CN114925069B - Big data GPS off-line analysis method - Google Patents
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Abstract
The invention discloses a big data GPS off-line analysis method, which comprises the following steps: s1, comprehensively encoding the region within a certain latitude and longitude range according to a given precision through Geohash, calling GPS position data of each region in the interface analysis encoding, and storing returned GPS position analysis information and Geohash codes of all GPS position points in a standard table; s2, registering the Geohash coding custom function which is the same as that in the S1 in a database; s3, encoding GPS position information in the current vehicle running track through a self-defined function, and then associating with the standard table in S1, so that area information corresponding to GPS position points in the running track is obtained in a large scale.
Description
Technical Field
The invention belongs to the field of geospatial application technology and big data offline computation, and particularly relates to a GPS offline analysis method under the condition of big data.
Background
With the continuous development of internet of vehicles technology, more and more owners purchase intelligent internet-connected vehicles, and meanwhile, more and more user behavior data are generated by the intelligent internet-connected vehicles, wherein the user behavior data comprise track data of the vehicles. The track data records the GPS positioning information of the automobile at different moments, and when the regional correlation statistics is carried out, the physical address of the actual meaning of the GPS positioning information needs to be analyzed according to the GPS coordinates, so that the region (province, city and county) where the GPS point belongs is identified. In the context of big data, it is a necessary requirement to quickly parse out GPS location data.
For the analysis of physical addresses of GPS points, a common technical means is to call an API interface provided by a map service provider. However, if each GPS point calls an API, the call interface has a time delay, so when resolving a large number of GPS points, the response time will be long, and the response time will increase linearly with the increase of the data volume. Obviously, this way of calling interfaces one by one is not suitable for fast parsing of a large number of GPS points.
In order to solve the above-mentioned problems, chinese patent document CN113138985A discloses a method and system for analyzing GPS data, in which the analyzed GPS position data is stored in a database, so that the GPS position data that has been analyzed before each repeated interface analysis is avoided. The method can improve the subsequent analysis speed to a certain extent along with the increase of the analyzed data, but still analyzes the data piece by piece, and the analysis process is complex depending on the calling of the interface, and the analysis speed is limited under the condition of big data.
Therefore, providing a method for quickly analyzing GPS position data, which is based on a database and does not depend on interface calls, becomes a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to solve the technical problems that: how to provide a GPS off-line analysis method which does not need to rely on interface calling and is simple and quick to analyze.
In order to solve the technical problems, the invention adopts the following technical scheme:
The big data GPS off-line analysis method is characterized by comprising the following steps: s1, comprehensively encoding the region within a certain latitude and longitude range according to a given precision through Geohash, calling GPS position data of each region in the interface analysis encoding, and storing returned GPS position analysis information and Geohash codes of all GPS position points in a standard table; s2, registering the Geohash coding custom function which is the same as that in the S1 in a database; s3, encoding GPS position information in the current vehicle running track through a self-defined function, and then associating with the standard table in S1, so that area information corresponding to GPS position points in the running track is obtained in a large scale. In this way, each GPS point in the longitude and latitude range is encoded, the code is stored in the standard table after corresponding to the analysis information of the position point, if the GPS point is used, the running track is not required to be analyzed in batches, and the position information of the encoding point is queried by using the encoding point in the standard table under the off-line condition. The method only needs to input the whole analyzed information in advance, the code is directly corresponding to the analyzed information in the follow-up, the query mode is simple, convenient and quick, the standard table is stable, updating and changing are not needed in a long time, and the analysis cost is low.
Further, the specific steps of encoding by using Geohash are as follows: firstly, respectively converting the longitude and the latitude of a GPS position point to be encoded into binary codes, then combining the generated longitude binary codes and latitude binary codes, and finally, converting the combined binary codes into 10-system vertical codes to generate Base32 codes. The adopted codes can ensure that the position points correspond to the longitudes and the latitudes, ensure that the position points are correct, accurately correspond to the position points after being combined, and ensure that the positions corresponding to the position points are sufficiently accurate.
Further, when the Geohash is used for comprehensively encoding the region in a certain longitude and latitude range according to the required precision, the geographic geometric center close to the longitude and latitude range is selected as a random point to serve as an encoding starting point, and all GPS position points in the longitude and latitude range are continuously encoded on the given precision in a manner of continuously expanding to surrounding points. In this way, the omission of encoding can be avoided in a manner that the center point is extended outwards.
Further, the given precision of the encoding corresponds to an encoding length, which is an integer from 1 to 12. The longer the code, the higher the corresponding accuracy.
Further, after the geometric center point selected randomly is coded by the Geohash, the Geohash code of the center point is put into the analyzed List, and all the Geohash objects except the center point are added into a queue to be analyzed; then, judging whether the analyzed List is empty or not, judging whether the thread of the thread pool is not running or not, and if the analyzed List is empty and the thread is not running, ending the analysis; if not, acquiring a thread from the thread pool, consuming a Geohash object in the queue, and calling an API interface to acquire analysis information of a GPS according to GPS position data of an object center point; judging whether the parsed information is normal, if so, ending the continued execution of the Geohash object, if so, storing the parsed information and Geohash codes into a standard table, simultaneously acquiring a plurality of Geohash objects adjacent to the Geohash object, respectively judging whether the center points of the Geohash objects are within a selected longitude and latitude range, if not, ending the continued execution of the Geohash object, if so, judging whether the Geohash codes are in a parsed List, if so, ending the continued execution of the Geohash object, if not, adding the Geohash object into a queue to be parsed, and waiting for parsing. Therefore, whether codes are omitted or not is ensured by a thread pool mode, one code is ensured to be corresponding to all the position points, and the accuracy of the information can be effectively ensured when the code analysis information is judged by the calling interface. Meanwhile, coding judgment is carried out on a plurality of objects in a longitude and latitude range, so that the accuracy of coding information can be effectively ensured.
Furthermore, the Geohash coding custom function inherits org.apoche.hadoop.hive.ql.exec.UDF class and implements evaluate functions. In this way, the custom function can be directly used in hive to perform Geohash encoding on a given latitude and longitude with a specified accuracy.
Compared with the prior art, the big data GPS off-line analysis method has the following advantages: 1. after setting the standard table, avoiding calling interfaces one by one to analyze GPS points; 2. the method can perform off-line analysis of the regional information of a large number of GPS position points, and greatly improves the analysis efficiency of the GPS points.
Drawings
FIG. 1 is a flow chart of a big data GPS offline parsing method in an embodiment;
FIG. 2 is a flowchart of the method for obtaining the standard table in the embodiment.
Detailed Description
The invention will be further described with reference to the drawings and examples.
Examples:
As shown in fig. 1, the big data GPS offline analysis method provided in this embodiment includes the following steps: s1, comprehensively encoding the region within a certain longitude and latitude range according to a given precision through Geohash, calling GPS position data of each region in interface analysis encoding, and storing returned GPS position analysis information and Geohash codes of all GPS position points in a standard table (specifically, the standard table is in a database and is a hive distributed table); s2, registering the Geohash coding custom function which is the same as that in the S1 in a database; s3, encoding GPS position information in the current vehicle running track through a self-defined function, and then associating with the standard table in S1, so that area information corresponding to GPS position points in the running track is obtained in a large scale.
Specifically, the coding mode adopted in this embodiment is Geohash coding. Geohash is a geocoding method, the basic principle of which is to understand the earth as a two-dimensional plane, recursively decompose the plane into smaller sub-blocks according to longitude and latitude, and each sub-block has the same code within a certain longitude and latitude range. Geohash converts longitude and latitude into a string and in most cases the more string prefixes match the closer distance.
The Geohash algorithm is divided into three steps, and the algorithm is described by taking longitude and latitude (116.389550,39.928167) as an example, and is specifically as follows:
First, longitude and latitude are converted into binary. Dividing the latitude interval [ -90,90] into [ -90,0), [0,90], it can be determined that 39.928167 belongs to the right interval [0,90], labeled 1; then, dividing [0,90] into [0,45 ] and [45,90], and determining that 39.928167 belongs to the left interval [0,45 ], and marking the left interval as 0; recursion of the splitting process, 39.928167 always belongs to a certain split interval, and the interval is gradually approaching 39.928167 along with the increase of splitting times; finally we can get the binary code 101110001100011 generated by latitude 39.928167. Similarly, splitting is performed in the longitude interval [ -180,180] of the earth, and the longitude 116.389550 is subjected to approximation encoding, so that the binary code generated by the longitude 116.389550 can be obtained as 110100101100010. Specifically, the binary code of latitude 39.928167 is shown in table 1:
TABLE 1
And secondly, merging binary codes generated by longitude and latitude. The longitude is placed in even bits and the latitude is placed in odd bits, with special attention to the number of bits starting from 0, 0 being the even bit. Thus, the codes generated in (116.389550,39.928167) are combined to obtain 111001110100100011110000001101.
Finally, the combined binary is converted into 10-ary data, and then Base32 coding is generated. The Base32 code consists of the numbers (0-9) +letters (with a, i, l, o removed). Every 5 binary digits obtain a 10-system number, the value range is 0-31, and the values respectively correspond to 32 characters coded by Base 32. Thus, the 10-ary numbers corresponding to the combined binary codes are 28, 29, 4, 15, 0 and 13, and the corresponding Base32 codes are wx4g0e (116.389550,39.928167).
Specifically, the correspondence between the digits and the Base32 encoded characters is shown in fig. 2.
TABLE 2
The length of the Geohash after being coded is different, and the corresponding precision is also different. The longer the coding length of the same block region is, the higher the accuracy is, but at the same time, the larger the number of codes corresponding to the region is. In practical application, the coding precision and the coding number should be comprehensively considered, and a proper coding length should be selected. For easy understanding, the correspondence between the Geohash code length and the precision given in this embodiment is shown in table 3:
| geohash length | Width of (L) | Height of (1) |
| 1 | 5009.4km | 4992.6km |
| 2 | 1252.3km | 624.1km |
| 3 | 156.5km | 156km |
| 4 | 39.1km | 19.5km |
| 5 | 4.9km | 4.9km |
| 6 | 1.2km | 609.4m |
| 7 | 152.9m | 152.4m |
| 8 | 38.2m | 19m |
| 9 | 4.8m | 4.8m |
| 10 | 1.2m | 59.5cm |
| 11 | 14.9cm | 14.9cm |
| 12 | 3.7cm | 1.9cm |
TABLE 3 Table 3
In Java, the Geohash algorithm is implemented using open-source Geohash packages. Introduction of pore dependence:
then call GeoHash static method implementation of class:
double lat＝39.928167；
double lon＝116.389550；
intprecision＝7；
GeoHash geoHash＝GeoHash.withCharacterPrecision(lat,lon,precision)；
String hashCode＝geoHash.toBase32()；
Where lat represents latitude, lon represents longitude, precision represents geohash coded bits, and precision takes an integer of 1 to 12. In order to comprehensively encode the region in China, obtain the corresponding relation between the Geohash code and province, city, county and the like, comprehensively consider the number of codes and the encoding precision, and in the embodiment, the encoding length is set to be 7.
When a region in a certain longitude and latitude range is comprehensively encoded according to the required precision through Geohash, a geographic geometric center close to the longitude and latitude range is selected as a random point to serve as an encoding starting point, and all GPS position points in the longitude and latitude range are continuously encoded on a given precision in a manner of continuously expanding to surrounding points. In this way, the omission of encoding can be avoided in a manner that the center point is extended outwards.
As shown in fig. 2, the flow of the component standard table in this embodiment is specifically as follows:
Step 1, after a geometric center point selected randomly is coded through Geohash, firstly placing Geohash codes of the center point into a parsed List, and adding the rest Geohash objects into a queue to be parsed;
Step 2, judging whether the analyzed List is empty or not, judging whether the thread of the thread pool is not running or not, and if the analyzed List is empty and the thread is not running, ending the analysis; if not, executing the next step;
step 3, acquiring threads from a thread pool, consuming a Geohash object in a queue, and calling an API interface to acquire analysis information of a GPS according to GPS position data of an object center point;
Step 4, judging whether the analyzed information is normal, if so, ending the continuous execution of the Geohash object, and if so, executing the next step;
Step 5, storing the analysis information and the Geohash codes into a standard table, simultaneously acquiring 8 Geohash objects adjacent to the Geohash objects, respectively judging whether the center points of the 8 Geohash objects are within a selected latitude and longitude range, ending the continuous execution of the Geohash objects if the center points are not within the range, and executing the next step on the Geohash objects if the center points are within the range;
And 6, judging whether the Geohash code is in the parsed List, if so, ending the continuous execution of the Geohash object, and if not, adding the Geohash object into a queue to be parsed, and waiting for parsing.
Through the steps, the region in the Chinese range can be coded on the appointed precision, and each Geohash code and the corresponding physical address information are stored in the standard table. The field names and field types of the standard table are shown in table 4:
| Field identification | Data type | Meaning of |
| geohash | string | Geohash code |
| province_code | string | Province code |
| province_name | string | Province name |
| city_code | string | City code |
| city_name | string | City name |
| district_code | string | County code |
| district_name | string | County name |
| addr | string | Physical address |
Therefore, whether codes are omitted or not is ensured by a thread pool mode, one code is ensured to be corresponding to all the position points, and the accuracy of the information can be effectively ensured when the code analysis information is judged by the calling interface. Meanwhile, coding judgment is carried out on a plurality of objects in a longitude and latitude range, so that the accuracy of coding information can be effectively ensured.
Because a large amount of track data is stored in the Hive distributed table, the same Geohash coding function is not ready in Hive, so after the standard table is obtained, before GPS position data in the track data is actually analyzed, a user-defined function of the Geohash coding needs to be registered. In this embodiment, the custom function needs to inherit the org.apache.hadoop.hive.ql.exec.udf class and implement evaluate functions, which is specifically as follows:
packing the command into jar packets, putting the jar packets under the relevant path of HDFS, such as HDFS:///home/udf/Hive/geohash. Jar, and executing the command at the Hive client:
create function default.geohash as'UDFGeoHash'using jar'hdfs:///home/udf/hive/geohash.jar'；
We can then get the Geohash code with latitude and longitude (lon, lat) and code length of precision in the Hive library by default.
After the standard table and the Geohash custom function are completed, GPS position data in a large quantity of paths is actually analyzed, and the GPS position data is simply encoded through the custom function and then inquired with the standard table join. An example of SQL is given below:
Where od.car_driving is a track table, day= '2021-01-01' represents track data of only the day of 2021, 1, and dim.geohash_standard represents a standard table. The above SQL obtains information of the GPS points and the corresponding resolved positions.
So far, we have completed batch offline interpretation of the GPS location data in the trajectory data.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the technical solution, and although the applicant has described the present invention in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents of the technical solution of the present invention can be made without departing from the spirit and scope of the technical solution, and all such modifications and equivalents are intended to be encompassed in the scope of the claims of the present invention.
Claims (4)
1. The big data GPS off-line analysis method is characterized by comprising the following steps: s1, comprehensively encoding the region within a certain latitude and longitude range according to a given precision through Geohash, calling GPS position data of each region in the interface analysis encoding, and storing returned GPS position analysis information and Geohash codes of all GPS position points in a standard table; when the region in China is fully encoded, the region in China is encoded in the appointed precision, and after each Geohash code and corresponding province, city and county physical address information are obtained, the physical address information and the Geohash code are stored in a standard table; when a region in a certain longitude and latitude range is comprehensively coded according to the required precision through Geohash, firstly selecting a geographic geometric center close to the longitude and latitude range as a random point as a coding starting point, and continuously coding all GPS position points in the longitude and latitude range on a given precision in a manner of continuously expanding to surrounding points; s2, registering a custom function corresponding to the Geohash code in the S1 in a database; the Geohash coding custom function inherits org.apoche.hadoop.hive.ql.exec.UDF class and realizes evaluate functions; s3, encoding GPS position information in the current vehicle running track through a self-defined function, and then carrying out association inquiry with the standard table in S1, so as to obtain the region information corresponding to the GPS position points in the running track in a large scale.
2. The method for offline analysis of big data GPS according to claim 1, wherein the specific steps of encoding using Geohash are as follows: firstly, respectively converting the longitude and the latitude of a GPS position point to be encoded into binary codes, then combining the generated longitude binary codes and latitude binary codes, and finally, converting the combined binary codes into 10-system vertical codes to generate Base32 codes.
3. The big data GPS offline parsing method according to claim 1 or 2, characterized in that the given precision of the encoding corresponds to the encoding length, which is an integer from 1 to 12.
4. The big data GPS offline analysis method according to claim 3, wherein after the geometric center point selected randomly is encoded by Geohash, geohash encoding of the center point is put into an analyzed List, and all Geohash objects except the center point are added into a queue to be analyzed; then, judging whether the analyzed List is empty or not, judging whether the thread of the thread pool is not running or not, and if the analyzed List is empty and the thread is not running, ending the analysis; if not, acquiring a thread from the thread pool, consuming a Geohash object in the queue, and calling an API interface to acquire analysis information of a GPS according to GPS position data of an object center point; judging whether the parsed information is normal, if so, ending the continued execution of the Geohash object, if so, storing the parsed information and Geohash codes into a standard table, simultaneously acquiring a plurality of Geohash objects adjacent to the Geohash object, respectively judging whether the center points of the Geohash objects are within a selected longitude and latitude range, if not, ending the continued execution of the Geohash object, if so, judging whether the Geohash codes are in a parsed List, if so, ending the continued execution of the Geohash object, if not, adding the Geohash object into a queue to be parsed, and waiting for parsing.
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