient storage list in;

Satellite positioning coordinate and travel direction are uploaded to car cluster center by step 42, driving vehicle;

Step 43, car cluster center receive Frame or satellite positioning coordinate and the travel direction that driving vehicle transmits, if reception is the Frame of this driving vehicle, take out the matched position p in Frame _s' and road section ID r _s, enter afterwards step 44; If what receive is satellite positioning coordinate and the travel direction of this driving vehicle, utilize minimum vertical projector distance method and in conjunction with travel direction, the satellite positioning coordinate of this driving vehicle shone upon and mated with road, obtain the matched position p of this driving vehicle _s" and road section ID r _s', enter afterwards step 44;

Step 44, according to matched position p _s' and road section ID r _sor matched position p _s" and road section ID r _s', the Gradient in front is handed down to this driving vehicle.