CN108346299B - Vehicle speed evaluation method and device - Google Patents
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Abstract
The invention discloses a vehicle speed evaluation method and device. The vehicle speed evaluation method includes: acquiring vehicle travel data including vehicle speed; searching speed map data corresponding to the vehicle travel track from the generated speed map according to the vehicle travel data; and evaluating the vehicle speed according to the comparison result of the acquired vehicle travel data and the searched speed map data. The scheme provided by the invention can reflect the speed condition of the vehicle more accurately.
Description
Technical Field
The invention relates to the technical field of vehicle networking, in particular to a vehicle speed evaluation method and device.
Background
With the development of the internet of things, the analysis and application of the big data of the internet of vehicles are more and more important for the driving of the vehicles. General vehicle speed evaluation is evaluated based on the absolute speed of a vehicle and the speed limit of a road, and the evaluation is not detailed enough and is not easy to monitor. The traffic speed on the road is often strongly time dependent, and the difference between the road speed limit and the actual traffic speed varies. Further, the vehicle speed is not only related to the road but also has a large relationship with the traveling speed of the remaining vehicles on the road.
Therefore, the prior related art uses the absolute speed to evaluate the vehicle speed, and has a great disadvantage.
Disclosure of Invention
In view of the above, the present invention provides a method and a device for estimating a vehicle speed, which can reflect the vehicle speed more accurately.
According to an aspect of the present invention, there is provided a vehicle stationarity evaluating method including:
acquiring vehicle travel data including vehicle speed;
searching speed map data corresponding to the vehicle travel track from the generated speed map according to the vehicle travel data;
and evaluating the vehicle speed according to the comparison result of the acquired vehicle travel data and the searched speed map data.
Preferably, the estimating the vehicle speed according to the comparison result of the acquired vehicle travel data and the searched speed map data includes:
determining whether the vehicle speed is relatively fast or relatively slow according to a position where the vehicle speed is located in a historical speed distribution of the speed map data; and/or the presence of a gas in the gas,
determining an estimated speed score based on the vehicle speed and a historical speed of the speed map data, and estimating the vehicle speed based on the determined estimated speed score.
Preferably, the speed map is generated in advance, and the generation process of the speed map includes:
determining 6-level and 7-level geohash codes corresponding to each longitude and latitude data in the vehicle travel data;
grouping all data contained in the vehicle travel data according to time, and grouping the vehicle speed according to a set value;
counting the speed distribution of the vehicle speed according to the 7-level and 6-level geohash codes respectively;
splicing the speed distribution of the 7-level geohash coding region and the speed distribution of the 6-level geohash coding region corresponding to the speed distribution according to 6-level geohash coding and time;
accumulating the spliced data and the historical data to obtain updated speed map data;
and after the speed map data is subjected to inverse coding processing to obtain city information, the city information is stored again according to the city partitions.
Preferably, the determining that the vehicle speed is relatively fast or relatively slow according to the position where the vehicle speed is located in the historical speed distribution of the speed map data includes:
judging the city according to the longitude and latitude data in the vehicle travel data;
determining 7-level geohash codes corresponding to the longitudes and latitudes, and determining eight 7-level geohash codes adjacent to each 7-level geohash code;
according to the city and the 7-level geohash code, inquiring data of the corresponding time and the 7-level geohash code area from the map data;
and determining the position of the vehicle speed in the interval distribution according to the interval distribution of the speed in the 7-level geohash coding region, and determining that the vehicle speed is relatively fast, ordinary or relatively slow according to the position.
Preferably, the determining an estimated speed score based on the vehicle speed and a historical speed of the speed map data comprises:
judging the city according to the longitude and latitude data in the vehicle travel data;
determining 7-level geohash codes corresponding to the longitudes and latitudes, and determining eight 7-level geohash codes adjacent to each 7-level geohash code;
according to the city and the 7-level geohash code, inquiring data of the corresponding time and the 7-level geohash code area from the map data;
setting a range around the speed of each point in the vehicle travel data as a speed interval of the point;
and respectively determining 7-level geohash coding region speed scores, 7-level adjacent geohash coding region speed scores, 6-level adjacent geohash coding region speed scores and default scores, and performing weighting operation according to the assigned weights to determine evaluation speed scores.
According to another aspect of the present invention, there is provided a vehicle speed evaluation device including:
the system comprises a journey data acquisition module, a data processing module and a data processing module, wherein the journey data acquisition module is used for acquiring vehicle journey data containing vehicle speed;
the map data acquisition module is used for searching speed map data corresponding to the vehicle travel track from the generated speed map according to the vehicle travel data;
and the comparison and evaluation module is used for evaluating the vehicle speed according to the comparison result of the vehicle travel data acquired by the travel data acquisition module and the speed map data searched by the map data acquisition module.
Preferably, the comparison and evaluation module comprises:
a first evaluation sub-module for determining whether the vehicle speed is relatively fast or relatively slow according to a location at which the vehicle speed is located in a historical speed profile of the speed map data; and/or the presence of a gas in the gas,
and the second evaluation sub-module is used for determining an evaluation speed score according to the vehicle speed and the historical speed of the speed map data, and evaluating the vehicle speed according to the determined evaluation speed score.
Preferably, the apparatus further comprises:
the map data generation module is used for determining 6-level and 7-level geohash codes corresponding to each longitude and latitude data in the vehicle travel data; grouping all data contained in the vehicle travel data according to time, and grouping the vehicle speed according to a set value; counting the speed distribution of the vehicle speed according to the 7-level and 6-level geohash codes respectively; splicing the speed distribution of the 7-level geohash coding region and the speed distribution of the 6-level geohash coding region corresponding to the speed distribution according to 6-level geohash coding and time; accumulating the spliced data and the historical data to obtain updated speed map data; and after the speed map data is subjected to inverse coding processing to obtain city information, the city information is stored again according to the city partitions.
Preferably, the first evaluation submodule judges the city according to longitude and latitude data in the vehicle journey data; determining 7-level geohash codes corresponding to the longitudes and latitudes, and determining eight 7-level geohash codes adjacent to each 7-level geohash code; according to the city and the 7-level geohash code, inquiring data of the corresponding time and the 7-level geohash code area from the map data; and determining the position of the vehicle speed in the interval distribution according to the interval distribution of the speed in the 7-level geohash coding region, and determining that the vehicle speed is relatively fast, ordinary or relatively slow according to the position.
Preferably, the second evaluation submodule judges the city according to longitude and latitude data in the vehicle journey data; determining 7-level geohash codes corresponding to the longitudes and latitudes, and determining eight 7-level geohash codes adjacent to each 7-level geohash code; according to the city and the 7-level geohash code, inquiring data of the corresponding time and the 7-level geohash code area from the map data; setting a range around the speed of each point in the vehicle travel data as a speed interval of the point; and respectively determining 7-level geohash coding region speed scores, 7-level adjacent geohash coding region speed scores, 6-level adjacent geohash coding region speed scores and default scores, and performing weighting operation according to the assigned weights to determine evaluation speed scores.
It can be found that, according to the technical solution of the embodiment of the present invention, after the vehicle trip data including the vehicle speed is acquired, the speed map data corresponding to the vehicle trip track can be searched from the generated speed map according to the vehicle trip data, and then the vehicle speed can be evaluated according to the comparison result between the acquired vehicle trip data and the searched speed map data. Because the generated speed map contains historical speed data of a plurality of vehicles on the travel track, and the current vehicle speed is compared with historical speeds of other vehicles relatively, compared with the prior art that absolute speed is used for evaluating the vehicle speed, the scheme of the embodiment of the invention can reflect the current vehicle speed more accurately.
Further, the embodiment of the present invention may determine that the vehicle speed is relatively fast or relatively slow according to the position where the vehicle speed is located in the historical speed distribution of the speed map data; and/or determining an estimated speed score according to the vehicle speed and the historical speed of the speed map data, and estimating the vehicle speed according to the determined estimated speed score, so that the estimation mode is more flexible and comprehensive.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in greater detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
FIG. 1 is a schematic flow chart diagram of a vehicle speed estimation method according to an embodiment of the present invention;
FIG. 2 is another schematic flow chart diagram of a vehicle speed estimation method according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a map data generation process in a vehicle speed evaluation method according to an embodiment of the invention;
FIG. 4 is a schematic flow chart diagram of a speed estimation process in a vehicle speed estimation method according to an embodiment of the invention;
FIG. 5 is a schematic block diagram of a vehicle speed estimation device according to an embodiment of the invention;
FIG. 6 is another schematic block diagram of a vehicle speed estimation apparatus according to an embodiment of the present invention;
fig. 7 is a schematic block diagram of a vehicle speed estimation apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The invention provides a vehicle speed evaluation method which can reflect the vehicle speed more accurately.
The technical solutions of the embodiments of the present invention are described in detail below with reference to the accompanying drawings.
FIG. 1 is a schematic flow diagram of a vehicle speed estimation method according to one embodiment of the invention. The method may be applied to a vehicle speed evaluation device.
Referring to fig. 1, the method includes:
in step 101, vehicle trip data including a vehicle speed is acquired.
The vehicle journey data used in the present invention may include vehicle owner ID, journey number trip _ number, and corresponding second-level GPS (Global Positioning System) data, and the like, where the second-level GPS data may include longitude, latitude, speed, heading angle, and timestamp corresponding to the data, and the like.
In step 102, speed map data corresponding to the vehicle travel track is searched from the generated speed map according to the vehicle travel data.
The invention can generate the speed map based on the vehicle travel data of a large number of vehicle owners in advance, and the step can search the speed map data corresponding to the vehicle travel track from the generated speed map according to the current vehicle travel data.
In step 103, the vehicle speed is evaluated based on the comparison of the acquired vehicle trip data and the looked-up speed map data.
Wherein the vehicle speed may be determined to be relatively fast or relatively slow according to a location at which the vehicle speed is located in a historical speed profile of the speed map data; and/or determining an estimated speed score based on the vehicle speed and a historical speed of the speed map data, and estimating the vehicle speed based on the determined estimated speed score.
It can be found that, according to the technical solution of the embodiment of the present invention, after the vehicle trip data including the vehicle speed is acquired, the speed map data corresponding to the vehicle trip track can be searched from the generated speed map according to the vehicle trip data, and then the vehicle speed can be evaluated according to the comparison result between the acquired vehicle trip data and the searched speed map data. Because the generated speed map contains historical speed data of a plurality of vehicles on the travel track, and the current vehicle speed is compared with historical speeds of other vehicles relatively, compared with the prior art that absolute speed is used for evaluating the vehicle speed, the scheme of the embodiment of the invention can reflect the current vehicle speed more accurately.
FIG. 2 is another schematic flow chart diagram of a vehicle speed estimation method according to an embodiment of the invention. Fig. 2 depicts the inventive arrangement in more detail with respect to fig. 1. The method may be applied to a vehicle speed evaluation device.
Referring to fig. 2, the method includes:
in step 201, a speed map is generated in advance.
This step of pre-generating a speed map may include:
determining 6-level and 7-level geohash codes corresponding to each longitude and latitude data in the vehicle travel data;
grouping related data contained in the vehicle travel data according to time, and grouping the vehicle speed according to a set value;
counting the speed distribution of the vehicle speed according to the 7-level and 6-level geohash codes respectively;
splicing the speed distribution of the 7-level geohash coding region and the speed distribution of the 6-level geohash coding region corresponding to the speed distribution according to 6-level geohash coding and time;
accumulating the spliced data and the historical data to obtain updated speed map data;
and after the speed map data is subjected to inverse coding processing to obtain city information, the city information is stored again according to the city partitions.
The geohash is an address code which can encode two-dimensional longitude and latitude into a one-dimensional character string. The process of obtaining the geohash code includes: converting the longitude and latitude data into special binary data by a dichotomy respectively; placing latitude data at odd bits, placing longitude data at even bits, and splicing binary data; the binary data is converted to a geohash code with base32 encoding. One of the geohash codes corresponds to a rectangular area on the map, and the longer the length of the geohash code is, the smaller the corresponding rectangular area is.
A detailed description of this step can be found in fig. 3.
In step 202, vehicle trip data including vehicle speed is acquired.
In step 203, speed map data corresponding to the vehicle travel route is searched for from the generated speed map based on the vehicle travel data, and the process proceeds to step 204 or step 205.
In step 204, it is determined whether the vehicle speed is relatively fast or relatively slow according to the position where the vehicle speed is located in the historical speed distribution of the speed map data, and step 206 is entered.
The processing procedure of the step can comprise:
judging the city according to the longitude and latitude data in the vehicle travel data;
determining 7-level geohash codes corresponding to the longitudes and latitudes, and determining eight 7-level geohash codes adjacent to each 7-level geohash code;
according to the city and the 7-level geohash code, inquiring data of corresponding time and the 7-level coding region from the map data;
and determining the position of the vehicle speed in the interval distribution according to the interval distribution of the speed in the 7-level coding region, and determining that the vehicle speed is relatively high, normal or relatively low according to the position.
It should be noted that the scheme of the present invention is illustrated by, but not limited to, eight adjacent 7-level geohash codes.
A detailed description of this step can be found in fig. 4.
In step 205, an estimated speed score is determined based on the vehicle speed and the historical speed of the speed map data, the vehicle speed is estimated based on the determined estimated speed score, and step 206 is entered.
The processing procedure of the step can comprise:
judging the city according to the longitude and latitude data in the vehicle travel data;
determining 7-level geohash codes corresponding to the longitudes and latitudes, and determining eight 7-level geohash codes adjacent to each 7-level geohash code;
according to the city and the 7-level geohash code, inquiring data of the corresponding time and the 7-level geohash code area from the map data;
setting a range around the speed of each point in the vehicle travel data as a speed interval of the point;
and respectively determining 7-level geohash coding region speed scores, 7-level adjacent geohash coding region speed scores, 6-level adjacent geohash coding region speed scores and default scores, and performing weighting operation according to the assigned weights to determine evaluation speed scores.
A detailed description of this step can be found in fig. 4.
In step 206, a comprehensive evaluation description is performed.
In this step, the results of step 204 and step 205 may be combined to perform a comprehensive evaluation description.
The step can integrate the speed description and the speed score, and can comprehensively evaluate the vehicle speed of the trip. The speed evaluation of relative speed can be obtained through speed description, the risk evaluation of speed can be obtained through speed scoring, and the higher the score is, the lower the risk is. The evaluation output format may be as shown in table 1 below:
TABLE 1
It should be noted that step 204 or step 205 may not be needed in the above figures.
It can be seen that the embodiment of the present invention may determine that the vehicle speed is relatively fast or relatively slow according to the position where the vehicle speed is located in the historical speed distribution of the speed map data; and/or determining an estimated speed score according to the vehicle speed and the historical speed of the speed map data, and estimating the vehicle speed according to the determined estimated speed score, so that the estimation mode is more flexible and comprehensive.
Fig. 3 is a schematic flowchart of a map data generation process in a vehicle speed estimation method according to an embodiment of the invention.
The vehicle journey data used by the invention, the dimensionality includes the vehicle owner ID, the journey code trip _ number, and the corresponding second-level GPS data, etc., wherein the second-level GPS data may include longitude, latitude, speed, course angle, and timestamp corresponding to the data, etc., as shown in the following data description of table 2:
| name (R) | Description of the invention | Type (B) | Definition of |
| ID | Vehicle owner code | string | |
| trip_number | Journey code | bigint | Using ignition time of stroke as stroke code |
| lon | Longitude (G) | double | 8 bit significant digit |
| lat | Latitude | double | 8 bit significant digit |
| speed | Speed of rotation | double | Unit: km/h |
| head | Course angle | int | |
| time_stamp | Time of day | bigint | Time stamp indicating time of positioning |
TABLE 2
Data volume used by the present invention: the number of car owners is about 10 ten thousand, and the amount of data generated per day is in hundred million.
Data screening of the invention: when the vehicle speed is 0, the vehicle does not run, and it is meaningless to discuss the scoring of the vehicle speed, so all data will be screened for the first time: data with a speed of 0 is deleted.
The position data used by the invention is longitude and latitude data, is two-dimensional continuous data, and has huge data volume and is difficult to store and call if the longitude and latitude data is directly used for generating the speed map, so the map data is recoded by the invention. The invention selects and adopts a geohash code to convert the two-dimensional longitude and latitude into one-dimensional data. The present invention chooses to use, but is not limited to, geohash coding with both 6-level and 7-level precision, taking into account the size of the data storage and the practical requirements. Wherein the maximum error of the level 6 geohash code on the map is about 610 meters, and the maximum error of the level 7 geohash code on the map is about 76 meters.
When the speed map is generated by the invention, the original data can be input once a day, so the basic speed map data can be calculated once a day and accumulated with the previous speed map data. In the generation stage of the speed map data, in order to make the map data more realistic, the data extraction of a single trip is small, and the data used in this case may be taken once every 15 seconds.
The process of generating the speed map according to the invention may comprise:
in step 301, a class 6 and a class 7 geohash code corresponding to each longitude and latitude data in the vehicle travel data is determined.
The vehicle journey data comprises an owner ID, a journey number trip _ number, corresponding second-level GPS data and the like, wherein the second-level GPS data can comprise longitude, latitude, speed, course angle and a timestamp corresponding to the data.
This step may be performed to calculate a geohash code for each latitude and longitude datum in the vehicle trip data, corresponding to the 6 th and 7 th grades.
In step 302, all data contained in the vehicle trip data is grouped by time.
This step may group all data contained in the vehicle trip data by time (1-24 hours).
In step 303, the vehicle speeds are grouped by set values.
The step can group the speeds according to 10km/h, namely, divide the speed interval. It should be noted that the set value is taken as 10km/h as an example but is not limited thereto.
In steps 302 and 303, time and speed intervals are added to facilitate grouping by time and speed.
In step 304, the speed profile of the vehicle speed is counted in a 7-level geohash code.
The step can count the frequency of the vehicle speed in each speed interval in each time period in each 7-level geohash coding region; and simultaneously calculating the 6-level coding region of each 7-level geohash coding region.
In step 305, the speed profile of the vehicle speed is counted in a 6-level geohash code.
The step can count the number of times that the vehicle speed appears in each speed interval in each time period in each 6-level geohash coding region.
In step 306, the velocity profile of the 7-level geohash encoding region and the velocity profile of the corresponding 6-level geohash encoding region are spliced according to the 6-level geohash encoding and time.
This step may concatenate the velocity profile of the level 7 coding region with the velocity profile of its corresponding level 6 coding region according to level 6 geohash coding and time.
In step 307, the stitching data is accumulated with the historical data to obtain updated speed map data.
This step may accumulate the stitching data with the historical data to update the speed map data.
In step 308, the speed map data is inversely encoded to obtain city information, and then is stored again according to the city partition.
The database used in the invention is Hive (a data warehouse based on Hadoop), and the Hive cannot establish efficient index, so that the invention establishes more reasonable and effective partitions for the data in order to facilitate the secondary development of the final data. The invention can store the region again according to the city partition, and each partition corresponds to a continuous region on the map at the moment, so that the invention is more practical and the data is convenient for secondary development. Meanwhile, the basic speed map data used for calling are considered to be updated once a day, and the data change is too frequent, so that the method can adopt a mode of updating once a month for the city partition data.
Wherein the process of step 308 may include:
81) the specific gravity of each speed interval is calculated once a month.
82) And carrying out inverse coding on each 7-level geohash code to obtain corresponding longitude and latitude data.
83) And calculating the city corresponding to the longitude and latitude point (longitude and latitude data).
84) And storing the speed map data again according to the city partitions.
Fig. 4 is a schematic flow chart of a speed evaluation process in a vehicle speed evaluation method according to an embodiment of the invention.
The vehicle journey data can be second-level gps data, and the gps data comprises the owner ID of the journey, the journey code trip _ num, the longitude, the latitude, the speed, the time and the like of the journey per second. In the speed evaluation process, in order to make the evaluation more accurate, the data used at this time may be second-level data.
The basic evaluation method of the invention is that for each point in a vehicle journey, evaluation is made according to the historical speed data of the geohash area where the point is located, and finally the evaluation result of each point can be synthesized to obtain the final evaluation. Wherein, the historical speed data of the geohash area can be obtained by the existing travel statistics. However, there may be some cases where there are more points in the geohash region and some cases where there are fewer points in the geohash region. When there are few points in a geohash region, the historical data in the region does not describe the velocity profile of the region very exactly. In order to solve the problem caused by too little historical data in the geohash region, five scores can be carried out on each point of the vehicle journey, wherein the five scores are respectively as follows: relative speed with respect to the 7-level area where the point is located, relative speed with respect to eight 7-level areas adjacent to the 7-level area where the point is located, relative speed with respect to the 6-level area where the point is located, relative speed with respect to eight 6-level areas adjacent to the 6-level area where the point is located, and relative speed with respect to all points of all areas (i.e., default speed). According to the method, the final score of the point speed can be obtained by integrating five scores according to the historical map speed data volume in each region.
In order to have a comprehensive evaluation on the vehicle speed, the evaluation method provided by the invention comprises two parts: in part, the speed of the stroke is described, and the described criteria can be: at the current time (1-24 hours), in the current 7-level region, the current speed is considered to be slow in the first third of the historical speed distribution, is considered to be fast in the last third, is considered to be normal (medium) in the middle third, and finally, the times described as fast, slow and medium in the whole journey can be counted. Another part is scoring the vehicle speed, and the base criteria for scoring may include: at the current time (1-24 hours), within the current region, the score is higher if more people driving at the current speed interval in the historical data; conversely, the lower the score. For each point in a vehicle trip, five scores may be calculated (see description below), and the velocity scores for each point are combined, and finally the score for each point may also be combined to obtain a score for the vehicle velocity of the trip. The evaluation score of the vehicle speed is the result of comparison with the vehicle speeds of other people, and the more people driving at the current speed, the higher the evaluation score of the vehicle speed is; the speed of the vehicle is also fast or slow relative to others.
The process of describing the speed of the vehicle by the invention can comprise the following steps:
in step 401, the city is judged according to the longitude and latitude data in the vehicle journey data.
The step can judge the city where the travel latitude and longitude are located, duplicate removal is carried out on the obtained city data, and only the data of the city partitions in Hive are inquired during inquiry.
In step 402, 7-level geohash codes corresponding to each longitude and latitude are determined, and eight 7-level geohash codes adjacent to each 7-level geohash code are determined.
This step may calculate 7-level geohash codes corresponding to each longitude and latitude, and then calculate eight adjacent 7-level geohash codes for each 7-level geohash code. It should be noted that eight 7-level geohash codes are taken as an example and are not limited thereto.
One coding region is approximately a rectangle, and eight coding regions are eight adjacent regions of the coding region, and are similar to a nine-square grid. For a point, it may be in the middle of the rectangular coding region or beside the rectangular coding region, and the single coding region alone is not well characterized. So when using a geohash code, typically eight adjacent regions are used. The reason why the 7-level geohash coding uses eight neighboring regions is to make the map data more representative of the characteristics of the current region, and the reason why the 6-level geohash coding is also used is to consider the region with small data amount, for example, on the highway, a 6-level geohash coding region can represent the characteristics of that region, while in the downtown, there may be big roads and small roads inside a 6-level geohash coding region, etc.
In step 403, according to the city and the 7-level geohash code, querying data corresponding to the time and the 7-level coding region from the map data, and entering step 404.
This step may query the data corresponding to the time and level 7 geohash encoding region in the corresponding city partition in the Hive database.
In step 404, according to the interval distribution of the speed in the 7-level geohash coding region, the position of the vehicle speed in the interval distribution is determined, and according to the position, the vehicle speed is determined to be relatively fast, normal or relatively slow.
In this step, approximate three-decimal places of respective velocity distributions may be calculated according to the interval distribution of velocities in the 7-level geohash encoding region. The three quantiles corresponding to the speed per second can be compared to judge whether the speed per second is faster, normal (medium) or slower. Here, the approximate quartile is exemplified, but not limited to, and for example, the approximate quartile may be used, and whether the speed per second is faster or slower may be determined.
The process of determining an estimated speed score and estimating the speed of the vehicle based on the estimated speed score according to the present invention may include:
in step 401, the city is judged according to the longitude and latitude data in the vehicle journey data.
The step can judge the city where the travel latitude and longitude are located, duplicate removal is carried out on the obtained city data, and only the data of the city partitions in Hive are inquired during inquiry.
In step 402, 7-level geohash codes corresponding to each longitude and latitude are determined, and eight 7-level geohash codes adjacent to each 7-level geohash code are determined.
This step may calculate 7-level geohash codes corresponding to each longitude and latitude, and then calculate eight adjacent 7-level geohash codes for each 7-level geohash code. It should be noted that eight 7-level geohash codes are taken as an example and are not limited thereto.
In step 403, according to the city and the 7-level geohash code, the data of the corresponding time and the 7-level geohash code area are queried from the map data, and the process goes to step 405.
This step may query the corresponding city partition in the hive database for data corresponding to the time and level 7 geohash encoding regions.
In step 405, a range set before and after the speed of each point in the vehicle travel data is set as a speed section of the point.
The step can take an interval of 5km/h before and after the speed per second, namely an interval with the length of 10km/h as the interval where the current speed is located, for example, the speed is 18km/h, and the corresponding interval is 13km/h-23 km/h.
Considering that the storage of the speed map data is stored in 10km/h, it is relatively accurate to use 5km/h before and after, for example, 18km/h of the vehicle speed, and to represent it using the interval of 13-23km/h, more accurately than to represent it using the interval of 10-20 km/h.
In step 406, a class 7 geohash encoding region velocity score is determined.
In this step, a velocity score1 of the corresponding 7-level coding region in the interval of the velocity per second is calculated. Suppose that the speed interval of the 7-level coding region is A1-An, and the statistical number in each interval is B1-Bn. If max (B1-Bn) ═ Bi, then:
Score(Aj)＝(Bj/Bi)*100,j＝1,2,3,…,n
where Bi is the maximum value of the statistical number of certain speed points in a certain speed interval, Bj is the statistical number of certain speed points in a certain speed interval, and score (aj) is the speed score in a certain speed interval.
Assuming that the second speed interval falls within two intervals of Ap and Aq, and the lengths within the two intervals are length _ p and length _ q, respectively (length _ p + length _ q is 10), then:
score (second) ((length _ p) ((ap) + length _ q) ((aq))/10)
Since the present invention divides the speed by 10km, length _ p + length _ q is 10. It should be noted that division of 5km, 20km, etc. may also be used, and the smaller the division area, the larger the occupied storage, and the more accurate the data storage.
In step 407, a level 7 neighboring geohash encoding region velocity score is determined.
In this step, similarly to step 405, a velocity score2 in eight adjacent 7-level regions per second of the velocity interval may be calculated (in this case, eight adjacent regions are calculated as one region).
In step 408, a level 6 geohash-coded region velocity score and a level 6 neighboring geohash-coded region velocity score are determined.
In this step, similar to steps 405 and 406, velocity scores score3 and score4 are calculated for each second velocity interval at the corresponding level 6 geohash coded region and for the level 6 geohash coded region where the adjacent level 7 geohash coded region is located.
In step 409, a speed default score is determined.
In this step, a default score per second of speed may be calculated, score 5.
In the present invention, when the relative speed (i.e., the default speed) with respect to all points of all the regions is calculated, it is found through statistics and analysis of data that many points with a speed of less than 1.5km/h occur at which the vehicle is almost stationary. Therefore, in order to make the evaluation of the absolute speed more accurate, the invention selects speed points more than 1.5km/h, rounds the rest points, and then counts the number of each group in groups to obtain an approximate distribution of all point speeds. That is, in order to process the points in the area where there is no data (or the area where the amount of data is small) in the history data, the present invention may replace the feature of the area where the points are located with the distribution feature of all the points. This can be viewed as treating the entire map as a coded region, then calculating the distribution of all points, and calculating a score.
In this step, the speed default score may be calculated as follows:
assuming the speed intervals A1-An, the statistical number in each interval is B1-Bn. Assuming max (B1-Bn) ═ Bi, then:
Score(Aj)＝(Bj/Bi)*100,j＝1,2,3,…,n。
where Bi is the maximum value of the statistical number of certain speed points in a certain speed interval, Bj is the statistical number of certain speed points in a certain speed interval, and score (aj) is the speed score in a certain speed interval.
For points with a speed less than 1km/h, the score of the interval may be replaced by a score of the speed interval [1,2], and for points with a speed greater than 130km/h, the score of the interval may be replaced by an average score of the 130-phase 135 segment. Because the speed is a continuous value, the interval calculation is mainly adopted to facilitate storage, and the continuous value cannot be stored when the map area characteristics are stored. Note that, points at a speed of less than 1km/h may be deleted.
In step 410, weights are assigned.
In this step, weights may be given to the respective regions according to the size of the data amount in the respective regions. The weighting order may be as follows:
corresponding to a 7-level region velocity score weight:
p1＝f1(count1)＝1–exp(-count1/40)
corresponding to the velocity score weight of the adjacent region of the 7-level region:
p2＝f2(count2)*(1–p1)＝(1–exp(-count2/80))*(1–p1)
corresponding to the speed score weight of the 6-level region where the 7-level region is located:
p3＝f3(count3)*(1–p1–p2)＝(1–exp(-count3/300))*(1–p1–p2)
corresponding to the grade 6 region score weight of the adjacent region of the grade 7 region:
p4＝f4(count4)*(1–p1–p2–p3)＝(1–exp(-count4/600))*(1–p1–p2–p3)
default speed score weight:
p5＝1–p1–p2–p3–p4
it should be noted that the above selected function is only for example and not limited thereto, and may be determined as needed, and the criterion for selecting the function is: the smaller the area data the more important the score of the small area is, while the score of the point is the more representative of the speed score of the trip.
In step 411, a weighting operation is performed according to the assigned weights, and an evaluation speed score is determined.
Wherein the velocity score per second is:
Score＝p1*score1+p2*score2+p3*score3+p4*score4+p5*score5
meanwhile, the second speed score can be given a weight according to the sizes of p1, p2, p3, p4 and p 5:
p_score＝f5(p1,p2,p3,p4,p5)＝p1+p2+3*p3+5*p4+10*p5
then, the velocity score weight per second is normalized, and the final velocity score can be obtained by performing weighted summation:
score weight for ith second:
final velocity score:
the vehicle speed estimation method of the present invention is described above in detail, and the vehicle speed estimation device and apparatus corresponding to the present invention are described below.
Fig. 5 is a schematic block diagram of a vehicle speed estimation apparatus according to an embodiment of the present invention.
Referring to fig. 5, in a vehicle speed evaluation device 50, there is provided: a journey data acquisition module 51, a map data acquisition module 52 and a comparison and evaluation module 53.
And a trip data acquisition module 51 for acquiring vehicle trip data including a vehicle speed.
And the map data acquisition module 52 is configured to search speed map data corresponding to the vehicle travel track from the generated speed map according to the vehicle travel data.
And the comparison and evaluation module 53 is used for evaluating the vehicle speed according to the comparison result of the vehicle travel data acquired by the travel data acquisition module 51 and the speed map data searched by the map data acquisition module 52. Wherein the vehicle speed may be determined to be relatively fast or relatively slow according to a location at which the vehicle speed is located in a historical speed profile of the speed map data; and/or determining an estimated speed score based on the vehicle speed and a historical speed of the speed map data, and estimating the vehicle speed based on the determined estimated speed score.
It can be found that, according to the technical solution of the embodiment of the present invention, after the vehicle trip data including the vehicle speed is acquired, the speed map data corresponding to the vehicle trip track can be searched from the generated speed map according to the vehicle trip data, and then the vehicle speed can be evaluated according to the comparison result between the acquired vehicle trip data and the searched speed map data. Because the generated speed map contains historical speed data of a plurality of vehicles on the travel track, and the current vehicle speed is compared with historical speeds of other vehicles relatively, compared with the prior art that absolute speed is used for evaluating the vehicle speed, the scheme of the embodiment of the invention can reflect the current vehicle speed more accurately.
Fig. 6 is another schematic block diagram of a vehicle speed estimation apparatus according to an embodiment of the present invention.
Referring to fig. 6, in a vehicle speed evaluation device 60, there is provided: the method comprises the following steps: a journey data acquisition module 51, a map data acquisition module 52, a comparison and evaluation module 53 and a map data generation module 54.
A map data generation module 54, configured to determine 6-level and 7-level geohash codes corresponding to each longitude and latitude data in the vehicle travel data; grouping all data contained in the vehicle travel data according to time, and grouping the vehicle speed according to a set value; counting the speed distribution of the vehicle speed according to the 7-level and 6-level geohash codes respectively; splicing the speed distribution of the 7-level geohash coding region and the speed distribution of the 6-level geohash coding region corresponding to the speed distribution according to 6-level geohash coding and time; accumulating the spliced data and the historical data to obtain updated speed map data; and after the speed map data is subjected to inverse coding processing to obtain city information, the city information is stored again according to the city partitions.
The comparing and evaluating module 53 may further include: a first evaluation submodule 531 and/or a second evaluation submodule 532.
A first evaluation submodule 531 for determining whether the vehicle speed is relatively fast or relatively slow according to a position where the vehicle speed is located in the historical speed distribution of the speed map data.
A second evaluation submodule 532 for determining an evaluated speed score based on the vehicle speed and a historical speed of the speed map data, and evaluating the vehicle speed based on the determined evaluated speed score.
The first evaluation submodule 531 judges the city according to the longitude and latitude data in the vehicle travel data; determining 7-level geohash codes corresponding to the longitudes and latitudes, and determining eight 7-level geohash codes adjacent to each 7-level geohash code; according to the city and the 7-level geohash code, inquiring data of the corresponding time and the 7-level geohash code area from the map data; and determining the position of the vehicle speed in the interval distribution according to the interval distribution of the speed in the 7-level geohash coding region, and determining that the vehicle speed is relatively fast, ordinary or relatively slow according to the position.
The second evaluation submodule 532 judges the city according to the longitude and latitude data in the vehicle travel data; determining 7-level geohash codes corresponding to the longitudes and latitudes, and determining eight 7-level geohash codes adjacent to each 7-level geohash code; according to the city and the 7-level geohash code, inquiring data of the corresponding time and the 7-level geohash code area from the map data; setting a range around the speed of each point in the vehicle travel data as a speed interval of the point; and respectively determining 7-level geohash coding region speed scores, 7-level adjacent geohash coding region speed scores, 6-level adjacent geohash coding region speed scores and default scores, and performing weighting operation according to the assigned weights to determine evaluation speed scores.
Fig. 7 is a schematic block diagram of a vehicle speed estimation apparatus according to an embodiment of the present invention.
Referring to fig. 7, in a vehicle speed evaluation device 70, there is included: a processor 71, a memory 72.
The processor 71 acquires vehicle travel data including a vehicle speed, searches for speed map data corresponding to a vehicle travel trajectory from a generated speed map according to the vehicle travel data, and estimates the vehicle speed according to a result of comparison between the acquired vehicle travel data and the searched speed map data.
The memory 72 stores a speed map.
Embodiments of the present invention also provide a non-transitory machine-readable storage medium having executable code stored thereon, which when executed by a processor of an electronic device, causes the processor to perform the following method:
acquiring vehicle travel data including vehicle speed;
searching speed map data corresponding to the vehicle travel track from the generated speed map according to the vehicle travel data;
and evaluating the vehicle speed according to the comparison result of the acquired vehicle travel data and the searched speed map data.
In summary, according to the technical solution of the embodiment of the present invention, after the vehicle trip data including the vehicle speed is acquired, the speed map data corresponding to the vehicle trip track can be searched from the generated speed map according to the vehicle trip data, and then the vehicle speed can be estimated according to the comparison result between the acquired vehicle trip data and the searched speed map data. Because the generated speed map contains historical speed data of a plurality of vehicles on the travel track, and the current vehicle speed is compared with historical speeds of other vehicles relatively, compared with the prior art that absolute speed is used for evaluating the vehicle speed, the scheme of the embodiment of the invention can reflect the current vehicle speed more accurately.
The technical solution according to the present invention has been described in detail above with reference to the accompanying drawings.
Furthermore, the method according to the invention may also be implemented as a computer program or computer program product comprising computer program code instructions for carrying out the above-mentioned steps defined in the above-mentioned method of the invention.
Alternatively, the invention may also be embodied as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) having stored thereon executable code (or a computer program, or computer instruction code) which, when executed by a processor of an electronic device (or computing device, server, etc.), causes the processor to perform the steps of the above-described method according to the invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both.
Those of ordinary skill in the art will understand that: the invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
Claims (8)
1. A vehicle speed evaluation method characterized by comprising:
acquiring vehicle travel data including vehicle speed;
searching speed map data corresponding to the vehicle travel track from the generated speed map according to the vehicle travel data;
evaluating the vehicle speed according to the comparison result of the acquired vehicle travel data and the searched speed map data;
the speed map is generated in advance, and the generation process of the speed map comprises the following steps: determining 6-level and 7-level geohash codes corresponding to each longitude and latitude data in the vehicle travel data; grouping all data contained in the vehicle travel data according to time, and grouping the vehicle speed according to a set value; counting the speed distribution of the vehicle speed according to the 7-level and 6-level geohash codes respectively; splicing the speed distribution of the 7-level geohash coding region and the speed distribution of the 6-level geohash coding region corresponding to the speed distribution according to 6-level geohash coding and time; accumulating the spliced data and the historical data to obtain updated speed map data; and after the speed map data is subjected to inverse coding processing to obtain city information, the city information is stored again according to the city partitions.
2. The method of claim 1, wherein said evaluating vehicle speed based on a comparison of said acquired vehicle trip data and said looked-up speed map data comprises:
determining whether the vehicle speed is relatively fast or relatively slow according to a position where the vehicle speed is located in a historical speed distribution of the speed map data; and/or the presence of a gas in the gas,
determining an estimated speed score based on the vehicle speed and a historical speed of the speed map data, and estimating the vehicle speed based on the determined estimated speed score.
3. The method of claim 2, wherein determining whether the vehicle speed is relatively fast or relatively slow based on where the vehicle speed is located in a historical speed profile of the speed map data comprises:
judging the city according to the longitude and latitude data in the vehicle travel data;
determining 7-level geohash codes corresponding to the longitudes and latitudes, and determining eight 7-level geohash codes adjacent to each 7-level geohash code;
according to the city and the 7-level geohash code, inquiring data of the corresponding time and the 7-level geohash code area from the map data;
and determining the position of the vehicle speed in the interval distribution according to the interval distribution of the speed in the 7-level geohash coding region, and determining that the vehicle speed is relatively fast, ordinary or relatively slow according to the position.
4. The method of claim 2, wherein determining an estimated speed score based on the vehicle speed and a historical speed of the speed map data comprises:
judging the city according to the longitude and latitude data in the vehicle travel data;
determining 7-level geohash codes corresponding to the longitudes and latitudes, and determining eight 7-level geohash codes adjacent to each 7-level geohash code;
according to the city and the 7-level geohash code, inquiring data of the corresponding time and the 7-level geohash code area from the map data;
setting a range around the speed of each point in the vehicle travel data as a speed interval of the point;
and respectively determining 7-level geohash coding region speed scores, 7-level adjacent geohash coding region speed scores, 6-level adjacent geohash coding region speed scores and default scores, and performing weighting operation according to the assigned weights to determine evaluation speed scores.
5. A vehicle speed evaluation device characterized by comprising:
the system comprises a journey data acquisition module, a data processing module and a data processing module, wherein the journey data acquisition module is used for acquiring vehicle journey data containing vehicle speed;
the map data acquisition module is used for searching speed map data corresponding to the vehicle travel track from the generated speed map according to the vehicle travel data;
the comparison and evaluation module is used for evaluating the vehicle speed according to the comparison result of the vehicle travel data acquired by the travel data acquisition module and the speed map data searched by the map data acquisition module;
the map data generation module is used for determining 6-level and 7-level geohash codes corresponding to each longitude and latitude data in the vehicle travel data; grouping all data contained in the vehicle travel data according to time, and grouping the vehicle speed according to a set value; counting the speed distribution of the vehicle speed according to the 7-level and 6-level geohash codes respectively; splicing the speed distribution of the 7-level geohash coding region and the speed distribution of the 6-level geohash coding region corresponding to the speed distribution according to 6-level geohash coding and time; accumulating the spliced data and the historical data to obtain updated speed map data; and after the speed map data is subjected to inverse coding processing to obtain city information, the city information is stored again according to the city partitions.
6. The apparatus of claim 5, wherein the comparative evaluation module comprises:
a first evaluation sub-module for determining whether the vehicle speed is relatively fast or relatively slow according to a location at which the vehicle speed is located in a historical speed profile of the speed map data; and/or the presence of a gas in the gas,
and the second evaluation sub-module is used for determining an evaluation speed score according to the vehicle speed and the historical speed of the speed map data, and evaluating the vehicle speed according to the determined evaluation speed score.
7. The apparatus of claim 6, wherein:
the first evaluation submodule judges the city according to longitude and latitude data in the vehicle travel data; determining 7-level geohash codes corresponding to the longitudes and latitudes, and determining eight 7-level geohash codes adjacent to each 7-level geohash code; according to the city and the 7-level geohash code, inquiring data of the corresponding time and the 7-level geohash code area from the map data; and determining the position of the vehicle speed in the interval distribution according to the interval distribution of the speed in the 7-level geohash coding region, and determining that the vehicle speed is relatively fast, ordinary or relatively slow according to the position.
8. The apparatus of claim 6, wherein:
the second evaluation submodule judges the city according to longitude and latitude data in the vehicle travel data; determining 7-level geohash codes corresponding to the longitudes and latitudes, and determining eight 7-level geohash codes adjacent to each 7-level geohash code; according to the city and the 7-level geohash code, inquiring data of the corresponding time and the 7-level geohash code area from the map data; setting a range around the speed of each point in the vehicle travel data as a speed interval of the point; and respectively determining 7-level geohash coding region speed scores, 7-level adjacent geohash coding region speed scores, 6-level adjacent geohash coding region speed scores and default scores, and performing weighting operation according to the assigned weights to determine evaluation speed scores.
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